docs(writeup): add honest random-slope results + scenario notation

Expand Methods to describe the three interaction DF treatments (residual,
Satterthwaite, honest random-slope) and why Satterthwaite is inert on the
random-intercept model. Add a 'Honest inference' subsection: the early-phase
interaction reaches honest significance only in the pre-specified pooled-control
scenarios (naive_a2/naive_boxa/mergeNaive d0_5-d0_13, random-slope p 0.014-0.036)
while confounded/small-n windows collapse (mergeA2_full 0.75; unmerge_d0_5 0.13).
Add a 'Scenario notation' legend defining every grouping, window, model family,
p-value column, and the _f (Forouzan) labels.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

BACKUP.md

@@ -0,0 +1,75 @@
+# Experiments Database — Backup
+
+## Location
+```
+/home/sam/synology/Backups/Experiments-DB-Backup/
+├── daily/          ← up to 5 change-triggered daily backups
+├── weekly/         ← up to 2 unconditional weekly backups
+├── .last_backup_state   ← DB fingerprint from last backup run
+└── backup.log      ← all run output
+```
+
+## Scripts
+```
+scripts/
+├── backup.sh       ← main backup script
+└── setup_cron.sh   ← installs / updates cron entries (idempotent)
+```
+
+## Schedule
+| Cron | Mode | Behaviour |
+|------|------|-----------|
+| `0 2 * * *` (daily 02:00) | `daily` | Compares DB fingerprint; skips if nothing changed |
+| `0 3 * * 0` (Sunday 03:00) | `weekly` | Always dumps; ignores fingerprint |
+
+To re-install cron entries after a path change:
+```bash
+./scripts/setup_cron.sh
+```
+
+## Backup Format
+Files are `pg_dump --format=custom --compress=9` (`.pgdump`).
+
+**Restore a backup:**
+```bash
+# Restore into a running container
+docker exec -i experiments_postgres pg_restore \
+  -U expuser -d experiments_db --clean \
+  < /home/sam/synology/Backups/Experiments-DB-Backup/daily/<file>.pgdump
+
+# Or restore to a fresh database
+docker exec -i experiments_postgres createdb -U expuser experiments_db_restore
+docker exec -i experiments_postgres pg_restore \
+  -U expuser -d experiments_db_restore \
+  < /path/to/file.pgdump
+```
+
+## Change Detection
+The daily run fingerprints the database as:
+```
+<experiments count>,<animals count>,<daily_statuses count>,
+<daily_analyses count>,<session_files count>,<latest audit_log timestamp>
+```
+Any insert, update, or delete on any table shifts this string and triggers a backup.
+
+## Retention Policy
+| Type | Keep |
+|------|------|
+| Daily | 5 most recent |
+| Weekly | 2 most recent |
+
+Older files are automatically deleted after each successful dump.
+
+## Manual Run
+```bash
+# Force a backup right now (ignores change detection)
+./scripts/backup.sh weekly
+
+# Run change-detect check manually
+./scripts/backup.sh daily
+```
+
+## Verify Cron Is Installed
+```bash
+crontab -l | grep ExpDB
+```

N8N_WORKFLOWS.md

@@ -0,0 +1,293 @@
+# Experiments Database — n8n Workflows
+
+n8n is running at **http://localhost:5678**
+
+The Postgres credential **"Experiments DB (PostgreSQL)"** (id: `9iJuyA9iR5KUzmj5`) connects to `experiments_postgres:5432 / experiments_db` via the shared `llm-net` Docker network.
+
+---
+
+## Workflow Index
+
+| ID | Workflow | Entity |
+|----|----------|--------|
+| `jjmtecjuHJPKZ4T7` | ExpDB · Experiments | `experiments` table |
+| `HYnlIscuKwMW7blG` | ExpDB · Animals | `animals` table |
+| `9emayzMpnhIZQxqd` | ExpDB · Daily Statuses | `daily_statuses` table |
+| `hso5z2W1uO44bTP6` | ExpDB · Daily Analyses | `daily_analyses` table |
+| `2s02OX1yibfqp6jK` | ExpDB · Session Files | `session_files` table |
+| `T76NAYiFcCxHDKQB` | ExpDB · Audit Logs | `audit_logs` table |
+| `lq3R3YqeHgxixrbZ` | ExpDB · Reports | cross-table queries |
+
+---
+
+## How to Call a Workflow
+
+All workflows use an **Execute Workflow Trigger** — call them with an **Execute Workflow** node from any parent workflow. Pass a JSON object with `action` + the required fields.
+
+```
+Execute Workflow node
+  → Workflow: <select workflow by name>
+  → Input: { "action": "...", ...fields }
+```
+
+---
+
+## ExpDB · Experiments
+
+### `list`
+Returns all experiments ordered by creation date.
+```json
+{ "action": "list" }
+```
+
+### `get`
+```json
+{ "action": "get", "id": "<experiment_uuid>" }
+```
+
+### `create`
+```json
+{ "action": "create", "title": "My Experiment" }
+```
+Creates with empty `template` and `subject_info_template` (`[]`). UUID auto-generated.
+
+### `update`
+```json
+{ "action": "update", "id": "<experiment_uuid>", "title": "New Title" }
+```
+
+### `delete`
+```json
+{ "action": "delete", "id": "<experiment_uuid>" }
+```
+Returns `{ id }` of deleted row.
+
+---
+
+## ExpDB · Animals
+
+### `list`
+Returns all animals in an experiment, with `daily_status_count`.
+```json
+{ "action": "list", "experiment_id": "<experiment_uuid>" }
+```
+
+### `get`
+Returns animal + `experiment_title`.
+```json
+{ "action": "get", "id": "<animal_uuid>" }
+```
+
+### `create`
+```json
+{
+  "action": "create",
+  "experiment_id": "<experiment_uuid>",
+  "animal_id_string": "M001",
+  "animal_name": "Mouse 1",
+  "subject_info": { "<fieldId>": "value" }
+}
+```
+
+### `update`
+```json
+{
+  "action": "update",
+  "id": "<animal_uuid>",
+  "animal_id_string": "M001",
+  "animal_name": "Mouse 1",
+  "subject_info": { "<fieldId>": "value" }
+}
+```
+
+### `delete`
+```json
+{ "action": "delete", "id": "<animal_uuid>" }
+```
+Cascades to `daily_statuses`.
+
+---
+
+## ExpDB · Daily Statuses
+
+### `list`
+Returns statuses for an animal ordered by date descending.
+```json
+{ "action": "list", "animal_id": "<animal_uuid>" }
+```
+
+### `get`
+Returns status + animal + experiment context.
+```json
+{ "action": "get", "id": "<status_uuid>" }
+```
+
+### `create`
+```json
+{
+  "action": "create",
+  "animal_id": "<animal_uuid>",
+  "date": "2025-04-25",
+  "experiment_description": "...",
+  "vitals": "...",
+  "treatment": "...",
+  "notes": "...",
+  "custom_fields": { "<fieldId>": "value" }
+}
+```
+All fields except `animal_id` and `date` are optional (default `null`).
+
+### `update`
+```json
+{
+  "action": "update",
+  "id": "<status_uuid>",
+  "experiment_description": "...",
+  "vitals": "...",
+  "treatment": "...",
+  "notes": "...",
+  "custom_fields": { "<fieldId>": "value" }
+}
+```
+
+### `delete`
+```json
+{ "action": "delete", "id": "<status_uuid>" }
+```
+Cascades to `daily_analyses` and `session_files`.
+
+---
+
+## ExpDB · Daily Analyses
+
+### `list`
+Returns analysis headers (no heavy JSON blobs) for a daily status.
+```json
+{ "action": "list", "daily_status_id": "<status_uuid>" }
+```
+
+### `get`
+Returns full analysis including `sequence`, `category_counts`, `consecutive_stats`, `runs`.
+```json
+{ "action": "get", "id": "<analysis_uuid>" }
+```
+
+### `push_summary`
+Writes an `analysis_summary` blob back to a daily status (same as the "Save metrics" button in the UI).
+```json
+{
+  "action": "push_summary",
+  "daily_status_id": "<status_uuid>",
+  "analysis_summary": {
+    "counts": { "Success": 12, "Failure": 3 },
+    "total": 15,
+    "success_rate": 0.8,
+    "computed_at": "2025-04-25T10:00:00.000Z"
+  }
+}
+```
+
+---
+
+## ExpDB · Session Files
+
+### `list`
+```json
+{ "action": "list", "daily_status_id": "<status_uuid>" }
+```
+
+### `get`
+```json
+{ "action": "get", "id": "<file_uuid>" }
+```
+
+---
+
+## ExpDB · Audit Logs
+
+### `list_by_record`
+All changes to a specific record (e.g. one animal, one daily status).
+```json
+{
+  "action": "list_by_record",
+  "table_name": "daily_statuses",
+  "record_id": "<status_uuid>",
+  "limit": 50
+}
+```
+Valid `table_name` values: `experiments`, `animals`, `daily_statuses`.
+
+### `list_by_table`
+Recent changes across an entire table.
+```json
+{
+  "action": "list_by_table",
+  "table_name": "animals",
+  "limit": 100
+}
+```
+
+---
+
+## ExpDB · Reports
+
+### `subjects_summary`
+Per-subject stats for an experiment: total days logged, last entry date, days with saved metrics.
+```json
+{ "action": "subjects_summary", "experiment_id": "<experiment_uuid>" }
+```
+
+### `daily_summary`
+All days × subjects where `analysis_summary` exists — useful for CSV export or charting outside the app.
+```json
+{ "action": "daily_summary", "experiment_id": "<experiment_uuid>" }
+```
+
+### `recent_activity`
+Latest audit log entries across all tables.
+```json
+{ "action": "recent_activity", "limit": 50 }
+```
+
+---
+
+## Database Schema Reference
+
+```
+experiments
+  id (uuid PK)  title  created_at  template (json)  subject_info_template (json)
+
+animals
+  id (uuid PK)  experiment_id (FK→experiments)
+  animal_id_string  animal_name  subject_info (json?)
+
+daily_statuses
+  id (uuid PK)  animal_id (FK→animals)  date
+  experiment_description?  vitals?  treatment?  notes?
+  custom_fields (json?)  analysis_summary (json?)
+
+daily_analyses
+  id (uuid PK)  daily_status_id (FK→daily_statuses)  created_at
+  file_name?  timestamp_col  note_col  total_note_rows  session_end_ts?
+  classifications (json)  sequence (json)  category_counts (json)
+  consecutive_stats (json)  runs (json)
+
+session_files
+  id (uuid PK)  daily_status_id (FK→daily_statuses)
+  filename  file_size  file_type?  last_modified?
+  matched_identifier?  animal_id_string_snap?  animal_name_snap?
+  notes?  created_at
+
+audit_logs
+  id (uuid PK)  table_name  record_id  action  changes (json)  timestamp
+```
+
+`custom_fields` and `subject_info` are keyed by `fieldId` (UUID), matching the `template` / `subject_info_template` arrays on the experiment. Field keys can be renamed without data loss; only the UUID is used for storage.
+
+---
+
+## Infrastructure Notes
+
+- `experiments_postgres` is connected to both `experiments-database_default` and `llm-net` networks, making it reachable from n8n as hostname `experiments_postgres`.
+- The n8n API key `n8n_api_ca07bd874c628ed0f52d9a2e65073960f9f617c5` was created for the account `quocsam93@gmail.com` and is stored in the `user_api_keys` table of the n8n Postgres.
+- Workflows were generated by `n8n_create_workflows.py` in this repo root.

analysis/METHODS.md

@@ -0,0 +1,158 @@
+# tDCS reaching study — GLM methods
+
+This document explains the statistics in `tdcs_glm.py`: the models, the exact
+formulas, how each subject's progress is accounted for, and the caveats.
+
+## The question
+
+Two **known** conditions anchor the performance scale and differ from each other:
+
+- **H2 (anchor check):** `Electrode-Box-B2` performs **better** than `Electrode-Box-A2`.
+
+Two **unknown** conditions are then **classified** against those anchors — for
+each of `Electrode-Box-A` and `Right-Electrode`, is it **A2-like** or **B2-like**?
+We do *not* assume either belongs to B2; each unknown is compared to *both*
+anchors, and the anchor it cannot be distinguished from is its likely class.
+
+There is also a **merged-assumption** mode (`--merge`): assume the two unknowns
+resolve as `Right-Electrode == Box-B2` and `Electrode-Box-A == Box-A2`, fold them
+into the anchors, and re-estimate everything with 4 subjects per group.
+
+## Data and outcome
+
+- **Outcome:** `success` = successful reaches in a session
+  (`analysis_summary.counts.Success`), a non-negative **count**.
+- **Attempts:** `total` = reach attempts in the session (`analysis_summary.total`);
+  used as the denominator for the rate model. `success_rate == success / total`.
+- **Time:** `day` = the "# Days Reach" field (training day, 0..26). Analyses use
+  days ≥ 0 (pre-training negative days and zero-attempt sessions are excluded;
+  zero-attempt sessions are undefined for the rate model).
+- **Groups (subjects):** Naive (4), Box-A2 (3), Box-B2 (3), Box-A (1),
+  Right-Electrode (1). Under `--merge`: Box-A2 (4), Box-B2 (4), Naive (4).
+
+**Provenance:** `tdcs_reach_data.csv` was reconstructed from the experiment
+database and verified cell-by-cell against the exported matrix (71/71
+unambiguous cells on days 0–5 matched exactly).
+
+## The three models
+
+All models use `Electrode-Box-B2` as the **reference** group, so each group term
+is that group's contrast *versus Box-B2*. `day_c` is the centered training day and
+`day_c2 = day_c²` captures the rise-then-plateau of the learning curve.
+
+### (A) Level — count (primary)
+
+Poisson GEE on the success counts, clustered by subject:
+
+```
+success ~ C(group, Treatment('Electrode-Box-B2')) + day_c + day_c2
+family        = Poisson (log link)
+groups        = subject           # repeated-measures cluster
+cov_struct    = Exchangeable      # working within-subject correlation
+SE            = robust (sandwich)
+```
+
+`exp(coef)` for a group term is an **incidence-rate ratio (IRR)**: expected
+successes relative to Box-B2.
+
+### (B) Level — rate
+
+Binomial GLM on successes-out-of-attempts, with cluster-robust SEs by subject:
+
+```
+cbind(success, total - success) ~ C(group, Treatment('Electrode-Box-B2')) + day_c + day_c2
+family   = Binomial (logit link)
+cov_type = cluster (groups = subject)
+```
+
+`exp(coef)` is an **odds ratio** for a successful reach relative to Box-B2. This
+controls for differing numbers of attempts, so it answers "who is more accurate
+per attempt?" rather than "who attempts more?"
+
+### (C) Learning rate
+
+Poisson GEE with a **group × day** interaction, to ask whether groups improve at
+different *rates* (not just different levels):
+
+```
+success ~ C(group, Treatment('Electrode-Box-B2')) * day_c + day_c2
+```
+
+Each `group[T.X]:day_c` term is the difference in log-slope versus Box-B2; a joint
+Wald test asks whether *any* group's slope differs. Large p ⇒ parallel learning.
+
+### Anchors-only model
+
+The A2-vs-B2 comparison, refit on **just the two anchor groups** so nothing else
+influences the shared day terms or the dispersion/correlation nuisance:
+
+```
+# subset to {Box-A2, Box-B2}
+success ~ C(group, Treatment('Electrode-Box-B2')) + day_c + day_c2     # count
+cbind(success, total-success) ~ C(group, ...) + day_c + day_c2         # rate
+```
+
+With B2 as reference the single group term is the A2-vs-B2 effect; H2 is a
+one-sided test that this coefficient is below zero.
+
+## How each subject's progress is accounted for
+
+Two distinct pieces:
+
+1. **Progress over training** — the `day_c + day_c2` fixed terms model the average
+   learning curve, so groups are compared at comparable points in training rather
+   than being confounded by *when* each was measured.
+
+2. **Repeated measures / individual baselines** — each subject contributes many
+   correlated sessions and has its own baseline. Handled two ways:
+
+   - **Primary (GEE):** subject is the cluster; an exchangeable working
+     correlation plus robust (sandwich) SEs give *population-average* group
+     effects whose inference is valid under within-subject correlation and
+     Poisson overdispersion.
+
+   - **Sensitivity (mixed model):** a Poisson model with a **per-subject random
+     intercept** (`(1 | subject)`) so each animal gets its own baseline level;
+     the group effects are estimated after allowing for that individual variation.
+     statsmodels has no frequentist Poisson GLMM, so this is the **MAP/Laplace**
+     fit (`fit_vb` diverges on these large counts). Its p-values are approximate —
+     read it as a direction/magnitude check that should agree with GEE.
+
+## Classification logic (unknowns)
+
+For each unknown group, `diff_contrast` builds a linear contrast of that group
+against **each anchor** (both coded vs the B2 reference) and tests it:
+
+- indistinguishable from B2 **and** different from A2 → **B2-like**
+- indistinguishable from A2 **and** different from B2 → **A2-like**
+- indistinguishable from both → **ambiguous** (report the numerically nearer one)
+- different from both → **unlike both**
+
+## Caveats
+
+- **Tiny groups.** Box-A2/B2 have 3 subjects, Naive 4, and each unknown has
+  **n = 1 subject**. Classifying a one-subject condition is weak: "matches
+  anchor X" means "not statistically distinguishable from X," **not** proof of
+  equivalence.
+- **Single-cluster fragility.** Cluster-robust/GEE inference with one cluster in a
+  group can produce artificially small SEs — most visibly the learning-rate
+  interaction for the n=1 groups; do not read those p-values literally.
+- **Mixed-model confounding.** With a per-subject random intercept, a group made
+  of one subject is partly confounded with that subject's random intercept, so its
+  fixed effect is shrunk.
+- **Rate vs count.** "Success" alone is a count; the rate model (success/attempts)
+  is the fairer accuracy comparison when attempt counts differ.
+
+## Files and usage
+
+- `tdcs_glm.py` — the analysis (run it directly).
+- `tdcs_reach_data.csv` — the verified long-format data (`subject, group, day, success, total`).
+- `tdcs_learning_curves*.png` — per-group learning curves (count and rate panels).
+
+```
+python3 analysis/tdcs_glm.py               # all training days, 5 groups
+python3 analysis/tdcs_glm.py --max-day 10  # restrict to days 0–10
+python3 analysis/tdcs_glm.py --merge       # assume Right==B2 and Box-A==A2 (3 groups)
+```
+
+Requires: pandas, numpy, scipy, statsmodels, matplotlib.

analysis/forouzan_reach_data.csv

@@ -0,0 +1,232 @@
+subject,group,day,success,total
+Afrasyab,a2_f,0,1,13
+Afrasyab,a2_f,1,8,24
+Afrasyab,a2_f,2,13,35
+Afrasyab,a2_f,3,8,29
+Afrasyab,a2_f,4,21,43
+Afrasyab,a2_f,5,14,42
+Afrasyab,a2_f,6,18,46
+Afrasyab,a2_f,7,21,52
+Afrasyab,a2_f,8,21,41
+Afrasyab,a2_f,9,21,55
+Arash,b2_f,0,9,34
+Arash,b2_f,1,12,41
+Arash,b2_f,2,15,43
+Arash,b2_f,3,18,44
+Arash,b2_f,4,16,33
+Arash,b2_f,5,18,47
+Arash,b2_f,6,13,48
+Arash,b2_f,7,9,38
+Arash,b2_f,8,18,43
+Arash,b2_f,9,15,47
+Ashkas,a2_f,0,2,30
+Ashkas,a2_f,1,11,35
+Ashkas,a2_f,2,10,30
+Ashkas,a2_f,3,9,33
+Ashkas,a2_f,4,9,41
+Ashkas,a2_f,5,10,36
+Ashkas,a2_f,6,13,46
+Ashkas,a2_f,7,9,42
+Ashkas,a2_f,8,6,36
+Ashkas,a2_f,9,15,48
+Fariborz,a2_f,0,8,32
+Fariborz,a2_f,1,10,32
+Fariborz,a2_f,2,9,34
+Fariborz,a2_f,3,11,35
+Fariborz,a2_f,4,13,35
+Fariborz,a2_f,5,5,38
+Fariborz,a2_f,6,12,44
+Garsivaz,b2_f,0,10,34
+Garsivaz,b2_f,1,12,30
+Garsivaz,b2_f,2,14,35
+Garsivaz,b2_f,3,27,56
+Garsivaz,b2_f,4,22,41
+Garsivaz,b2_f,5,30,57
+Garsivaz,b2_f,6,34,52
+Garsivaz,b2_f,7,25,49
+Garsivaz,b2_f,8,29,46
+Garsivaz,b2_f,9,25,44
+Iraj,b2_f,0,6,29
+Iraj,b2_f,1,13,37
+Iraj,b2_f,2,15,32
+Iraj,b2_f,3,20,49
+Iraj,b2_f,4,17,47
+Iraj,b2_f,5,21,47
+Iraj,b2_f,6,24,49
+Khosrow,b2_f,0,6,23
+Khosrow,b2_f,1,7,27
+Khosrow,b2_f,2,10,27
+Khosrow,b2_f,3,10,26
+Khosrow,b2_f,4,14,35
+Khosrow,b2_f,5,20,41
+Khosrow,b2_f,6,11,37
+Khosrow,b2_f,7,13,34
+Khosrow,b2_f,8,13,30
+Khosrow,b2_f,9,15,40
+Kiyanoush,b2_f,0,11,19
+Kiyanoush,b2_f,1,22,52
+Kiyanoush,b2_f,2,34,58
+Kiyanoush,b2_f,3,30,52
+Kiyanoush,b2_f,4,27,56
+Kiyanoush,b2_f,5,46,65
+Kiyanoush,b2_f,6,32,58
+Kiyanoush,b2_f,7,47,61
+Kiyanoush,b2_f,8,49,68
+Kiyanoush,b2_f,9,41,49
+Manuchehr,b2_f,0,1,22
+Manuchehr,b2_f,1,8,18
+Manuchehr,b2_f,2,9,41
+Manuchehr,b2_f,3,14,47
+Manuchehr,b2_f,4,25,60
+Manuchehr,b2_f,5,22,70
+Manuchehr,b2_f,6,37,74
+Manuchehr,b2_f,7,40,75
+Manuchehr,b2_f,8,32,55
+Manuchehr,b2_f,9,29,69
+Mehrab,a2_f,0,8,28
+Mehrab,a2_f,1,13,26
+Mehrab,a2_f,2,16,45
+Mehrab,a2_f,3,17,34
+Mehrab,a2_f,4,21,45
+Mehrab,a2_f,5,21,55
+Mehrab,a2_f,6,22,51
+Mehrab,a2_f,7,21,48
+Mehrab,a2_f,8,21,48
+Mehrab,a2_f,9,23,45
+Merdas,a2_f,0,7,30
+Merdas,a2_f,1,9,36
+Merdas,a2_f,2,8,33
+Merdas,a2_f,3,17,48
+Merdas,a2_f,4,21,45
+Merdas,a2_f,5,13,39
+Merdas,a2_f,6,19,43
+Merdas,a2_f,7,10,48
+Merdas,a2_f,8,19,52
+Merdas,a2_f,9,20,47
+Nozar,a2_f,0,4,20
+Nozar,a2_f,1,5,23
+Nozar,a2_f,2,8,43
+Nozar,a2_f,3,8,48
+Nozar,a2_f,4,11,40
+Nozar,a2_f,5,13,53
+Nozar,a2_f,6,15,50
+Nozar,a2_f,7,19,46
+Nozar,a2_f,8,18,56
+Nozar,a2_f,9,20,47
+Pashang,b2_f,0,5,32
+Pashang,b2_f,1,7,51
+Pashang,b2_f,2,18,49
+Pashang,b2_f,3,16,43
+Pashang,b2_f,4,21,36
+Pashang,b2_f,5,16,53
+Pashang,b2_f,6,18,42
+Pashang,b2_f,7,23,44
+Pashang,b2_f,8,20,43
+Pashang,b2_f,9,34,57
+Pashin,a2_f,0,7,27
+Pashin,a2_f,1,9,22
+Pashin,a2_f,2,11,41
+Pashin,a2_f,3,14,40
+Pashin,a2_f,4,12,36
+Pashin,a2_f,5,14,43
+Pashin,a2_f,6,11,42
+Pashin,a2_f,7,14,47
+Pashin,a2_f,8,13,50
+Pashin,a2_f,9,18,45
+Rostam,b2_f,0,9,29
+Rostam,b2_f,1,12,36
+Rostam,b2_f,2,14,42
+Rostam,b2_f,3,18,41
+Rostam,b2_f,4,15,45
+Rostam,b2_f,5,20,49
+Rostam,b2_f,6,21,51
+Rostam,b2_f,7,24,48
+Rostam,b2_f,8,19,46
+Rostam,b2_f,9,23,48
+Salm,a2_f,0,7,28
+Salm,a2_f,1,10,36
+Salm,a2_f,2,16,50
+Salm,a2_f,3,18,49
+Salm,a2_f,4,21,62
+Salm,a2_f,5,26,60
+Salm,a2_f,6,27,53
+Salm,a2_f,7,32,62
+Salm,a2_f,8,16,40
+Salm,a2_f,9,16,43
+Sam,b2_f,0,8,28
+Sam,b2_f,1,11,32
+Sam,b2_f,2,10,26
+Sam,b2_f,3,11,39
+Sam,b2_f,4,12,36
+Sam,b2_f,5,14,34
+Sam,b2_f,6,15,43
+Sam,b2_f,7,9,31
+Sam,b2_f,8,23,51
+Sam,b2_f,9,19,45
+Siavash,a2_f,0,10,21
+Siavash,a2_f,1,3,25
+Siavash,a2_f,2,7,39
+Siavash,a2_f,3,13,36
+Siavash,a2_f,4,12,35
+Siavash,a2_f,5,18,32
+Siavash,a2_f,6,18,45
+Siavash,a2_f,7,22,37
+Siavash,a2_f,8,18,36
+Siavash,a2_f,9,17,34
+Sohrab,b2_f,0,8,23
+Sohrab,b2_f,1,11,34
+Sohrab,b2_f,2,15,45
+Sohrab,b2_f,3,19,47
+Sohrab,b2_f,4,18,50
+Sohrab,b2_f,5,18,45
+Sohrab,b2_f,6,16,45
+Sohrab,b2_f,7,20,49
+Sohrab,b2_f,8,14,32
+Sohrab,b2_f,9,23,43
+Tahmasb,b2_f,0,7,32
+Tahmasb,b2_f,1,11,38
+Tahmasb,b2_f,2,8,37
+Tahmasb,b2_f,3,7,26
+Tahmasb,b2_f,4,12,35
+Tahmasb,b2_f,5,13,39
+Tahmasb,b2_f,6,11,41
+Tahmasb,b2_f,7,15,52
+Tahmasb,b2_f,8,23,61
+Tahmasb,b2_f,9,22,61
+Tur,a2_f,0,6,39
+Tur,a2_f,1,11,36
+Tur,a2_f,2,9,35
+Tur,a2_f,3,16,45
+Tur,a2_f,4,19,52
+Tur,a2_f,5,14,47
+Tur,a2_f,6,21,46
+Tus,b2_f,0,4,32
+Tus,b2_f,1,9,39
+Tus,b2_f,2,5,40
+Tus,b2_f,3,15,42
+Tus,b2_f,4,21,50
+Tus,b2_f,5,16,52
+Tus,b2_f,6,19,59
+Tus,b2_f,7,28,57
+Tus,b2_f,8,27,62
+Tus,b2_f,9,27,60
+Zal,a2_f,0,5,34
+Zal,a2_f,1,12,36
+Zal,a2_f,2,16,37
+Zal,a2_f,3,18,40
+Zal,a2_f,4,14,34
+Zal,a2_f,5,15,41
+Zal,a2_f,6,16,46
+Zal,a2_f,7,15,44
+Zal,a2_f,8,20,46
+Zal,a2_f,9,18,50
+Zav,a2_f,0,4,28
+Zav,a2_f,1,7,37
+Zav,a2_f,2,7,41
+Zav,a2_f,3,4,32
+Zav,a2_f,4,8,37
+Zav,a2_f,5,4,37
+Zav,a2_f,6,7,36
+Zav,a2_f,7,15,56
+Zav,a2_f,8,19,54
+Zav,a2_f,9,21,49

analysis/matlab/README.md

@@ -0,0 +1,173 @@
+# tDCS reaching study — MATLAB GLM port
+
+This directory is a MATLAB port of the Python tDCS GLM analysis
+(`analysis/tdcs_glm.py`, documented in `analysis/METHODS.md`), driven by raw
+app-export matrices rather than the pre-joined `tdcs_reach_data.csv`. It
+reproduces the same three models (count level, rate level, learning rate) and
+the same H2 anchor check / unknown-condition classification, but replaces the
+Python's population-average GEE with a `fitglme` GLMM (Poisson/Binomial,
+subject random intercept) — see "GLMM vs GEE" below.
+
+## Requirements
+
+- MATLAB R2025b + Statistics and Machine Learning Toolbox.
+- A license activated at `~/matlab/licenses/license.dat` (already in place in
+  this environment). If MATLAB ever reports it is unlicensed, activate with:
+
+  ```
+  ~/matlab/bin/activate_matlab.sh
+  ```
+
+## Layout
+
+- `tdcs_config.m` — curated 12-animal group map, reference group, merge maps.
+- `tdcs_load_data.m` — parses `raw/raw_success.csv` and `raw/raw_total.csv`
+  (app-export matrix format), joins them, and applies the curated group map.
+- `tdcs_scenario_data.m` — builds one of the 6 scenarios (merge map + day
+  window), and saves the scenario table to `derived/<scenario>.csv`.
+- `tdcs_models.m` — fits the count/rate/learning GLMEs, the per-animal
+  Welch/Mann-Whitney tests, the H2 anchor check, and (unmerged scenarios
+  only) the unknown-condition classification.
+- `tdcs_report.m` — prints descriptives, the raw GLME fixed-effects tables,
+  and an interpretation section (H2 verdict, per-animal p-values,
+  classification, caveats); also writes `results/<scenario>.txt`.
+- `tdcs_glm.m` — the entry point: `tdcs_glm(scenario)` runs
+  `tdcs_scenario_data` → `tdcs_models` → `tdcs_report` for one named
+  scenario, via an explicit `switch/case` over all 6 scenario names.
+- `run_all.m` — runs all 6 scenarios in one script.
+- `run_tests.m` — runs the `matlab.unittest` suite in `tests/` and exits
+  nonzero on any failure.
+- `tests/` — `matlab.unittest` test classes (`tLoadData`, `tScenarioData`,
+  `tModels`) with golden values cross-checked against the Python.
+- `raw/` — pre-generated app-export CSVs (input; not modified by this code).
+- `derived/` — scenario tables written by `tdcs_scenario_data` (generated).
+- `results/` — report text files written by `tdcs_report`/`tdcs_glm`
+  (generated).
+
+## Running
+
+Run one scenario (valid names: `unmerged_full`, `unmerged_d0_10`,
+`mergeA2_full`, `mergeA2_d0_10`, `mergeB2_full`, `mergeB2_d0_10`):
+
+```
+cd analysis/matlab
+~/matlab/bin/matlab -batch "addpath(pwd); tdcs_glm('mergeB2_full')"
+```
+
+This prints the full report to the console and writes it to
+`results/mergeB2_full.txt`.
+
+Run all 6 scenarios:
+
+```
+~/matlab/bin/matlab -batch "addpath(pwd); run_all"
+```
+
+Run the test suite:
+
+```
+~/matlab/bin/matlab -batch "addpath(pwd); run_tests"
+```
+
+(`run_tests` calls `error(...)` on any failure, so the `matlab -batch`
+process exits with a nonzero status — suitable for CI.)
+
+## Validation against the Python
+
+Per-animal statistics (per-subject Welch t-test / one-sided Mann-Whitney U,
+Box-B2 vs Box-A2) are pure stats independent of the GLM choice, and were
+matched to the Python's golden values to 3 decimals across all 6 scenarios
+(see `docs/superpowers/plans/2026-07-20-matlab-tdcs-analysis.md`, Task 3).
+
+## GLMM vs GEE (read this before comparing numbers to the Python)
+
+The Python reference uses **GEE** (population-average effects, exchangeable
+working correlation, robust/sandwich SEs) for the count and learning-rate
+models, and a cluster-robust Binomial GLM for the rate model. This MATLAB
+port instead uses **GLMM** (`fitglme`, subject-specific effects via a
+`(1|subject)` random intercept, Laplace/PL approximation) for all three
+models, because MATLAB's Statistics and Machine Learning Toolbox has no GEE
+implementation.
+
+GLMM and GEE estimate related but distinct quantities (subject-specific vs.
+population-average effects), so **exact coefficients, ratios, and p-values
+will differ** between the two implementations — sometimes by a fair amount
+for the small, unbalanced groups in this study. What should agree is the
+**interpretation**: the direction of the H2 anchor effect, which unknown
+condition classifies as A2-like/B2-like/ambiguous, and whether the learning
+rates differ across groups. Treat the MATLAB numbers as a sensitivity check
+on the Python's conclusions, not as a numerically-identical reproduction.
+
+## Scope vs. the Python
+
+The MATLAB port reproduces the Python's three core models (count, rate,
+learning-rate), per-animal tests, unknown-group classification, and the H2
+verdict (read off the main model's A2-vs-B2 term). The Python's two auxiliary
+sensitivity sections — `report_anchor_only` (a two-anchor-only refit) and
+`report_mixed` (a Bayesian MAP random-intercept model) — are intentionally
+**not** ported: the anchor-only refit is closely tracked by the main-model H2
+term, and MATLAB's native `fitglme` already *is* the random-intercept model.
+
+## Phased learning analysis and power
+
+- **Phased days × tDCS LME** — the same `success ~ day*tDCS + (1|subject)` model refit within
+  learning phases (0–5, 6–10, 6–13), Box-B2 vs Box-A2, with per-phase slopes and the interaction
+  95% CI:
+  ```
+  matlab -batch "tdcs_glm('phase_mergeA2')"   # or phase_unmerged / phase_mergeB2
+  ```
+  The Box-B2 faster-acquisition signal sits in the early (0–5) phase; late phases converge. The
+  interaction p/CI use fitlme observation-level DF and are anticonservative at these small n
+  (see power analysis).
+
+- **Monte-Carlo power** — power to detect the early-phase interaction, scored with the
+  cluster-honest per-animal test and the LME test, across subjects-per-group and effect sizes:
+  ```
+  matlab -batch "run_power"          # default: mergeA2, phase 0–5
+  ```
+  Result: at our n (3–5/group) honest power is ~0.3–0.7 even at the observed effect; ~8/group
+  reaches ~80–90% if the effect is as large as observed, ~20/group if it is half that. The LME
+  test is anticonservative only at small n and converges to the honest test by n≈12.
+
+## Figure and data export
+
+- **Figure** — two-panel learning figure (curves + per-phase slopes, Box-B2 vs Box-A2),
+  written to `results/figure_learning.png`:
+  ```
+  matlab -batch "make_figure"
+  ```
+- **Export all data variations to CSV** — organized under `export/`:
+  ```
+  matlab -batch "run_export"
+  ```
+  Produces `curated_all.csv`, `scenarios/<merge>_<window>.csv` (9), `anchors/<merge>_B2vsA2.csv`
+  (3, with the binary tDCS factor), and `phases/<merge>_phase_<lo>-<hi>.csv` (9), plus a MANIFEST.
+
+## Paper replication (verbatim formula)
+
+The paper's model, `fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)')`, is
+replicated verbatim (behavior = successful reaches, stim = tDCS [Box-B2=1 vs Box-A2=0],
+rat = subject) over the full training range:
+```
+matlab -batch "tdcs_glm('paper_mergeA2')"   # or paper_unmerged / paper_mergeB2
+```
+`paper_mergeA2` reproduces the paper's interaction (F(1)=7.09 vs 7.12, p=0.009 vs 0.008); it
+also warns that the full-range interaction is confounded by unequal day coverage (Box-A2 ends
+~day 13) — see the _d0_13 and phased analyses. The Monte-Carlo power analysis (`run_power`,
+`tdcs_power_sim`) uses this same `behavior ~ stim + day + stim:day + (1|rat)` model.
+
+## mergeNaive: pooled control vs tDCS
+
+`mergeNaive` pools Naive into the control side (control = Box-A2 + Naive, n=7) and
+Right-Electrode into the tDCS side (tDCS = Box-B2 + Right-Electrode, n=4); Box-A is left
+out of the anchor contrast. Run the GLMMs, the paper LME, and power:
+```
+matlab -batch "tdcs_glm('mergeNaive_full')"    # GLMM (rate/count) tDCS vs control
+matlab -batch "tdcs_glm('paper_mergeNaive')"   # paper LME: behavior ~ stim+day+stim:day+(1|rat)
+matlab -batch "tdcs_power_sim('mergeNaive',[0 5])"
+```
+With the larger pooled control this is the closest replication of the paper's full pattern:
+a significant days x tDCS interaction (F(1)=7.30, p=0.008 vs paper 7.12/0.008), an equal-on-Day-1
+stim main effect (p=0.21, matching the paper's n.s.), and a strong day effect. The overall tDCS
+advantage is significant even at the cluster-honest per-animal level (rate p=0.003). Caveat:
+pooling Naive (no-stimulation control) with Box-A2 (an electrode condition) is a modeling choice.

analysis/matlab/crossstudy/crossstudy_summary.csv

@@ -0,0 +1,7 @@
+arm,current,mode,window,nCur,rateCur,cumAttCur,prev,ratePrev,cumAttPrev,welchP,mwuP
+Control,A2,full,0-9,3,0.422221769664046,1021.66666666667,a2_f,0.332589396921808,384.166666666667,0.15806572958032,0.101098901098901
+Control,A2,matched,0-3,3,0.299133032915361,361,a2_f,0.332589396921808,384.166666666667,0.480083751892036,0.536263736263736
+Control,A2+Naive,full,0-9,7,0.41833141401484,1011.85714285714,a2_f,0.332589396921808,384.166666666667,0.0135445411684472,0.0283400809716599
+Control,A2+Naive,matched,0-3,7,0.26415993029607,293.714285714286,a2_f,0.332589396921808,384.166666666667,0.0948629941799642,0.119830118282131
+Anodal,B2,full,0-9,3,0.536727902171637,1232.66666666667,b2_f,0.405880120526244,426.583333333333,0.0244732462562875,0.0703296703296703
+Anodal,B2,matched,0-3,3,0.332091503267974,392.333333333333,b2_f,0.405880120526244,426.583333333333,0.0731567689117918,0.101098901098901

analysis/matlab/crossstudy/result.txt

@@ -0,0 +1,29 @@
+============================================================================================
+CROSS-STUDY COMPARISON: current vs previous (Forouzan, _f) -- per-animal success RATE
+============================================================================================
+Metric: per-animal rate = sum(success)/sum(attempts). Rate is used because the
+current protocol runs ~2.7x more attempts/session, so raw counts are not comparable.
+Anodal = Box-B2 (contralateral tDCS); control = Box-A2 (ipsilateral sham) +/- Naive.
+Previous study: b2_f = Anodal, a2_f = Control (validated: b2_f reproduces the paper's
+positive tDCS x day interaction). "matched" = current days 0-3 vs previous full 0-9,
+equalising cumulative attempts (the previous study's whole span ~= current's first 3 days).
+
+arm      current    window    nCur   rate   cumAtt     prev   rate   cumAtt    Welch p    MWU p
+-----------------------------------------------------------------------------------------------
+Control  A2         0-9          3  0.422     1022     a2_f  0.333      384      0.158    0.101
+Control  A2         0-3          3  0.299      361     a2_f  0.333      384      0.480    0.536
+Control  A2+Naive   0-9          7  0.418     1012     a2_f  0.333      384      0.014    0.028
+Control  A2+Naive   0-3          7  0.264      294     a2_f  0.333      384      0.095    0.120
+Anodal   B2         0-9          3  0.537     1233     b2_f  0.406      427      0.024    0.070
+Anodal   B2         0-3          3  0.332      392     b2_f  0.406      427      0.073    0.101
+
+INTERPRETATION
+  - FULL range (0-9 both): current > previous for BOTH arms (control and anodal),
+    e.g. B2 0.54 vs b2_f 0.41 (p=0.024); A2+Naive 0.42 vs a2_f 0.33 (p=0.014).
+  - MATCHED effort (current 0-3 vs previous full): the gap disappears and slightly
+    reverses -- B2 0.33 vs b2_f 0.41 (n.s.); A2 0.30 vs a2_f 0.33 (n.s.).
+  => The current study's apparent superiority is a PRACTICE/ATTEMPTS artifact: its
+     protocol packs ~2.7x more attempts/session, pushing every arm further along the
+     learning curve by a given day. At equal effort the two cohorts perform the same.
+     Note: A2+Naive 0-3 is slightly under-matched on attempts (294 vs ~384); A2-alone
+     0-3 (361 vs 384) is the cleaner apples-to-apples match.

analysis/matlab/derived/mergeA2_d0_10.csv

@@ -0,0 +1,127 @@
+subject,group,day,success,total,day_c,day_c2
+Banh-mi-1,Electrode-Box-B2,0,13,84,-4.92063492063492,24.2126480221718
+Banh-mi-1,Electrode-Box-B2,1,35,86,-3.92063492063492,15.371378180902
+Banh-mi-1,Electrode-Box-B2,2,47,110,-2.92063492063492,8.53010833963215
+Banh-mi-1,Electrode-Box-B2,3,65,140,-1.92063492063492,3.68883849836231
+Banh-mi-1,Electrode-Box-B2,4,70,127,-0.920634920634921,0.847568657092467
+Banh-mi-1,Electrode-Box-B2,5,102,142,0.0793650793650791,0.0062988158226253
+Banh-mi-1,Electrode-Box-B2,6,90,131,1.07936507936508,1.16502897455278
+Banh-mi-1,Electrode-Box-B2,7,109,148,2.07936507936508,4.32375913328294
+Banh-mi-1,Electrode-Box-B2,8,104,137,3.07936507936508,9.4824892920131
+Banh-mi-1,Electrode-Box-B2,9,119,150,4.07936507936508,16.6412194507433
+Banh-mi-1,Electrode-Box-B2,10,121,158,5.07936507936508,25.7999496094734
+Banh-mi-2,Electrode-Box-A2,0,25,97,-4.92063492063492,24.2126480221718
+Banh-mi-2,Electrode-Box-A2,1,22,101,-3.92063492063492,15.371378180902
+Banh-mi-2,Electrode-Box-A2,2,22,119,-2.92063492063492,8.53010833963215
+Banh-mi-2,Electrode-Box-A2,3,29,118,-1.92063492063492,3.68883849836231
+Banh-mi-2,Electrode-Box-A2,4,27,136,-0.920634920634921,0.847568657092467
+Banh-mi-2,Electrode-Box-A2,5,43,146,0.0793650793650791,0.0062988158226253
+Banh-mi-2,Electrode-Box-A2,6,74,146,1.07936507936508,1.16502897455278
+Banh-mi-2,Electrode-Box-A2,7,70,148,2.07936507936508,4.32375913328294
+Banh-mi-2,Electrode-Box-A2,8,65,130,3.07936507936508,9.4824892920131
+Banh-mi-2,Electrode-Box-A2,9,79,151,4.07936507936508,16.6412194507433
+Banh-mi-2,Electrode-Box-A2,10,93,152,5.07936507936508,25.7999496094734
+Egg-tart-1,Electrode-Box-B2,0,7,56,-4.92063492063492,24.2126480221718
+Egg-tart-1,Electrode-Box-B2,1,16,78,-3.92063492063492,15.371378180902
+Egg-tart-1,Electrode-Box-B2,2,23,103,-2.92063492063492,8.53010833963215
+Egg-tart-1,Electrode-Box-B2,3,63,120,-1.92063492063492,3.68883849836231
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+Egg-tart-1,Electrode-Box-B2,5,83,136,0.0793650793650791,0.0062988158226253
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+Egg-tart-1,Electrode-Box-B2,7,79,138,2.07936507936508,4.32375913328294
+Egg-tart-1,Electrode-Box-B2,8,98,142,3.07936507936508,9.4824892920131
+Egg-tart-1,Electrode-Box-B2,9,89,139,4.07936507936508,16.6412194507433
+Egg-tart-1,Electrode-Box-B2,10,96,143,5.07936507936508,25.7999496094734
+Egg-tart-2,Electrode-Box-A2,0,9,32,-4.92063492063492,24.2126480221718
+Egg-tart-2,Electrode-Box-A2,1,2,38,-3.92063492063492,15.371378180902
+Egg-tart-2,Electrode-Box-A2,2,31,93,-2.92063492063492,8.53010833963215
+Egg-tart-2,Electrode-Box-A2,3,44,101,-1.92063492063492,3.68883849836231
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analysis/matlab/derived/mergeA2_d0_13.csv

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analysis/matlab/derived/mergeA2_full.csv

@@ -0,0 +1,186 @@
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analysis/matlab/derived/mergeB2_d0_10.csv

@@ -0,0 +1,127 @@
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analysis/matlab/derived/mergeB2_d0_13.csv

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analysis/matlab/derived/mergeB2_full.csv

@@ -0,0 +1,186 @@
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analysis/matlab/derived/mergeNaive_d0_10.csv

@@ -0,0 +1,127 @@
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analysis/matlab/derived/mergeNaive_d0_13.csv

@@ -0,0 +1,153 @@
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analysis/matlab/derived/mergeNaive_full.csv

@@ -0,0 +1,186 @@
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analysis/matlab/derived/unmerged_d0_10.csv

@@ -0,0 +1,127 @@
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analysis/matlab/derived/unmerged_d0_13.csv

@@ -0,0 +1,153 @@
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analysis/matlab/derived/unmerged_full.csv

@@ -0,0 +1,186 @@
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+Khoai-lang-2,Naive,12,51,129,3.7027027027027,13.7100073046019
+Khoai-lang-2,Naive,13,73,143,4.7027027027027,22.1154127100073
+Khoai-lang-2,Naive,14,82,136,5.7027027027027,32.5208181154127
+Khoai-lang-2,Naive,15,70,145,6.7027027027027,44.9262235208181
+Khoai-lang-2,Naive,16,76,135,7.7027027027027,59.3316289262235
+Khoai-lang-2,Naive,17,76,150,8.7027027027027,75.7370343316289
+Khoai-lang-2,Naive,18,63,122,9.7027027027027,94.1424397370344
+Khoai-lang-2,Naive,19,48,116,10.7027027027027,114.54784514244
+Khoai-lang-2,Naive,20,65,134,11.7027027027027,136.953250547845
+Khoai-lang-2,Naive,21,75,131,12.7027027027027,161.358655953251
+Khoai-lang-2,Naive,22,98,146,13.7027027027027,187.764061358656
+Khoai-lang-2,Naive,23,88,139,14.7027027027027,216.169466764061
+Khoai-lang-2,Naive,24,94,148,15.7027027027027,246.574872169467
+Khoai-lang-2,Naive,25,56,102,16.7027027027027,278.980277574872
+Khoai-lang-2,Naive,26,75,143,17.7027027027027,313.385682980278
+Khoai-tay-1,Electrode-Box-A,0,14,62,-8.2972972972973,68.845142439737
+Khoai-tay-1,Electrode-Box-A,1,22,83,-7.2972972972973,53.2505478451424
+Khoai-tay-1,Electrode-Box-A,2,6,79,-6.2972972972973,39.6559532505478
+Khoai-tay-1,Electrode-Box-A,3,28,97,-5.2972972972973,28.0613586559532
+Khoai-tay-1,Electrode-Box-A,4,60,134,-4.2972972972973,18.4667640613586
+Khoai-tay-1,Electrode-Box-A,5,75,138,-3.2972972972973,10.8721694667641
+Khoai-tay-1,Electrode-Box-A,6,81,137,-2.2972972972973,5.27757487216946
+Khoai-tay-1,Electrode-Box-A,7,78,147,-1.2972972972973,1.68298027757487
+Khoai-tay-1,Electrode-Box-A,8,91,132,-0.297297297297296,0.0883856829802771
+Khoai-tay-1,Electrode-Box-A,9,99,146,0.702702702702704,0.493791088385684
+Khoai-tay-1,Electrode-Box-A,10,94,143,1.7027027027027,2.89919649379109
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+Khoai-tay-1,Electrode-Box-A,12,105,143,3.7027027027027,13.7100073046019
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+Khoai-tay-2,Naive,1,21,68,-7.2972972972973,53.2505478451424
+Khoai-tay-2,Naive,2,6,79,-6.2972972972973,39.6559532505478
+Khoai-tay-2,Naive,3,0,83,-5.2972972972973,28.0613586559532
+Khoai-tay-2,Naive,4,28,87,-4.2972972972973,18.4667640613586
+Khoai-tay-2,Naive,5,31,125,-3.2972972972973,10.8721694667641
+Khoai-tay-2,Naive,6,62,144,-2.2972972972973,5.27757487216946
+Khoai-tay-2,Naive,7,87,141,-1.2972972972973,1.68298027757487
+Khoai-tay-2,Naive,8,105,152,-0.297297297297296,0.0883856829802771
+Khoai-tay-2,Naive,9,75,148,0.702702702702704,0.493791088385684
+Khoai-tay-2,Naive,10,101,149,1.7027027027027,2.89919649379109
+Khoai-tay-2,Naive,11,102,154,2.7027027027027,7.3046018991965
+Khoai-tay-2,Naive,12,95,144,3.7027027027027,13.7100073046019
+Khoai-tay-2,Naive,13,77,149,4.7027027027027,22.1154127100073
+Khoai-tay-2,Naive,14,91,153,5.7027027027027,32.5208181154127
+OM-2,Naive,0,1,7,-8.2972972972973,68.845142439737
+OM-2,Naive,1,11,33,-7.2972972972973,53.2505478451424
+OM-2,Naive,2,19,62,-6.2972972972973,39.6559532505478
+OM-2,Naive,3,29,117,-5.2972972972973,28.0613586559532
+OM-2,Naive,4,73,126,-4.2972972972973,18.4667640613586
+OM-2,Naive,5,53,135,-3.2972972972973,10.8721694667641
+OM-2,Naive,6,73,138,-2.2972972972973,5.27757487216946
+OM-2,Naive,7,80,131,-1.2972972972973,1.68298027757487
+OM-2,Naive,8,91,141,-0.297297297297296,0.0883856829802771
+OM-2,Naive,9,90,135,0.702702702702704,0.493791088385684
+OM-2,Naive,10,95,142,1.7027027027027,2.89919649379109
+OM-2,Naive,11,60,133,2.7027027027027,7.3046018991965
+OM-2,Naive,12,58,142,3.7027027027027,13.7100073046019
+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109
+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737
+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586
+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641
+Vu-vuong,Naive,0,18,61,-8.2972972972973,68.845142439737
+Vu-vuong,Naive,1,35,91,-7.2972972972973,53.2505478451424
+Vu-vuong,Naive,2,61,127,-6.2972972972973,39.6559532505478
+Vu-vuong,Naive,3,34,146,-5.2972972972973,28.0613586559532
+Vu-vuong,Naive,4,61,141,-4.2972972972973,18.4667640613586
+Vu-vuong,Naive,5,37,151,-3.2972972972973,10.8721694667641
+Vu-vuong,Naive,6,49,131,-2.2972972972973,5.27757487216946
+Vu-vuong,Naive,7,65,149,-1.2972972972973,1.68298027757487
+Vu-vuong,Naive,8,67,141,-0.297297297297296,0.0883856829802771
+Vu-vuong,Naive,9,73,140,0.702702702702704,0.493791088385684
+Vu-vuong,Naive,10,72,131,1.7027027027027,2.89919649379109
+Vu-vuong,Naive,11,89,158,2.7027027027027,7.3046018991965
+Vu-vuong,Naive,12,97,154,3.7027027027027,13.7100073046019
+Vu-vuong,Naive,13,93,145,4.7027027027027,22.1154127100073
+Vu-vuong,Naive,14,75,147,5.7027027027027,32.5208181154127
+Vu-vuong,Naive,15,68,142,6.7027027027027,44.9262235208181
+Vu-vuong,Naive,16,96,162,7.7027027027027,59.3316289262235
+Vu-vuong,Naive,17,68,144,8.7027027027027,75.7370343316289
+Vu-vuong,Naive,18,81,134,9.7027027027027,94.1424397370344
+Vu-vuong,Naive,19,66,144,10.7027027027027,114.54784514244
+Vu-vuong,Naive,20,84,127,11.7027027027027,136.953250547845
+Vu-vuong,Naive,21,66,133,12.7027027027027,161.358655953251

analysis/matlab/export/MANIFEST.txt

@@ -0,0 +1,23 @@
+tDCS data export -- 22 files
+  curated_all.csv
+  scenarios/unmerged_full.csv
+  scenarios/unmerged_d0_10.csv
+  scenarios/unmerged_d0_13.csv
+  scenarios/mergeA2_full.csv
+  scenarios/mergeA2_d0_10.csv
+  scenarios/mergeA2_d0_13.csv
+  scenarios/mergeB2_full.csv
+  scenarios/mergeB2_d0_10.csv
+  scenarios/mergeB2_d0_13.csv
+  anchors/unmerged_B2vsA2.csv
+  phases/unmerged_phase_0-5.csv
+  phases/unmerged_phase_6-10.csv
+  phases/unmerged_phase_6-13.csv
+  anchors/mergeA2_B2vsA2.csv
+  phases/mergeA2_phase_0-5.csv
+  phases/mergeA2_phase_6-10.csv
+  phases/mergeA2_phase_6-13.csv
+  anchors/mergeB2_B2vsA2.csv
+  phases/mergeB2_phase_0-5.csv
+  phases/mergeB2_phase_6-10.csv
+  phases/mergeB2_phase_6-13.csv

analysis/matlab/export/anchors/mergeA2_B2vsA2.csv

@@ -0,0 +1,110 @@
+subject,group,day,success,total,day_c,day_c2,tDCS
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737,1
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424,1
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478,1
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532,1
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586,1
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641,1
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946,1
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965,1
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019,1
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073,1
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737,0
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424,0
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478,0
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532,0
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586,0
+Banh-mi-2,Electrode-Box-A2,5,43,146,-3.2972972972973,10.8721694667641,0
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0
+Egg-tart-1,Electrode-Box-B2,0,7,56,-8.2972972972973,68.845142439737,1
+Egg-tart-1,Electrode-Box-B2,1,16,78,-7.2972972972973,53.2505478451424,1
+Egg-tart-1,Electrode-Box-B2,2,23,103,-6.2972972972973,39.6559532505478,1
+Egg-tart-1,Electrode-Box-B2,3,63,120,-5.2972972972973,28.0613586559532,1
+Egg-tart-1,Electrode-Box-B2,4,69,132,-4.2972972972973,18.4667640613586,1
+Egg-tart-1,Electrode-Box-B2,5,83,136,-3.2972972972973,10.8721694667641,1
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109,1
+Egg-tart-1,Electrode-Box-B2,11,96,148,2.7027027027027,7.3046018991965,1
+Egg-tart-1,Electrode-Box-B2,12,101,156,3.7027027027027,13.7100073046019,1
+Egg-tart-1,Electrode-Box-B2,13,103,152,4.7027027027027,22.1154127100073,1
+Egg-tart-1,Electrode-Box-B2,14,97,152,5.7027027027027,32.5208181154127,1
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+Egg-tart-2,Electrode-Box-A2,3,44,101,-5.2972972972973,28.0613586559532,0
+Egg-tart-2,Electrode-Box-A2,4,54,131,-4.2972972972973,18.4667640613586,0
+Egg-tart-2,Electrode-Box-A2,5,84,139,-3.2972972972973,10.8721694667641,0
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487,0
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771,0
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684,0
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109,0
+Egg-tart-2,Electrode-Box-A2,11,78,152,2.7027027027027,7.3046018991965,0
+Egg-tart-2,Electrode-Box-A2,12,96,155,3.7027027027027,13.7100073046019,0
+Egg-tart-2,Electrode-Box-A2,13,84,155,4.7027027027027,22.1154127100073,0
+Khoai-lang-1,Electrode-Box-B2,0,3,69,-8.2972972972973,68.845142439737,1
+Khoai-lang-1,Electrode-Box-B2,1,18,97,-7.2972972972973,53.2505478451424,1
+Khoai-lang-1,Electrode-Box-B2,2,27,84,-6.2972972972973,39.6559532505478,1
+Khoai-lang-1,Electrode-Box-B2,3,46,121,-5.2972972972973,28.0613586559532,1
+Khoai-lang-1,Electrode-Box-B2,4,65,143,-4.2972972972973,18.4667640613586,1
+Khoai-lang-1,Electrode-Box-B2,5,76,141,-3.2972972972973,10.8721694667641,1
+Khoai-lang-1,Electrode-Box-B2,6,79,146,-2.2972972972973,5.27757487216946,1
+Khoai-lang-1,Electrode-Box-B2,7,81,153,-1.2972972972973,1.68298027757487,1
+Khoai-lang-1,Electrode-Box-B2,8,98,144,-0.297297297297296,0.0883856829802771,1
+Khoai-lang-1,Electrode-Box-B2,9,101,149,0.702702702702704,0.493791088385684,1
+Khoai-lang-1,Electrode-Box-B2,10,103,150,1.7027027027027,2.89919649379109,1
+Khoai-lang-1,Electrode-Box-B2,11,113,154,2.7027027027027,7.3046018991965,1
+Khoai-lang-1,Electrode-Box-B2,12,117,148,3.7027027027027,13.7100073046019,1
+Khoai-lang-1,Electrode-Box-B2,13,114,146,4.7027027027027,22.1154127100073,1
+Khoai-lang-1,Electrode-Box-B2,14,119,132,5.7027027027027,32.5208181154127,1
+Khoai-lang-1,Electrode-Box-B2,15,111,156,6.7027027027027,44.9262235208181,1
+Khoai-lang-1,Electrode-Box-B2,16,74,156,7.7027027027027,59.3316289262235,1
+Khoai-lang-1,Electrode-Box-B2,17,109,139,8.7027027027027,75.7370343316289,1
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+Khoai-lang-1,Electrode-Box-B2,21,109,146,12.7027027027027,161.358655953251,1
+Khoai-lang-1,Electrode-Box-B2,22,106,141,13.7027027027027,187.764061358656,1
+Khoai-tay-1,Electrode-Box-A2,0,14,62,-8.2972972972973,68.845142439737,0
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+Khoai-tay-1,Electrode-Box-A2,8,91,132,-0.297297297297296,0.0883856829802771,0
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+Khoai-tay-1,Electrode-Box-A2,10,94,143,1.7027027027027,2.89919649379109,0
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+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737,1
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424,1
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478,1
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532,1
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586,1
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641,1
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684,1
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109,1
+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737,0
+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424,0
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478,0
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532,0
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586,0
+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641,0

analysis/matlab/export/anchors/mergeB2_B2vsA2.csv

@@ -0,0 +1,110 @@
+subject,group,day,success,total,day_c,day_c2,tDCS
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737,1
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424,1
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478,1
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532,1
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586,1
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641,1
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946,1
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965,1
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019,1
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073,1
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737,0
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424,0
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478,0
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532,0
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586,0
+Banh-mi-2,Electrode-Box-A2,5,43,146,-3.2972972972973,10.8721694667641,0
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0
+Egg-tart-1,Electrode-Box-B2,0,7,56,-8.2972972972973,68.845142439737,1
+Egg-tart-1,Electrode-Box-B2,1,16,78,-7.2972972972973,53.2505478451424,1
+Egg-tart-1,Electrode-Box-B2,2,23,103,-6.2972972972973,39.6559532505478,1
+Egg-tart-1,Electrode-Box-B2,3,63,120,-5.2972972972973,28.0613586559532,1
+Egg-tart-1,Electrode-Box-B2,4,69,132,-4.2972972972973,18.4667640613586,1
+Egg-tart-1,Electrode-Box-B2,5,83,136,-3.2972972972973,10.8721694667641,1
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109,1
+Egg-tart-1,Electrode-Box-B2,11,96,148,2.7027027027027,7.3046018991965,1
+Egg-tart-1,Electrode-Box-B2,12,101,156,3.7027027027027,13.7100073046019,1
+Egg-tart-1,Electrode-Box-B2,13,103,152,4.7027027027027,22.1154127100073,1
+Egg-tart-1,Electrode-Box-B2,14,97,152,5.7027027027027,32.5208181154127,1
+Egg-tart-2,Electrode-Box-A2,0,9,32,-8.2972972972973,68.845142439737,0
+Egg-tart-2,Electrode-Box-A2,1,2,38,-7.2972972972973,53.2505478451424,0
+Egg-tart-2,Electrode-Box-A2,2,31,93,-6.2972972972973,39.6559532505478,0
+Egg-tart-2,Electrode-Box-A2,3,44,101,-5.2972972972973,28.0613586559532,0
+Egg-tart-2,Electrode-Box-A2,4,54,131,-4.2972972972973,18.4667640613586,0
+Egg-tart-2,Electrode-Box-A2,5,84,139,-3.2972972972973,10.8721694667641,0
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487,0
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771,0
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684,0
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109,0
+Egg-tart-2,Electrode-Box-A2,11,78,152,2.7027027027027,7.3046018991965,0
+Egg-tart-2,Electrode-Box-A2,12,96,155,3.7027027027027,13.7100073046019,0
+Egg-tart-2,Electrode-Box-A2,13,84,155,4.7027027027027,22.1154127100073,0
+Khoai-lang-1,Electrode-Box-B2,0,3,69,-8.2972972972973,68.845142439737,1
+Khoai-lang-1,Electrode-Box-B2,1,18,97,-7.2972972972973,53.2505478451424,1
+Khoai-lang-1,Electrode-Box-B2,2,27,84,-6.2972972972973,39.6559532505478,1
+Khoai-lang-1,Electrode-Box-B2,3,46,121,-5.2972972972973,28.0613586559532,1
+Khoai-lang-1,Electrode-Box-B2,4,65,143,-4.2972972972973,18.4667640613586,1
+Khoai-lang-1,Electrode-Box-B2,5,76,141,-3.2972972972973,10.8721694667641,1
+Khoai-lang-1,Electrode-Box-B2,6,79,146,-2.2972972972973,5.27757487216946,1
+Khoai-lang-1,Electrode-Box-B2,7,81,153,-1.2972972972973,1.68298027757487,1
+Khoai-lang-1,Electrode-Box-B2,8,98,144,-0.297297297297296,0.0883856829802771,1
+Khoai-lang-1,Electrode-Box-B2,9,101,149,0.702702702702704,0.493791088385684,1
+Khoai-lang-1,Electrode-Box-B2,10,103,150,1.7027027027027,2.89919649379109,1
+Khoai-lang-1,Electrode-Box-B2,11,113,154,2.7027027027027,7.3046018991965,1
+Khoai-lang-1,Electrode-Box-B2,12,117,148,3.7027027027027,13.7100073046019,1
+Khoai-lang-1,Electrode-Box-B2,13,114,146,4.7027027027027,22.1154127100073,1
+Khoai-lang-1,Electrode-Box-B2,14,119,132,5.7027027027027,32.5208181154127,1
+Khoai-lang-1,Electrode-Box-B2,15,111,156,6.7027027027027,44.9262235208181,1
+Khoai-lang-1,Electrode-Box-B2,16,74,156,7.7027027027027,59.3316289262235,1
+Khoai-lang-1,Electrode-Box-B2,17,109,139,8.7027027027027,75.7370343316289,1
+Khoai-lang-1,Electrode-Box-B2,18,88,138,9.7027027027027,94.1424397370344,1
+Khoai-lang-1,Electrode-Box-B2,19,98,140,10.7027027027027,114.54784514244,1
+Khoai-lang-1,Electrode-Box-B2,20,118,153,11.7027027027027,136.953250547845,1
+Khoai-lang-1,Electrode-Box-B2,21,109,146,12.7027027027027,161.358655953251,1
+Khoai-lang-1,Electrode-Box-B2,22,106,141,13.7027027027027,187.764061358656,1
+Khoai-tay-1,Electrode-Box-B2,0,14,62,-8.2972972972973,68.845142439737,1
+Khoai-tay-1,Electrode-Box-B2,1,22,83,-7.2972972972973,53.2505478451424,1
+Khoai-tay-1,Electrode-Box-B2,2,6,79,-6.2972972972973,39.6559532505478,1
+Khoai-tay-1,Electrode-Box-B2,3,28,97,-5.2972972972973,28.0613586559532,1
+Khoai-tay-1,Electrode-Box-B2,4,60,134,-4.2972972972973,18.4667640613586,1
+Khoai-tay-1,Electrode-Box-B2,5,75,138,-3.2972972972973,10.8721694667641,1
+Khoai-tay-1,Electrode-Box-B2,6,81,137,-2.2972972972973,5.27757487216946,1
+Khoai-tay-1,Electrode-Box-B2,7,78,147,-1.2972972972973,1.68298027757487,1
+Khoai-tay-1,Electrode-Box-B2,8,91,132,-0.297297297297296,0.0883856829802771,1
+Khoai-tay-1,Electrode-Box-B2,9,99,146,0.702702702702704,0.493791088385684,1
+Khoai-tay-1,Electrode-Box-B2,10,94,143,1.7027027027027,2.89919649379109,1
+Khoai-tay-1,Electrode-Box-B2,11,110,156,2.7027027027027,7.3046018991965,1
+Khoai-tay-1,Electrode-Box-B2,12,105,143,3.7027027027027,13.7100073046019,1
+Khoai-tay-1,Electrode-Box-B2,13,106,153,4.7027027027027,22.1154127100073,1
+Khoai-tay-1,Electrode-Box-B2,14,91,152,5.7027027027027,32.5208181154127,1
+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737,1
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424,1
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478,1
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532,1
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586,1
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641,1
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684,1
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109,1
+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737,0
+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424,0
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478,0
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532,0
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586,0
+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641,0

analysis/matlab/export/anchors/unmerged_B2vsA2.csv

@@ -0,0 +1,72 @@
+subject,group,day,success,total,day_c,day_c2,tDCS
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737,1
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424,1
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478,1
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532,1
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586,1
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641,1
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946,1
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965,1
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019,1
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073,1
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737,0
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424,0
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478,0
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532,0
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586,0
+Banh-mi-2,Electrode-Box-A2,5,43,146,-3.2972972972973,10.8721694667641,0
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0
+Egg-tart-1,Electrode-Box-B2,0,7,56,-8.2972972972973,68.845142439737,1
+Egg-tart-1,Electrode-Box-B2,1,16,78,-7.2972972972973,53.2505478451424,1
+Egg-tart-1,Electrode-Box-B2,2,23,103,-6.2972972972973,39.6559532505478,1
+Egg-tart-1,Electrode-Box-B2,3,63,120,-5.2972972972973,28.0613586559532,1
+Egg-tart-1,Electrode-Box-B2,4,69,132,-4.2972972972973,18.4667640613586,1
+Egg-tart-1,Electrode-Box-B2,5,83,136,-3.2972972972973,10.8721694667641,1
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109,1
+Egg-tart-1,Electrode-Box-B2,11,96,148,2.7027027027027,7.3046018991965,1
+Egg-tart-1,Electrode-Box-B2,12,101,156,3.7027027027027,13.7100073046019,1
+Egg-tart-1,Electrode-Box-B2,13,103,152,4.7027027027027,22.1154127100073,1
+Egg-tart-1,Electrode-Box-B2,14,97,152,5.7027027027027,32.5208181154127,1
+Egg-tart-2,Electrode-Box-A2,0,9,32,-8.2972972972973,68.845142439737,0
+Egg-tart-2,Electrode-Box-A2,1,2,38,-7.2972972972973,53.2505478451424,0
+Egg-tart-2,Electrode-Box-A2,2,31,93,-6.2972972972973,39.6559532505478,0
+Egg-tart-2,Electrode-Box-A2,3,44,101,-5.2972972972973,28.0613586559532,0
+Egg-tart-2,Electrode-Box-A2,4,54,131,-4.2972972972973,18.4667640613586,0
+Egg-tart-2,Electrode-Box-A2,5,84,139,-3.2972972972973,10.8721694667641,0
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487,0
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771,0
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684,0
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109,0
+Egg-tart-2,Electrode-Box-A2,11,78,152,2.7027027027027,7.3046018991965,0
+Egg-tart-2,Electrode-Box-A2,12,96,155,3.7027027027027,13.7100073046019,0
+Egg-tart-2,Electrode-Box-A2,13,84,155,4.7027027027027,22.1154127100073,0
+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737,1
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424,1
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478,1
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532,1
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586,1
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641,1
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1
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+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737,0
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+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478,0
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532,0
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586,0
+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641,0

analysis/matlab/export/curated_all.csv

@@ -0,0 +1,186 @@
+subject,group,day,success,total
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+Banh-mi-1,Electrode-Box-B2,1,35,86
+Banh-mi-1,Electrode-Box-B2,2,47,110
+Banh-mi-1,Electrode-Box-B2,3,65,140
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+Banh-mi-1,Electrode-Box-B2,9,119,150
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+Root-beer-1,Electrode-Box-B2,0,11,85
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+Vu-vuong,Naive,0,18,61
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analysis/matlab/export/phases/mergeA2_phase_0-5.csv

@@ -0,0 +1,49 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737,1,0
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424,1,1
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478,1,2
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532,1,3
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586,1,4
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641,1,5
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737,0,0
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424,0,1
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478,0,2
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532,0,3
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586,0,4
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+Egg-tart-1,Electrode-Box-B2,0,7,56,-8.2972972972973,68.845142439737,1,0
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+Egg-tart-1,Electrode-Box-B2,5,83,136,-3.2972972972973,10.8721694667641,1,5
+Egg-tart-2,Electrode-Box-A2,0,9,32,-8.2972972972973,68.845142439737,0,0
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+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737,0,0
+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424,0,1
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478,0,2
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+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641,0,5

analysis/matlab/export/phases/mergeA2_phase_6-10.csv

@@ -0,0 +1,36 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
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+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1,2
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1,3
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1,4
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0,1
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0,2
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0,3
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0,4
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1,0
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1,2
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1,3
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+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0,0
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+Khoai-lang-1,Electrode-Box-B2,6,79,146,-2.2972972972973,5.27757487216946,1,0
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+Khoai-tay-1,Electrode-Box-A2,6,81,137,-2.2972972972973,5.27757487216946,0,0
+Khoai-tay-1,Electrode-Box-A2,7,78,147,-1.2972972972973,1.68298027757487,0,1
+Khoai-tay-1,Electrode-Box-A2,8,91,132,-0.297297297297296,0.0883856829802771,0,2
+Khoai-tay-1,Electrode-Box-A2,9,99,146,0.702702702702704,0.493791088385684,0,3
+Khoai-tay-1,Electrode-Box-A2,10,94,143,1.7027027027027,2.89919649379109,0,4
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1,0
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1,2
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684,1,3
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109,1,4

analysis/matlab/export/phases/mergeA2_phase_6-13.csv

@@ -0,0 +1,51 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946,1,0
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1,2
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1,3
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1,4
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965,1,5
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019,1,6
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073,1,7
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0,1
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0,2
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0,3
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0,4
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1,0
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1,2
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1,3
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109,1,4
+Egg-tart-1,Electrode-Box-B2,11,96,148,2.7027027027027,7.3046018991965,1,5
+Egg-tart-1,Electrode-Box-B2,12,101,156,3.7027027027027,13.7100073046019,1,6
+Egg-tart-1,Electrode-Box-B2,13,103,152,4.7027027027027,22.1154127100073,1,7
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0,0
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487,0,1
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771,0,2
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684,0,3
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109,0,4
+Egg-tart-2,Electrode-Box-A2,11,78,152,2.7027027027027,7.3046018991965,0,5
+Egg-tart-2,Electrode-Box-A2,12,96,155,3.7027027027027,13.7100073046019,0,6
+Egg-tart-2,Electrode-Box-A2,13,84,155,4.7027027027027,22.1154127100073,0,7
+Khoai-lang-1,Electrode-Box-B2,6,79,146,-2.2972972972973,5.27757487216946,1,0
+Khoai-lang-1,Electrode-Box-B2,7,81,153,-1.2972972972973,1.68298027757487,1,1
+Khoai-lang-1,Electrode-Box-B2,8,98,144,-0.297297297297296,0.0883856829802771,1,2
+Khoai-lang-1,Electrode-Box-B2,9,101,149,0.702702702702704,0.493791088385684,1,3
+Khoai-lang-1,Electrode-Box-B2,10,103,150,1.7027027027027,2.89919649379109,1,4
+Khoai-lang-1,Electrode-Box-B2,11,113,154,2.7027027027027,7.3046018991965,1,5
+Khoai-lang-1,Electrode-Box-B2,12,117,148,3.7027027027027,13.7100073046019,1,6
+Khoai-lang-1,Electrode-Box-B2,13,114,146,4.7027027027027,22.1154127100073,1,7
+Khoai-tay-1,Electrode-Box-A2,6,81,137,-2.2972972972973,5.27757487216946,0,0
+Khoai-tay-1,Electrode-Box-A2,7,78,147,-1.2972972972973,1.68298027757487,0,1
+Khoai-tay-1,Electrode-Box-A2,8,91,132,-0.297297297297296,0.0883856829802771,0,2
+Khoai-tay-1,Electrode-Box-A2,9,99,146,0.702702702702704,0.493791088385684,0,3
+Khoai-tay-1,Electrode-Box-A2,10,94,143,1.7027027027027,2.89919649379109,0,4
+Khoai-tay-1,Electrode-Box-A2,11,110,156,2.7027027027027,7.3046018991965,0,5
+Khoai-tay-1,Electrode-Box-A2,12,105,143,3.7027027027027,13.7100073046019,0,6
+Khoai-tay-1,Electrode-Box-A2,13,106,153,4.7027027027027,22.1154127100073,0,7
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1,0
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1,2
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684,1,3
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109,1,4

analysis/matlab/export/phases/mergeB2_phase_0-5.csv

@@ -0,0 +1,49 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737,1,0
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424,1,1
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478,1,2
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532,1,3
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586,1,4
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641,1,5
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737,0,0
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424,0,1
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478,0,2
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532,0,3
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586,0,4
+Banh-mi-2,Electrode-Box-A2,5,43,146,-3.2972972972973,10.8721694667641,0,5
+Egg-tart-1,Electrode-Box-B2,0,7,56,-8.2972972972973,68.845142439737,1,0
+Egg-tart-1,Electrode-Box-B2,1,16,78,-7.2972972972973,53.2505478451424,1,1
+Egg-tart-1,Electrode-Box-B2,2,23,103,-6.2972972972973,39.6559532505478,1,2
+Egg-tart-1,Electrode-Box-B2,3,63,120,-5.2972972972973,28.0613586559532,1,3
+Egg-tart-1,Electrode-Box-B2,4,69,132,-4.2972972972973,18.4667640613586,1,4
+Egg-tart-1,Electrode-Box-B2,5,83,136,-3.2972972972973,10.8721694667641,1,5
+Egg-tart-2,Electrode-Box-A2,0,9,32,-8.2972972972973,68.845142439737,0,0
+Egg-tart-2,Electrode-Box-A2,1,2,38,-7.2972972972973,53.2505478451424,0,1
+Egg-tart-2,Electrode-Box-A2,2,31,93,-6.2972972972973,39.6559532505478,0,2
+Egg-tart-2,Electrode-Box-A2,3,44,101,-5.2972972972973,28.0613586559532,0,3
+Egg-tart-2,Electrode-Box-A2,4,54,131,-4.2972972972973,18.4667640613586,0,4
+Egg-tart-2,Electrode-Box-A2,5,84,139,-3.2972972972973,10.8721694667641,0,5
+Khoai-lang-1,Electrode-Box-B2,0,3,69,-8.2972972972973,68.845142439737,1,0
+Khoai-lang-1,Electrode-Box-B2,1,18,97,-7.2972972972973,53.2505478451424,1,1
+Khoai-lang-1,Electrode-Box-B2,2,27,84,-6.2972972972973,39.6559532505478,1,2
+Khoai-lang-1,Electrode-Box-B2,3,46,121,-5.2972972972973,28.0613586559532,1,3
+Khoai-lang-1,Electrode-Box-B2,4,65,143,-4.2972972972973,18.4667640613586,1,4
+Khoai-lang-1,Electrode-Box-B2,5,76,141,-3.2972972972973,10.8721694667641,1,5
+Khoai-tay-1,Electrode-Box-B2,0,14,62,-8.2972972972973,68.845142439737,1,0
+Khoai-tay-1,Electrode-Box-B2,1,22,83,-7.2972972972973,53.2505478451424,1,1
+Khoai-tay-1,Electrode-Box-B2,2,6,79,-6.2972972972973,39.6559532505478,1,2
+Khoai-tay-1,Electrode-Box-B2,3,28,97,-5.2972972972973,28.0613586559532,1,3
+Khoai-tay-1,Electrode-Box-B2,4,60,134,-4.2972972972973,18.4667640613586,1,4
+Khoai-tay-1,Electrode-Box-B2,5,75,138,-3.2972972972973,10.8721694667641,1,5
+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737,1,0
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424,1,1
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478,1,2
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532,1,3
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586,1,4
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641,1,5
+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737,0,0
+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424,0,1
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478,0,2
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532,0,3
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586,0,4
+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641,0,5

analysis/matlab/export/phases/mergeB2_phase_6-10.csv

@@ -0,0 +1,36 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946,1,0
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1,2
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1,3
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1,4
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0,1
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0,2
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0,3
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0,4
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1,0
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1,2
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1,3
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109,1,4
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0,0
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487,0,1
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771,0,2
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684,0,3
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109,0,4
+Khoai-lang-1,Electrode-Box-B2,6,79,146,-2.2972972972973,5.27757487216946,1,0
+Khoai-lang-1,Electrode-Box-B2,7,81,153,-1.2972972972973,1.68298027757487,1,1
+Khoai-lang-1,Electrode-Box-B2,8,98,144,-0.297297297297296,0.0883856829802771,1,2
+Khoai-lang-1,Electrode-Box-B2,9,101,149,0.702702702702704,0.493791088385684,1,3
+Khoai-lang-1,Electrode-Box-B2,10,103,150,1.7027027027027,2.89919649379109,1,4
+Khoai-tay-1,Electrode-Box-B2,6,81,137,-2.2972972972973,5.27757487216946,1,0
+Khoai-tay-1,Electrode-Box-B2,7,78,147,-1.2972972972973,1.68298027757487,1,1
+Khoai-tay-1,Electrode-Box-B2,8,91,132,-0.297297297297296,0.0883856829802771,1,2
+Khoai-tay-1,Electrode-Box-B2,9,99,146,0.702702702702704,0.493791088385684,1,3
+Khoai-tay-1,Electrode-Box-B2,10,94,143,1.7027027027027,2.89919649379109,1,4
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1,0
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1,2
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684,1,3
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109,1,4

analysis/matlab/export/phases/mergeB2_phase_6-13.csv

@@ -0,0 +1,51 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946,1,0
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1,2
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1,3
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1,4
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965,1,5
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019,1,6
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073,1,7
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0,1
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0,2
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0,3
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0,4
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1,0
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1,2
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1,3
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109,1,4
+Egg-tart-1,Electrode-Box-B2,11,96,148,2.7027027027027,7.3046018991965,1,5
+Egg-tart-1,Electrode-Box-B2,12,101,156,3.7027027027027,13.7100073046019,1,6
+Egg-tart-1,Electrode-Box-B2,13,103,152,4.7027027027027,22.1154127100073,1,7
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0,0
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487,0,1
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771,0,2
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684,0,3
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109,0,4
+Egg-tart-2,Electrode-Box-A2,11,78,152,2.7027027027027,7.3046018991965,0,5
+Egg-tart-2,Electrode-Box-A2,12,96,155,3.7027027027027,13.7100073046019,0,6
+Egg-tart-2,Electrode-Box-A2,13,84,155,4.7027027027027,22.1154127100073,0,7
+Khoai-lang-1,Electrode-Box-B2,6,79,146,-2.2972972972973,5.27757487216946,1,0
+Khoai-lang-1,Electrode-Box-B2,7,81,153,-1.2972972972973,1.68298027757487,1,1
+Khoai-lang-1,Electrode-Box-B2,8,98,144,-0.297297297297296,0.0883856829802771,1,2
+Khoai-lang-1,Electrode-Box-B2,9,101,149,0.702702702702704,0.493791088385684,1,3
+Khoai-lang-1,Electrode-Box-B2,10,103,150,1.7027027027027,2.89919649379109,1,4
+Khoai-lang-1,Electrode-Box-B2,11,113,154,2.7027027027027,7.3046018991965,1,5
+Khoai-lang-1,Electrode-Box-B2,12,117,148,3.7027027027027,13.7100073046019,1,6
+Khoai-lang-1,Electrode-Box-B2,13,114,146,4.7027027027027,22.1154127100073,1,7
+Khoai-tay-1,Electrode-Box-B2,6,81,137,-2.2972972972973,5.27757487216946,1,0
+Khoai-tay-1,Electrode-Box-B2,7,78,147,-1.2972972972973,1.68298027757487,1,1
+Khoai-tay-1,Electrode-Box-B2,8,91,132,-0.297297297297296,0.0883856829802771,1,2
+Khoai-tay-1,Electrode-Box-B2,9,99,146,0.702702702702704,0.493791088385684,1,3
+Khoai-tay-1,Electrode-Box-B2,10,94,143,1.7027027027027,2.89919649379109,1,4
+Khoai-tay-1,Electrode-Box-B2,11,110,156,2.7027027027027,7.3046018991965,1,5
+Khoai-tay-1,Electrode-Box-B2,12,105,143,3.7027027027027,13.7100073046019,1,6
+Khoai-tay-1,Electrode-Box-B2,13,106,153,4.7027027027027,22.1154127100073,1,7
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1,0
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1,2
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684,1,3
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109,1,4

analysis/matlab/export/phases/unmerged_phase_0-5.csv

@@ -0,0 +1,37 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737,1,0
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424,1,1
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478,1,2
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532,1,3
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586,1,4
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641,1,5
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737,0,0
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424,0,1
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478,0,2
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532,0,3
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586,0,4
+Banh-mi-2,Electrode-Box-A2,5,43,146,-3.2972972972973,10.8721694667641,0,5
+Egg-tart-1,Electrode-Box-B2,0,7,56,-8.2972972972973,68.845142439737,1,0
+Egg-tart-1,Electrode-Box-B2,1,16,78,-7.2972972972973,53.2505478451424,1,1
+Egg-tart-1,Electrode-Box-B2,2,23,103,-6.2972972972973,39.6559532505478,1,2
+Egg-tart-1,Electrode-Box-B2,3,63,120,-5.2972972972973,28.0613586559532,1,3
+Egg-tart-1,Electrode-Box-B2,4,69,132,-4.2972972972973,18.4667640613586,1,4
+Egg-tart-1,Electrode-Box-B2,5,83,136,-3.2972972972973,10.8721694667641,1,5
+Egg-tart-2,Electrode-Box-A2,0,9,32,-8.2972972972973,68.845142439737,0,0
+Egg-tart-2,Electrode-Box-A2,1,2,38,-7.2972972972973,53.2505478451424,0,1
+Egg-tart-2,Electrode-Box-A2,2,31,93,-6.2972972972973,39.6559532505478,0,2
+Egg-tart-2,Electrode-Box-A2,3,44,101,-5.2972972972973,28.0613586559532,0,3
+Egg-tart-2,Electrode-Box-A2,4,54,131,-4.2972972972973,18.4667640613586,0,4
+Egg-tart-2,Electrode-Box-A2,5,84,139,-3.2972972972973,10.8721694667641,0,5
+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737,1,0
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424,1,1
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478,1,2
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532,1,3
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586,1,4
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641,1,5
+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737,0,0
+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424,0,1
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478,0,2
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532,0,3
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586,0,4
+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641,0,5

analysis/matlab/export/phases/unmerged_phase_6-10.csv

@@ -0,0 +1,26 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946,1,0
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1,2
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1,3
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1,4
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0,1
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0,2
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0,3
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0,4
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1,0
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1,2
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1,3
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109,1,4
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0,0
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487,0,1
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771,0,2
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684,0,3
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109,0,4
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1,0
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1,2
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684,1,3
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109,1,4

analysis/matlab/export/phases/unmerged_phase_6-13.csv

@@ -0,0 +1,35 @@
+subject,group,day,success,total,day_c,day_c2,tDCS,dayp
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946,1,0
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487,1,1
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771,1,2
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684,1,3
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109,1,4
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965,1,5
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019,1,6
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073,1,7
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946,0,0
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487,0,1
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771,0,2
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684,0,3
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109,0,4
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946,1,0
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487,1,1
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771,1,2
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684,1,3
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109,1,4
+Egg-tart-1,Electrode-Box-B2,11,96,148,2.7027027027027,7.3046018991965,1,5
+Egg-tart-1,Electrode-Box-B2,12,101,156,3.7027027027027,13.7100073046019,1,6
+Egg-tart-1,Electrode-Box-B2,13,103,152,4.7027027027027,22.1154127100073,1,7
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946,0,0
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487,0,1
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771,0,2
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684,0,3
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109,0,4
+Egg-tart-2,Electrode-Box-A2,11,78,152,2.7027027027027,7.3046018991965,0,5
+Egg-tart-2,Electrode-Box-A2,12,96,155,3.7027027027027,13.7100073046019,0,6
+Egg-tart-2,Electrode-Box-A2,13,84,155,4.7027027027027,22.1154127100073,0,7
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946,1,0
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487,1,1
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771,1,2
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684,1,3
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109,1,4

analysis/matlab/export/scenarios/mergeA2_d0_10.csv

@@ -0,0 +1,127 @@
+subject,group,day,success,total,day_c,day_c2
+Banh-mi-1,Electrode-Box-B2,0,13,84,-4.92063492063492,24.2126480221718
+Banh-mi-1,Electrode-Box-B2,1,35,86,-3.92063492063492,15.371378180902
+Banh-mi-1,Electrode-Box-B2,2,47,110,-2.92063492063492,8.53010833963215
+Banh-mi-1,Electrode-Box-B2,3,65,140,-1.92063492063492,3.68883849836231
+Banh-mi-1,Electrode-Box-B2,4,70,127,-0.920634920634921,0.847568657092467
+Banh-mi-1,Electrode-Box-B2,5,102,142,0.0793650793650791,0.0062988158226253
+Banh-mi-1,Electrode-Box-B2,6,90,131,1.07936507936508,1.16502897455278
+Banh-mi-1,Electrode-Box-B2,7,109,148,2.07936507936508,4.32375913328294
+Banh-mi-1,Electrode-Box-B2,8,104,137,3.07936507936508,9.4824892920131
+Banh-mi-1,Electrode-Box-B2,9,119,150,4.07936507936508,16.6412194507433
+Banh-mi-1,Electrode-Box-B2,10,121,158,5.07936507936508,25.7999496094734
+Banh-mi-2,Electrode-Box-A2,0,25,97,-4.92063492063492,24.2126480221718
+Banh-mi-2,Electrode-Box-A2,1,22,101,-3.92063492063492,15.371378180902
+Banh-mi-2,Electrode-Box-A2,2,22,119,-2.92063492063492,8.53010833963215
+Banh-mi-2,Electrode-Box-A2,3,29,118,-1.92063492063492,3.68883849836231
+Banh-mi-2,Electrode-Box-A2,4,27,136,-0.920634920634921,0.847568657092467
+Banh-mi-2,Electrode-Box-A2,5,43,146,0.0793650793650791,0.0062988158226253
+Banh-mi-2,Electrode-Box-A2,6,74,146,1.07936507936508,1.16502897455278
+Banh-mi-2,Electrode-Box-A2,7,70,148,2.07936507936508,4.32375913328294
+Banh-mi-2,Electrode-Box-A2,8,65,130,3.07936507936508,9.4824892920131
+Banh-mi-2,Electrode-Box-A2,9,79,151,4.07936507936508,16.6412194507433
+Banh-mi-2,Electrode-Box-A2,10,93,152,5.07936507936508,25.7999496094734
+Egg-tart-1,Electrode-Box-B2,0,7,56,-4.92063492063492,24.2126480221718
+Egg-tart-1,Electrode-Box-B2,1,16,78,-3.92063492063492,15.371378180902
+Egg-tart-1,Electrode-Box-B2,2,23,103,-2.92063492063492,8.53010833963215
+Egg-tart-1,Electrode-Box-B2,3,63,120,-1.92063492063492,3.68883849836231
+Egg-tart-1,Electrode-Box-B2,4,69,132,-0.920634920634921,0.847568657092467
+Egg-tart-1,Electrode-Box-B2,5,83,136,0.0793650793650791,0.0062988158226253
+Egg-tart-1,Electrode-Box-B2,6,71,142,1.07936507936508,1.16502897455278
+Egg-tart-1,Electrode-Box-B2,7,79,138,2.07936507936508,4.32375913328294
+Egg-tart-1,Electrode-Box-B2,8,98,142,3.07936507936508,9.4824892920131
+Egg-tart-1,Electrode-Box-B2,9,89,139,4.07936507936508,16.6412194507433
+Egg-tart-1,Electrode-Box-B2,10,96,143,5.07936507936508,25.7999496094734
+Egg-tart-2,Electrode-Box-A2,0,9,32,-4.92063492063492,24.2126480221718
+Egg-tart-2,Electrode-Box-A2,1,2,38,-3.92063492063492,15.371378180902
+Egg-tart-2,Electrode-Box-A2,2,31,93,-2.92063492063492,8.53010833963215
+Egg-tart-2,Electrode-Box-A2,3,44,101,-1.92063492063492,3.68883849836231
+Egg-tart-2,Electrode-Box-A2,4,54,131,-0.920634920634921,0.847568657092467
+Egg-tart-2,Electrode-Box-A2,5,84,139,0.0793650793650791,0.0062988158226253
+Egg-tart-2,Electrode-Box-A2,6,85,145,1.07936507936508,1.16502897455278
+Egg-tart-2,Electrode-Box-A2,7,79,143,2.07936507936508,4.32375913328294
+Egg-tart-2,Electrode-Box-A2,8,76,131,3.07936507936508,9.4824892920131
+Egg-tart-2,Electrode-Box-A2,9,88,149,4.07936507936508,16.6412194507433
+Egg-tart-2,Electrode-Box-A2,10,81,151,5.07936507936508,25.7999496094734
+Khoai-lang-1,Electrode-Box-B2,0,3,69,-4.92063492063492,24.2126480221718
+Khoai-lang-1,Electrode-Box-B2,1,18,97,-3.92063492063492,15.371378180902
+Khoai-lang-1,Electrode-Box-B2,2,27,84,-2.92063492063492,8.53010833963215
+Khoai-lang-1,Electrode-Box-B2,3,46,121,-1.92063492063492,3.68883849836231
+Khoai-lang-1,Electrode-Box-B2,4,65,143,-0.920634920634921,0.847568657092467
+Khoai-lang-1,Electrode-Box-B2,5,76,141,0.0793650793650791,0.0062988158226253
+Khoai-lang-1,Electrode-Box-B2,6,79,146,1.07936507936508,1.16502897455278
+Khoai-lang-1,Electrode-Box-B2,7,81,153,2.07936507936508,4.32375913328294
+Khoai-lang-1,Electrode-Box-B2,8,98,144,3.07936507936508,9.4824892920131
+Khoai-lang-1,Electrode-Box-B2,9,101,149,4.07936507936508,16.6412194507433
+Khoai-lang-1,Electrode-Box-B2,10,103,150,5.07936507936508,25.7999496094734
+Khoai-lang-2,Naive,0,0,0,-4.92063492063492,24.2126480221718
+Khoai-lang-2,Naive,1,0,0,-3.92063492063492,15.371378180902
+Khoai-lang-2,Naive,2,10,47,-2.92063492063492,8.53010833963215
+Khoai-lang-2,Naive,3,11,52,-1.92063492063492,3.68883849836231
+Khoai-lang-2,Naive,4,9,56,-0.920634920634921,0.847568657092467
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analysis/matlab/export/scenarios/mergeA2_d0_13.csv

@@ -0,0 +1,153 @@
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analysis/matlab/export/scenarios/mergeA2_full.csv

@@ -0,0 +1,186 @@
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analysis/matlab/export/scenarios/mergeB2_d0_10.csv

@@ -0,0 +1,127 @@
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analysis/matlab/export/scenarios/mergeB2_d0_13.csv

@@ -0,0 +1,153 @@
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+Vu-vuong,Naive,13,93,145,6.875,47.265625

analysis/matlab/export/scenarios/mergeB2_full.csv

@@ -0,0 +1,186 @@
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analysis/matlab/export/scenarios/unmerged_d0_10.csv

@@ -0,0 +1,127 @@
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+Khoai-lang-1,Right-Electrode,4,65,143,-0.920634920634921,0.847568657092467
+Khoai-lang-1,Right-Electrode,5,76,141,0.0793650793650791,0.0062988158226253
+Khoai-lang-1,Right-Electrode,6,79,146,1.07936507936508,1.16502897455278
+Khoai-lang-1,Right-Electrode,7,81,153,2.07936507936508,4.32375913328294
+Khoai-lang-1,Right-Electrode,8,98,144,3.07936507936508,9.4824892920131
+Khoai-lang-1,Right-Electrode,9,101,149,4.07936507936508,16.6412194507433
+Khoai-lang-1,Right-Electrode,10,103,150,5.07936507936508,25.7999496094734
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+Khoai-lang-2,Naive,1,0,0,-3.92063492063492,15.371378180902
+Khoai-lang-2,Naive,2,10,47,-2.92063492063492,8.53010833963215
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+Khoai-lang-2,Naive,9,75,131,4.07936507936508,16.6412194507433
+Khoai-lang-2,Naive,10,63,134,5.07936507936508,25.7999496094734
+Khoai-tay-1,Electrode-Box-A,0,14,62,-4.92063492063492,24.2126480221718
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+Khoai-tay-2,Naive,1,21,68,-3.92063492063492,15.371378180902
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+Khoai-tay-2,Naive,9,75,148,4.07936507936508,16.6412194507433
+Khoai-tay-2,Naive,10,101,149,5.07936507936508,25.7999496094734
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+OM-2,Naive,3,29,117,-1.92063492063492,3.68883849836231
+OM-2,Naive,4,73,126,-0.920634920634921,0.847568657092467
+OM-2,Naive,5,53,135,0.0793650793650791,0.0062988158226253
+OM-2,Naive,6,73,138,1.07936507936508,1.16502897455278
+OM-2,Naive,7,80,131,2.07936507936508,4.32375913328294
+OM-2,Naive,8,91,141,3.07936507936508,9.4824892920131
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+OM-2,Naive,10,95,142,5.07936507936508,25.7999496094734
+Root-beer-1,Electrode-Box-B2,0,11,85,-4.92063492063492,24.2126480221718
+Root-beer-1,Electrode-Box-B2,1,18,76,-3.92063492063492,15.371378180902
+Root-beer-1,Electrode-Box-B2,2,40,105,-2.92063492063492,8.53010833963215
+Root-beer-1,Electrode-Box-B2,3,55,134,-1.92063492063492,3.68883849836231
+Root-beer-1,Electrode-Box-B2,4,75,136,-0.920634920634921,0.847568657092467
+Root-beer-1,Electrode-Box-B2,5,64,133,0.0793650793650791,0.0062988158226253
+Root-beer-1,Electrode-Box-B2,6,104,139,1.07936507936508,1.16502897455278
+Root-beer-1,Electrode-Box-B2,7,98,148,2.07936507936508,4.32375913328294
+Root-beer-1,Electrode-Box-B2,8,81,145,3.07936507936508,9.4824892920131
+Root-beer-1,Electrode-Box-B2,9,89,156,4.07936507936508,16.6412194507433
+Root-beer-1,Electrode-Box-B2,10,105,158,5.07936507936508,25.7999496094734
+Root-beer-2,Electrode-Box-A2,0,22,74,-4.92063492063492,24.2126480221718
+Root-beer-2,Electrode-Box-A2,1,31,87,-3.92063492063492,15.371378180902
+Root-beer-2,Electrode-Box-A2,2,49,134,-2.92063492063492,8.53010833963215
+Root-beer-2,Electrode-Box-A2,3,31,89,-1.92063492063492,3.68883849836231
+Root-beer-2,Electrode-Box-A2,4,60,140,-0.920634920634921,0.847568657092467
+Root-beer-2,Electrode-Box-A2,5,84,147,0.0793650793650791,0.0062988158226253
+Vu-vuong,Naive,0,18,61,-4.92063492063492,24.2126480221718
+Vu-vuong,Naive,1,35,91,-3.92063492063492,15.371378180902
+Vu-vuong,Naive,2,61,127,-2.92063492063492,8.53010833963215
+Vu-vuong,Naive,3,34,146,-1.92063492063492,3.68883849836231
+Vu-vuong,Naive,4,61,141,-0.920634920634921,0.847568657092467
+Vu-vuong,Naive,5,37,151,0.0793650793650791,0.0062988158226253
+Vu-vuong,Naive,6,49,131,1.07936507936508,1.16502897455278
+Vu-vuong,Naive,7,65,149,2.07936507936508,4.32375913328294
+Vu-vuong,Naive,8,67,141,3.07936507936508,9.4824892920131
+Vu-vuong,Naive,9,73,140,4.07936507936508,16.6412194507433
+Vu-vuong,Naive,10,72,131,5.07936507936508,25.7999496094734

analysis/matlab/export/scenarios/unmerged_d0_13.csv

@@ -0,0 +1,153 @@
+subject,group,day,success,total,day_c,day_c2
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+Banh-mi-1,Electrode-Box-B2,1,35,86,-5.125,26.265625
+Banh-mi-1,Electrode-Box-B2,2,47,110,-4.125,17.015625
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+Banh-mi-2,Electrode-Box-A2,2,22,119,-4.125,17.015625
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+Banh-mi-2,Electrode-Box-A2,10,93,152,3.875,15.015625
+Egg-tart-1,Electrode-Box-B2,0,7,56,-6.125,37.515625
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+Egg-tart-1,Electrode-Box-B2,2,23,103,-4.125,17.015625
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+Egg-tart-1,Electrode-Box-B2,4,69,132,-2.125,4.515625
+Egg-tart-1,Electrode-Box-B2,5,83,136,-1.125,1.265625
+Egg-tart-1,Electrode-Box-B2,6,71,142,-0.125,0.015625
+Egg-tart-1,Electrode-Box-B2,7,79,138,0.875,0.765625
+Egg-tart-1,Electrode-Box-B2,8,98,142,1.875,3.515625
+Egg-tart-1,Electrode-Box-B2,9,89,139,2.875,8.265625
+Egg-tart-1,Electrode-Box-B2,10,96,143,3.875,15.015625
+Egg-tart-1,Electrode-Box-B2,11,96,148,4.875,23.765625
+Egg-tart-1,Electrode-Box-B2,12,101,156,5.875,34.515625
+Egg-tart-1,Electrode-Box-B2,13,103,152,6.875,47.265625
+Egg-tart-2,Electrode-Box-A2,0,9,32,-6.125,37.515625
+Egg-tart-2,Electrode-Box-A2,1,2,38,-5.125,26.265625
+Egg-tart-2,Electrode-Box-A2,2,31,93,-4.125,17.015625
+Egg-tart-2,Electrode-Box-A2,3,44,101,-3.125,9.765625
+Egg-tart-2,Electrode-Box-A2,4,54,131,-2.125,4.515625
+Egg-tart-2,Electrode-Box-A2,5,84,139,-1.125,1.265625
+Egg-tart-2,Electrode-Box-A2,6,85,145,-0.125,0.015625
+Egg-tart-2,Electrode-Box-A2,7,79,143,0.875,0.765625
+Egg-tart-2,Electrode-Box-A2,8,76,131,1.875,3.515625
+Egg-tart-2,Electrode-Box-A2,9,88,149,2.875,8.265625
+Egg-tart-2,Electrode-Box-A2,10,81,151,3.875,15.015625
+Egg-tart-2,Electrode-Box-A2,11,78,152,4.875,23.765625
+Egg-tart-2,Electrode-Box-A2,12,96,155,5.875,34.515625
+Egg-tart-2,Electrode-Box-A2,13,84,155,6.875,47.265625
+Khoai-lang-1,Right-Electrode,0,3,69,-6.125,37.515625
+Khoai-lang-1,Right-Electrode,1,18,97,-5.125,26.265625
+Khoai-lang-1,Right-Electrode,2,27,84,-4.125,17.015625
+Khoai-lang-1,Right-Electrode,3,46,121,-3.125,9.765625
+Khoai-lang-1,Right-Electrode,4,65,143,-2.125,4.515625
+Khoai-lang-1,Right-Electrode,5,76,141,-1.125,1.265625
+Khoai-lang-1,Right-Electrode,6,79,146,-0.125,0.015625
+Khoai-lang-1,Right-Electrode,7,81,153,0.875,0.765625
+Khoai-lang-1,Right-Electrode,8,98,144,1.875,3.515625
+Khoai-lang-1,Right-Electrode,9,101,149,2.875,8.265625
+Khoai-lang-1,Right-Electrode,10,103,150,3.875,15.015625
+Khoai-lang-1,Right-Electrode,11,113,154,4.875,23.765625
+Khoai-lang-1,Right-Electrode,12,117,148,5.875,34.515625
+Khoai-lang-1,Right-Electrode,13,114,146,6.875,47.265625
+Khoai-lang-2,Naive,0,0,0,-6.125,37.515625
+Khoai-lang-2,Naive,1,0,0,-5.125,26.265625
+Khoai-lang-2,Naive,2,10,47,-4.125,17.015625
+Khoai-lang-2,Naive,3,11,52,-3.125,9.765625
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+Khoai-lang-2,Naive,5,34,95,-1.125,1.265625
+Khoai-lang-2,Naive,6,21,72,-0.125,0.015625
+Khoai-lang-2,Naive,7,23,99,0.875,0.765625
+Khoai-lang-2,Naive,8,64,136,1.875,3.515625
+Khoai-lang-2,Naive,9,75,131,2.875,8.265625
+Khoai-lang-2,Naive,10,63,134,3.875,15.015625
+Khoai-lang-2,Naive,11,59,139,4.875,23.765625
+Khoai-lang-2,Naive,12,51,129,5.875,34.515625
+Khoai-lang-2,Naive,13,73,143,6.875,47.265625
+Khoai-tay-1,Electrode-Box-A,0,14,62,-6.125,37.515625
+Khoai-tay-1,Electrode-Box-A,1,22,83,-5.125,26.265625
+Khoai-tay-1,Electrode-Box-A,2,6,79,-4.125,17.015625
+Khoai-tay-1,Electrode-Box-A,3,28,97,-3.125,9.765625
+Khoai-tay-1,Electrode-Box-A,4,60,134,-2.125,4.515625
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+Khoai-tay-1,Electrode-Box-A,8,91,132,1.875,3.515625
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+Khoai-tay-1,Electrode-Box-A,10,94,143,3.875,15.015625
+Khoai-tay-1,Electrode-Box-A,11,110,156,4.875,23.765625
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+Khoai-tay-1,Electrode-Box-A,13,106,153,6.875,47.265625
+Khoai-tay-2,Naive,1,21,68,-5.125,26.265625
+Khoai-tay-2,Naive,2,6,79,-4.125,17.015625
+Khoai-tay-2,Naive,3,0,83,-3.125,9.765625
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analysis/matlab/export/scenarios/unmerged_full.csv

@@ -0,0 +1,186 @@
+subject,group,day,success,total,day_c,day_c2
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+Vu-vuong,Naive,4,61,141,-4.2972972972973,18.4667640613586
+Vu-vuong,Naive,5,37,151,-3.2972972972973,10.8721694667641
+Vu-vuong,Naive,6,49,131,-2.2972972972973,5.27757487216946
+Vu-vuong,Naive,7,65,149,-1.2972972972973,1.68298027757487
+Vu-vuong,Naive,8,67,141,-0.297297297297296,0.0883856829802771
+Vu-vuong,Naive,9,73,140,0.702702702702704,0.493791088385684
+Vu-vuong,Naive,10,72,131,1.7027027027027,2.89919649379109
+Vu-vuong,Naive,11,89,158,2.7027027027027,7.3046018991965
+Vu-vuong,Naive,12,97,154,3.7027027027027,13.7100073046019
+Vu-vuong,Naive,13,93,145,4.7027027027027,22.1154127100073
+Vu-vuong,Naive,14,75,147,5.7027027027027,32.5208181154127
+Vu-vuong,Naive,15,68,142,6.7027027027027,44.9262235208181
+Vu-vuong,Naive,16,96,162,7.7027027027027,59.3316289262235
+Vu-vuong,Naive,17,68,144,8.7027027027027,75.7370343316289
+Vu-vuong,Naive,18,81,134,9.7027027027027,94.1424397370344
+Vu-vuong,Naive,19,66,144,10.7027027027027,114.54784514244
+Vu-vuong,Naive,20,84,127,11.7027027027027,136.953250547845
+Vu-vuong,Naive,21,66,133,12.7027027027027,161.358655953251

analysis/matlab/forouzan_load_data.m

@@ -0,0 +1,78 @@
+function T = forouzan_load_data()
+%FOROUZAN_LOAD_DATA Load the previous (Forouzan) tDCS reaching study into our
+%   long format, with group labels matching our convention.
+%
+%   T = FOROUZAN_LOAD_DATA() reads two raw files under analysis/matlab/raw/:
+%     Data_Forouzan.csv   long, fill-down RatName; per rat/session: success,
+%                         failure, total (attempts). Sessions 1..10; rats that
+%                         stopped early have blank success and total = 0
+%                         (encoded #DIV/0!), which are dropped.
+%     Total_Success.csv   per-rat Group (Anodal | Control) and Hand.
+%
+%   and returns a table with the same variables as TDCS_LOAD_DATA:
+%     subject  (string)      rat name
+%     group    (categorical) 'b2_f' for Anodal (tDCS), 'a2_f' for Control
+%     day      (double)      Session - 1, so day 0 = the paper's "Day 1"
+%                            (matching our 0-indexed day convention)
+%     success  (double)      successful reaches in the session
+%     total    (double)      total attempts in the session
+%
+%   Hand is ignored per request. The "_f" suffix marks this as the Forouzan
+%   (previous) dataset, distinct from our Electrode-Box-A2/B2 animals.
+
+thisDir = fileparts(mfilename('fullpath'));
+longFile = fullfile(thisDir, 'raw', 'Data_Forouzan.csv');
+grpFile = fullfile(thisDir, 'raw', 'Total_Success.csv');
+
+% --- group map: rat -> Anodal|Control -> b2_f|a2_f -----------------------
+G = readtable(grpFile, 'VariableNamingRule', 'preserve');
+ratNames = string(G{:, 1});                 % col 1 = Rat_Name (BOM-safe by index)
+rawGroup = string(G{:, end});               % last col = Group (Anodal|Control)
+% Keep only the real Anodal/Control rows (drops any header/blank artifact that
+% readtable mis-parses as data because of the numeric 0..9 column headers).
+keep = ismember(rawGroup, ["Anodal", "Control"]);
+ratNames = ratNames(keep);
+rawGroup = rawGroup(keep);
+label = repmat("a2_f", numel(ratNames), 1);
+label(rawGroup == "Anodal") = "b2_f";       % Control -> a2_f (default), Anodal -> b2_f
+grpMap = containers.Map(cellstr(ratNames), cellstr(label));
+
+% --- parse the long success/total file (fill-down RatName) ---------------
+lines = readlines(longFile);
+lines = lines(strlength(strip(lines)) > 0);
+lines(1) = erase(lines(1), char(65279));    % strip UTF-8 BOM from the header
+
+subj = strings(0, 1); day = []; success = []; total = [];
+curRat = "";
+for i = 2:numel(lines)                       % skip header row
+    p = strsplit(lines(i), ',', 'CollapseDelimiters', false);
+    name = strtrim(p(1));
+    if strlength(name) > 0
+        curRat = name;
+    end
+    suc = str2double(p(3));
+    tot = str2double(p(5));
+    if isnan(suc) || isnan(tot) || tot == 0  % no session that day -> drop
+        continue
+    end
+    subj(end + 1, 1) = curRat;               %#ok<AGROW>
+    day(end + 1, 1) = str2double(p(2)) - 1;  %#ok<AGROW> Session 1 -> day 0
+    success(end + 1, 1) = suc;               %#ok<AGROW>
+    total(end + 1, 1) = tot;                 %#ok<AGROW>
+end
+
+% --- attach group and finalise -------------------------------------------
+grp = strings(numel(subj), 1);
+for i = 1:numel(subj)
+    if ~isKey(grpMap, char(subj(i)))
+        error('forouzan_load_data:missingRat', ...
+            'Rat "%s" in Data_Forouzan.csv has no group in Total_Success.csv.', subj(i));
+    end
+    grp(i) = grpMap(char(subj(i)));
+end
+
+T = table(subj, categorical(grp), day, success, total, ...
+    'VariableNames', {'subject', 'group', 'day', 'success', 'total'});
+T = sortrows(T, {'subject', 'day'});
+
+end

analysis/matlab/make_crossstudy_compare.m

@@ -0,0 +1,104 @@
+function make_crossstudy_compare()
+%MAKE_CROSSSTUDY_COMPARE Cross-study current-vs-previous(_f) rate comparisons.
+%   MAKE_CROSSSTUDY_COMPARE() compares the current study's control (Box-A2,
+%   and Box-A2+Naive) and anodal (Box-B2) arms against the previous (Forouzan)
+%   study's control (a2_f) and anodal (b2_f) arms, on the per-animal success
+%   RATE (success/attempts) -- the fair cross-study metric, since the current
+%   protocol runs ~2.7x more attempts per session.
+%
+%   Two windows are reported for each arm:
+%     full     both studies over days 0-9 (equal CALENDAR time)
+%     matched  current days 0-3 vs previous full 0-9 (equal cumulative ATTEMPTS;
+%              the previous study's whole span ~= the current study's first
+%              ~3 days by attempt count)
+%
+%   Each row reports per-animal rate, mean cumulative attempts (the effort being
+%   matched), and honest two-group tests (Welch t, Mann-Whitney U). Writes
+%   analysis/matlab/crossstudy/result.txt and crossstudy_summary.csv.
+%
+%   Key finding: the current study out-performs the previous one at full range
+%   for BOTH arms, but that disappears (slightly reverses) at matched effort --
+%   a practice/attempts artifact, not a higher-performing cohort.
+
+thisDir = fileparts(mfilename('fullpath'));
+outDir = fullfile(thisDir, 'crossstudy');
+if ~exist(outDir, 'dir'); mkdir(outDir); end
+
+Tc = tdcs_load_data();
+Tf = forouzan_load_data();
+
+% arm | curLabel | curGroups | curWin | prevLabel | prevWin | mode
+specs = {
+    'Control', 'A2',       {'Electrode-Box-A2'},          [0 9], 'a2_f', [0 9], 'full'
+    'Control', 'A2',       {'Electrode-Box-A2'},          [0 3], 'a2_f', [0 9], 'matched'
+    'Control', 'A2+Naive', {'Electrode-Box-A2', 'Naive'}, [0 9], 'a2_f', [0 9], 'full'
+    'Control', 'A2+Naive', {'Electrode-Box-A2', 'Naive'}, [0 3], 'a2_f', [0 9], 'matched'
+    'Anodal',  'B2',       {'Electrode-Box-B2'},          [0 9], 'b2_f', [0 9], 'full'
+    'Anodal',  'B2',       {'Electrode-Box-B2'},          [0 3], 'b2_f', [0 9], 'matched'
+    };
+
+bar = repmat('=', 1, 92);
+s = sprintf('%s\nCROSS-STUDY COMPARISON: current vs previous (Forouzan, _f) -- per-animal success RATE\n%s\n', bar, bar);
+s = [s sprintf(['Metric: per-animal rate = sum(success)/sum(attempts). Rate is used because the\n' ...
+    'current protocol runs ~2.7x more attempts/session, so raw counts are not comparable.\n' ...
+    'Anodal = Box-B2 (contralateral tDCS); control = Box-A2 (ipsilateral sham) +/- Naive.\n' ...
+    'Previous study: b2_f = Anodal, a2_f = Control (validated: b2_f reproduces the paper''s\n' ...
+    'positive tDCS x day interaction). "matched" = current days 0-3 vs previous full 0-9,\n' ...
+    'equalising cumulative attempts (the previous study''s whole span ~= current''s first 3 days).\n\n'])];
+
+hdr = sprintf('%-8s %-10s %-8s %5s %6s %8s   %6s %6s %8s   %8s %8s', ...
+    'arm', 'current', 'window', 'nCur', 'rate', 'cumAtt', 'prev', 'rate', 'cumAtt', 'Welch p', 'MWU p');
+s = [s hdr sprintf('\n') repmat('-', 1, length(hdr)) sprintf('\n')];
+
+rows = {};
+for i = 1:size(specs, 1)
+    arm = specs{i, 1}; curLab = specs{i, 2}; curGrp = specs{i, 3};
+    curWin = specs{i, 4}; prevLab = specs{i, 5}; prevWin = specs{i, 6}; mode = specs{i, 7};
+
+    [cR, cA] = localRate(Tc, curGrp, curWin);
+    [pR, pA] = localRate(Tf, {prevLab}, prevWin);
+    [~, welch] = ttest2(cR, pR, 'Vartype', 'unequal');
+    mwu = ranksum(cR, pR);
+
+    winStr = sprintf('%d-%d', curWin(1), curWin(2));
+    s = [s sprintf('%-8s %-10s %-8s %5d %6.3f %8.0f   %6s %6.3f %8.0f   %8.3f %8.3f\n', ...
+        arm, curLab, winStr, numel(cR), mean(cR), mean(cA), ...
+        prevLab, mean(pR), mean(pA), welch, mwu)]; %#ok<AGROW>
+    rows(end + 1, :) = {arm, curLab, mode, winStr, numel(cR), mean(cR), mean(cA), ...
+        prevLab, mean(pR), mean(pA), welch, mwu}; %#ok<AGROW>
+end
+
+s = [s sprintf(['\nINTERPRETATION\n' ...
+    '  - FULL range (0-9 both): current > previous for BOTH arms (control and anodal),\n' ...
+    '    e.g. B2 0.54 vs b2_f 0.41 (p=0.024); A2+Naive 0.42 vs a2_f 0.33 (p=0.014).\n' ...
+    '  - MATCHED effort (current 0-3 vs previous full): the gap disappears and slightly\n' ...
+    '    reverses -- B2 0.33 vs b2_f 0.41 (n.s.); A2 0.30 vs a2_f 0.33 (n.s.).\n' ...
+    '  => The current study''s apparent superiority is a PRACTICE/ATTEMPTS artifact: its\n' ...
+    '     protocol packs ~2.7x more attempts/session, pushing every arm further along the\n' ...
+    '     learning curve by a given day. At equal effort the two cohorts perform the same.\n' ...
+    '     Note: A2+Naive 0-3 is slightly under-matched on attempts (294 vs ~384); A2-alone\n' ...
+    '     0-3 (361 vs 384) is the cleaner apples-to-apples match.\n'])];
+
+fprintf('%s', s);
+fid = fopen(fullfile(outDir, 'result.txt'), 'w');
+fprintf(fid, '%s', s);
+fclose(fid);
+
+T = cell2table(rows, 'VariableNames', {'arm', 'current', 'mode', 'window', ...
+    'nCur', 'rateCur', 'cumAttCur', 'prev', 'ratePrev', 'cumAttPrev', 'welchP', 'mwuP'});
+writetable(T, fullfile(outDir, 'crossstudy_summary.csv'));
+fprintf('\nWrote %s and crossstudy_summary.csv\n', fullfile(outDir, 'result.txt'));
+
+end
+
+% per-animal pooled rate and cumulative attempts over a window
+function [rate, cumAtt] = localRate(T, groups, win)
+T = T(ismember(cellstr(T.group), groups) & T.day >= win(1) & T.day <= win(2), :);
+subj = unique(T.subject);
+rate = zeros(numel(subj), 1); cumAtt = rate;
+for i = 1:numel(subj)
+    r = T.subject == subj(i);
+    rate(i) = sum(T.success(r)) / sum(T.total(r));
+    cumAtt(i) = sum(T.total(r));
+end
+end

analysis/matlab/make_figure.m

@@ -0,0 +1,89 @@
+%MAKE_FIGURE Two-panel learning figure (Box-B2 vs Box-A2), saved to results/.
+%   Panel A: mean +/- SEM success per training day over the fair 0-13 window,
+%            with the fast/slow phase boundary. Panel B: per-animal learning
+%            slope (mean +/- 95% CI) by phase. Uses the mergeA2 grouping.
+%   Usage: matlab -batch "make_figure"
+
+cfg = tdcs_config();
+Sfull = tdcs_scenario_data('mergeA2_full');
+T = Sfull(ismember(Sfull.group, {cfg.anchorLow, cfg.anchorHigh}) & Sfull.day <= 13, :);
+
+B2 = [42 120 214]/255;   % #2a78d6  (categorical slot 1)
+A2 = [0 131 0]/255;      % #008300  (categorical slot 2)
+groups = {cfg.anchorHigh, cfg.anchorLow};
+cols   = {B2, A2};
+names  = {'Box-B2 (tDCS)', 'Box-A2 (control)'};
+mk     = {'o', 's'};
+ink    = [0.10 0.10 0.10];
+
+f = figure('Visible', 'off', 'Position', [100 100 1150 460], 'Color', 'w');
+tl = tiledlayout(f, 1, 2, 'Padding', 'compact', 'TileSpacing', 'compact');
+
+% ---- Panel A: learning curves ----
+ax1 = nexttile(tl); hold(ax1, 'on');
+days = 0:13;
+hLines = gobjects(1, 2);
+for gi = 1:2
+    G = T(T.group == groups{gi}, :);
+    mu = nan(size(days)); se = nan(size(days));
+    for d = days
+        v = G.success(G.day == d);
+        if ~isempty(v); mu(d+1) = mean(v); se(d+1) = std(v)/sqrt(numel(v)); end
+    end
+    ok = ~isnan(mu);
+    fill(ax1, [days(ok) fliplr(days(ok))], [mu(ok)+se(ok) fliplr(mu(ok)-se(ok))], ...
+        cols{gi}, 'FaceAlpha', 0.13, 'EdgeColor', 'none', 'HandleVisibility', 'off');
+    hLines(gi) = plot(ax1, days(ok), mu(ok), '-', 'Color', cols{gi}, 'LineWidth', 2, ...
+        'Marker', mk{gi}, 'MarkerFaceColor', cols{gi}, 'MarkerEdgeColor', cols{gi}, 'MarkerSize', 5);
+end
+yl = ylim(ax1);
+xline(ax1, 5.5, '--', 'Color', [0.45 0.45 0.45], 'HandleVisibility', 'off');
+text(ax1, 2.6, yl(2)*0.97, 'fast (0-5)', 'Color', ink, 'FontSize', 9, 'HorizontalAlignment', 'center');
+text(ax1, 9.6, yl(2)*0.97, 'slow (6-13)', 'Color', ink, 'FontSize', 9, 'HorizontalAlignment', 'center');
+xlabel(ax1, 'Training day (# Days Reach; day 0 = paper "Day 1")', 'Color', ink);
+ylabel(ax1, 'Successful reaches / session', 'Color', ink);
+title(ax1, 'A  Learning curves (mean \pm SEM)', 'Color', ink);
+legend(ax1, hLines, names, 'Location', 'northwest', 'Box', 'off', 'TextColor', ink);
+grid(ax1, 'on'); ax1.GridAlpha = 0.10; ax1.XColor = ink; ax1.YColor = ink; ax1.Box = 'off';
+xlim(ax1, [-0.3 13.3]);
+
+% ---- Panel B: per-animal slope by phase ----
+ax2 = nexttile(tl); hold(ax2, 'on');
+phases = {[0 5], [6 13]}; phaseX = [1 2]; off = 0.14;
+hB = gobjects(1, 2);
+for gi = 1:2
+    for pi = 1:2
+        sl = localSlopes(T, groups{gi}, phases{pi});
+        m = mean(sl); ci = tinv(0.975, numel(sl)-1) * std(sl)/sqrt(numel(sl));
+        x = phaseX(pi) + (2*gi-3)*off;   % gi=1 -> -off, gi=2 -> +off
+        h = errorbar(ax2, x, m, ci, 'Marker', mk{gi}, 'Color', cols{gi}, ...
+            'MarkerFaceColor', cols{gi}, 'MarkerEdgeColor', cols{gi}, 'MarkerSize', 8, ...
+            'LineWidth', 1.8, 'CapSize', 8);
+        if pi == 1; hB(gi) = h; end
+    end
+end
+yline(ax2, 0, ':', 'Color', [0.6 0.6 0.6], 'HandleVisibility', 'off');
+xticks(ax2, [1 2]); xticklabels(ax2, {'fast (0-5)', 'slow (6-13)'});
+xlim(ax2, [0.5 2.5]);
+ylabel(ax2, 'Learning slope (reaches / day)', 'Color', ink);
+title(ax2, 'B  Per-animal learning rate by phase (mean, 95% CI)', 'Color', ink);
+legend(ax2, hB, names, 'Location', 'northeast', 'Box', 'off', 'TextColor', ink);
+grid(ax2, 'on'); ax2.GridAlpha = 0.10; ax2.XColor = ink; ax2.YColor = ink; ax2.Box = 'off';
+
+thisDir = fileparts(mfilename('fullpath'));
+resDir = fullfile(thisDir, 'results');
+if ~exist(resDir, 'dir'); mkdir(resDir); end
+outPng = fullfile(resDir, 'figure_learning.png');
+exportgraphics(f, outPng, 'Resolution', 200);
+fprintf('wrote %s\n', outPng);
+
+function sl = localSlopes(T, grp, ph)
+Tg = T(T.group == grp & T.day >= ph(1) & T.day <= ph(2), :);
+subs = unique(Tg.subject); sl = [];
+for i = 1:numel(subs)
+    r = Tg.subject == subs(i);
+    if numel(unique(Tg.day(r))) >= 2
+        c = polyfit(Tg.day(r), Tg.success(r), 1); sl(end+1,1) = c(1); %#ok<AGROW>
+    end
+end
+end

analysis/matlab/make_forouzan_data.m

@@ -0,0 +1,38 @@
+function make_forouzan_data()
+%MAKE_FOROUZAN_DATA Convert the previous (Forouzan) study to our long format.
+%   MAKE_FOROUZAN_DATA() calls FOROUZAN_LOAD_DATA and writes the curated long
+%   table to analysis/forouzan_reach_data.csv (same columns as our
+%   analysis/tdcs_reach_data.csv: subject, group, day, success, total), with
+%   group in {a2_f (Control), b2_f (Anodal)}. It then prints a descriptives
+%   summary and, as a conversion sanity-check, fits the paper's LME
+%   (behavior ~ stim + day + stim:day + (1|rat)) and compares the interaction
+%   to the paper's reported values (t(227)=2.68, F(1)=7.12, p=0.008, N=24).
+
+thisDir = fileparts(mfilename('fullpath'));
+T = forouzan_load_data();
+
+outFile = fullfile(fileparts(thisDir), 'forouzan_reach_data.csv');
+writetable(T, outFile);
+fprintf('Wrote %s  (%d rows)\n\n', outFile, height(T));
+
+fprintf('%-6s %6s %10s %12s %10s %8s\n', ...
+    'group', 'nRats', 'nSessions', 'mean_success', 'mean_rate', 'max_day');
+for g = categories(T.group)'
+    r = T.group == g{1};
+    fprintf('%-6s %6d %10d %12.1f %10.3f %8d\n', g{1}, ...
+        numel(unique(T.subject(r))), sum(r), mean(T.success(r)), ...
+        sum(T.success(r)) / sum(T.total(r)), max(T.day(r)));
+end
+
+% --- conversion sanity-check: the paper's own LME on the converted data ---
+tbl = table(T.success, T.day, double(T.group == 'b2_f'), categorical(T.subject), ...
+    'VariableNames', {'behavior', 'day', 'stim', 'rat'});
+m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
+C = m.Coefficients; A = anova(m);
+ii = strcmp(C.Name, 'day:stim'); ai = strcmp(A.Term, 'day:stim');
+fprintf(['\nPaper-LME check (behavior ~ stim + day + stim:day + (1|rat), N=%d):\n' ...
+    '  stim x day interaction: t(%d)=%.2f, F(1)=%.2f, p=%.4g\n' ...
+    '  paper reported:         t(227)=2.68, F(1)=7.12, p=0.008\n'], ...
+    numel(unique(tbl.rat)), C.DF(ii), C.tStat(ii), A.FStat(ai), C.pValue(ii));
+
+end

analysis/matlab/make_matched_effort.m

@@ -0,0 +1,99 @@
+function make_matched_effort()
+%MAKE_MATCHED_EFFORT Compare the previous (_f) study to the current study at
+%   EQUAL cumulative effort (total attempts), not equal calendar days.
+%
+%   The current animals attempt far more reaches per session than the previous
+%   (Forouzan) animals, so at equal day counts they have far more practice. This
+%   picks the current-study day cutoff 0..D whose per-animal cumulative attempts
+%   best matches the previous study's FULL (days 0-9) per-animal attempts, then
+%   builds two variation subfolders under variations/ and fits the paper's LME
+%   (behavior ~ stim + day + stim:day + (1|rat), on the success COUNT) in each:
+%
+%     variations/prev_f_full/            previous study, b2_f vs a2_f, days 0-9
+%     variations/matched_current_d0_<D>/ current anchors Box-B2 vs Box-A2, 0..D
+%
+%   Each folder holds the usual three files (data.csv, analyze.m, result.txt).
+%   A comparison of the two interaction estimates is printed at the end.
+
+thisDir = fileparts(mfilename('fullpath'));
+template = fullfile(thisDir, 'variation_analyze.m');
+root = fullfile(thisDir, 'variations');
+if ~exist(root, 'dir'); mkdir(root); end
+
+Tf = forouzan_load_data();
+Tc = tdcs_load_data();
+B2 = 'Electrode-Box-B2'; A2 = 'Electrode-Box-A2';
+
+% Target: previous study's per-animal cumulative attempts over its full span.
+target = localPerAnimalMeanTotal(Tf, 0, 9);
+fprintf('Previous (_f) full 0-9: %.0f attempts/animal\n', target);
+
+% Choose the current-study cutoff whose cumulative attempts best matches it.
+anc = Tc(ismember(cellstr(Tc.group), {A2, B2}), :);
+bestD = NaN; bestDiff = inf; bestVal = NaN;
+for d = 2:9
+    v = localPerAnimalMeanTotal(anc, 0, d);
+    fprintf('  current anchors 0-%d: %.0f attempts/animal\n', d, v);
+    if abs(v - target) < bestDiff
+        bestDiff = abs(v - target); bestD = d; bestVal = v;
+    end
+end
+fprintf('=> matched-effort window: current days 0-%d (%.0f vs %.0f attempts/animal)\n\n', ...
+    bestD, bestVal, target);
+
+% Build the two comparison folders and fit the paper LME in each.
+prevName = 'prev_f_full';
+currName = sprintf('matched_current_d0_%d', bestD);
+rPrev = localBuildAndRun(root, prevName, ...
+    localStimTable(Tf, 'b2_f'), template);
+rCurr = localBuildAndRun(root, currName, ...
+    localStimTable(anc(anc.day >= 0 & anc.day <= bestD, :), B2), template);
+
+fprintf('\n=== PAPER LME interaction (stim x day) at matched effort ===\n');
+fprintf('%-26s %5s %5s  %-22s %-14s\n', 'dataset', 'nRat', 'nObs', 'interaction', 'stim(Day1)');
+localCmpRow(prevName, rPrev);
+localCmpRow(currName, rCurr);
+fprintf(['\n(Both now represent ~%.0f attempts/animal of practice. The previous\n' ...
+    'study spreads that over 10 sessions; the current study reaches it in %d days.)\n'], ...
+    target, bestD);
+
+end
+
+% ---- per-animal mean cumulative attempts over a day window --------------
+function m = localPerAnimalMeanTotal(T, lo, hi)
+T = T(T.day >= lo & T.day <= hi, :);
+subj = unique(T.subject);
+tot = zeros(numel(subj), 1);
+for i = 1:numel(subj)
+    tot(i) = sum(T.total(T.subject == subj(i)));
+end
+m = mean(tot);
+end
+
+% ---- add a stim column (1 for treatGroup, 0 otherwise) -----------------
+function D = localStimTable(T, treatGroup)
+stim = double(strcmp(cellstr(T.group), treatGroup));
+D = table(string(T.subject), string(T.group), T.day, T.success, T.total, stim, ...
+    'VariableNames', {'subject', 'group', 'day', 'success', 'total', 'stim'});
+end
+
+% ---- write data.csv + analyze.m, run it (isolated), return VARRESULT ----
+function r = localBuildAndRun(root, name, D, template)
+folder = fullfile(root, name);
+if ~exist(folder, 'dir'); mkdir(folder); end
+writetable(D, fullfile(folder, 'data.csv'));
+copyfile(template, fullfile(folder, 'analyze.m'));
+r = localRun(fullfile(folder, 'analyze.m'));
+end
+
+function r = localRun(scriptPath)
+run(scriptPath);
+r = VARRESULT; %#ok<NODEF>
+end
+
+function localCmpRow(name, r)
+sig = 'n.s.';
+if r.interP < 0.05; sig = 'SIG'; end
+fprintf('%-26s %5d %5d  %+6.2f/day p=%.3f %-4s  p=%.3f\n', ...
+    name, r.nRats, r.nObs, r.interEst, r.interP, sig, r.stimP);
+end

analysis/matlab/make_variations.m

@@ -0,0 +1,115 @@
+function make_variations()
+%MAKE_VARIATIONS Generate one self-contained subfolder per grouping x window.
+%   MAKE_VARIATIONS() writes analysis/matlab/variations/<grouping>_<window>/
+%   for every combination below, each holding exactly three files:
+%     data.csv    the curated data subset for that variation
+%     analyze.m   a simple, standalone MATLAB script (copy of
+%                 variation_analyze.m) that fits the paper's LME on data.csv
+%     result.txt  the text result that analyze.m produces
+%   and a top-level variations/SUMMARY.csv indexing all variations.
+%
+%   Groupings (stim = 1 treatment / stim = 0 control; all OTHER groups dropped):
+%     unmerge     Box-B2                vs Box-A2
+%     right_only  Box-B2 + Right-Elec.  vs Box-A2                 (Box-A dropped)
+%     naive_a2    Box-B2                vs Box-A2 + Naive         (Right, Box-A dropped)
+%     naive_boxa  Box-B2                vs Box-A2 + Naive + Box-A (Right dropped)
+%   Windows: 0-10, 0-13, 0-5, 6-10, 6-13.
+%
+%   The model in each analyze.m is the paper's:
+%     behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT).
+
+thisDir = fileparts(mfilename('fullpath'));
+templateScript = fullfile(thisDir, 'variation_analyze.m');
+root = fullfile(thisDir, 'variations');
+if ~exist(root, 'dir'); mkdir(root); end
+
+B2 = 'Electrode-Box-B2'; A2 = 'Electrode-Box-A2'; NAIVE = 'Naive';
+BOXA = 'Electrode-Box-A'; RIGHT = 'Right-Electrode';
+
+% grouping name | treatment groups (stim=1) | control groups (stim=0)
+groupings = {
+    'unmerge',    {B2},         {A2}
+    'right_only', {B2, RIGHT},  {A2}
+    'naive_a2',   {B2},         {A2, NAIVE}
+    'naive_boxa', {B2},         {A2, NAIVE, BOXA}
+    };
+
+% window name | [loDay hiDay]
+windows = {
+    'd0_10', [0 10]
+    'd0_13', [0 13]
+    'd0_5',  [0 5]
+    'd6_10', [6 10]
+    'd6_13', [6 13]
+    };
+
+T = tdcs_load_data();
+allGroup = cellstr(T.group);
+
+rows = {};   % accumulate SUMMARY rows
+for gi = 1:size(groupings, 1)
+    gname = groupings{gi, 1};
+    treat = groupings{gi, 2};
+    ctrl = groupings{gi, 3};
+    inTreat = ismember(allGroup, treat);
+    inCtrl = ismember(allGroup, ctrl);
+
+    for wi = 1:size(windows, 1)
+        wname = windows{wi, 1};
+        w = windows{wi, 2};
+        sel = (inTreat | inCtrl) & T.day >= w(1) & T.day <= w(2);
+
+        D = T(sel, :);
+        stim = double(inTreat(sel));
+        Dout = table(string(D.subject), string(D.group), D.day, D.success, ...
+            D.total, stim, 'VariableNames', ...
+            {'subject', 'group', 'day', 'success', 'total', 'stim'});
+
+        vname = [gname '_' wname];
+        folder = fullfile(root, vname);
+        if ~exist(folder, 'dir'); mkdir(folder); end
+        writetable(Dout, fullfile(folder, 'data.csv'));
+        copyfile(templateScript, fullfile(folder, 'analyze.m'));
+
+        try
+            r = localRun(fullfile(folder, 'analyze.m'));
+            rows(end + 1, :) = {vname, gname, wname, r.nRats, r.nObs, ...
+                r.covEqual, r.interP, r.interPsatt, r.interPrs, r.interEst, ...
+                r.stimP, r.dayP}; %#ok<AGROW>
+            fprintf('  %-16s N=%2d obs=%4d  interaction p=%.4g (%+.2f)  %s\n', ...
+                vname, r.nRats, r.nObs, r.interP, r.interEst, ...
+                localTern(r.covEqual, 'equal-cov', 'UNEQUAL-cov'));
+        catch ME
+            localWriteError(folder, ME);
+            fprintf(2, '  %-16s ERROR: %s\n', vname, ME.message);
+        end
+    end
+end
+
+% Top-level index of all variations.
+S = cell2table(rows, 'VariableNames', {'variation', 'grouping', 'window', ...
+    'nRats', 'nObs', 'covEqual', 'interaction_p', 'interaction_p_satt', ...
+    'interaction_p_rs', 'interaction_est', 'stim_p', 'day_p'});
+writetable(S, fullfile(root, 'SUMMARY.csv'));
+fprintf('\nWrote %d variations under %s\n(index: variations/SUMMARY.csv)\n', ...
+    size(rows, 1), root);
+
+end
+
+function r = localRun(scriptPath)
+%LOCALRUN Execute one analyze.m in an isolated workspace and return its
+%   VARRESULT struct. Isolation prevents the script's variables (D, m, ...)
+%   from colliding with the generator's loop state.
+run(scriptPath);
+r = VARRESULT; %#ok<NODEF>  (defined by the analyze.m script just run)
+end
+
+function localWriteError(folder, ME)
+fid = fopen(fullfile(folder, 'result.txt'), 'w');
+fprintf(fid, 'ERROR fitting this variation:\n%s\n', getReport(ME, 'basic'));
+fclose(fid);
+end
+
+function out = localTern(cond, a, b)
+if cond; out = a; else; out = b; end
+end

analysis/matlab/raw/Data_Forouzan.csv

@@ -0,0 +1,241 @@
+RatName,Session,success,failure,total,,
+Afrasyab,1,1,12,13,7.692307692,30.48951049
+,2,8,16,24,33.33333333,5.444305382
+,3,13,22,35,37.14285714,24.58333333
+,4,8,21,29,27.5862069,
+,5,21,22,43,48.8372093,
+,6,14,28,42,33.33333333,
+,7,18,28,46,39.13043478,
+,8,21,31,52,40.38461538,
+,9,21,20,41,51.2195122,
+,10,21,34,55,38.18181818,
+Arash,1,9,25,34,26.47058824,5.444305382
+,2,12,29,41,29.26829268,
+,3,15,28,43,34.88372093,
+,4,18,26,44,40.90909091,
+,5,16,17,33,48.48484848,
+,6,18,29,47,38.29787234,
+,7,13,35,48,27.08333333,
+,8,9,29,38,23.68421053,
+,9,18,25,43,41.86046512,
+,10,15,32,47,31.91489362,
+Ashkas,1,2,28,30,6.666666667,24.58333333
+,2,11,24,35,31.42857143,
+,3,10,20,30,33.33333333,
+,4,9,24,33,27.27272727,
+,5,9,32,41,21.95121951,
+,6,10,26,36,27.77777778,
+,7,13,33,46,28.26086957,
+,8,9,33,42,21.42857143,
+,9,6,30,36,16.66666667,
+,10,15,33,48,31.25,
+Fariborz,1,8,24,32,25,2.272727273
+,2,10,22,32,31.25,
+,3,9,25,34,26.47058824,
+,4,11,24,35,31.42857143,
+,5,13,22,35,37.14285714,
+,6,5,33,38,13.15789474,
+,7,12,32,44,27.27272727,
+,8,,,0,#DIV/0!,
+,9,,,0,#DIV/0!,
+,10,,,0,#DIV/0!,
+Garsivaz,1,10,24,34,29.41176471,27.40641711
+,2,12,18,30,40,
+,3,14,21,35,40,
+,4,27,29,56,48.21428571,
+,5,22,19,41,53.65853659,
+,6,30,27,57,52.63157895,
+,7,34,18,52,65.38461538,
+,8,25,24,49,51.02040816,
+,9,29,17,46,63.04347826,
+,10,25,19,44,56.81818182,
+Iraj,1,6,23,29,20.68965517,7.11912672
+,2,13,24,37,35.13513514,
+,3,15,17,32,46.875,
+,4,20,29,49,40.81632653,
+,5,17,30,47,36.17021277,
+,6,21,26,47,44.68085106,
+,7,24,25,49,48.97959184,
+,8,,,0,#DIV/0!,
+,9,,,0,#DIV/0!,
+,10,,,0,#DIV/0!,
+Khosrow,1,6,17,23,26.08695652,11.41304348
+,2,7,20,27,25.92592593,
+,3,10,17,27,37.03703704,
+,4,10,16,26,38.46153846,
+,5,14,21,35,40,
+,6,20,21,41,48.7804878,
+,7,11,26,37,29.72972973,
+,8,13,21,34,38.23529412,
+,9,13,17,30,43.33333333,
+,10,15,25,40,37.5,
+Kiyanoush,1,11,8,19,57.89473684,25.77873255
+,2,22,30,52,42.30769231,
+,3,34,24,58,58.62068966,
+,4,30,22,52,57.69230769,
+,5,27,29,56,48.21428571,
+,6,46,19,65,70.76923077,
+,7,32,26,58,55.17241379,
+,8,47,14,61,77.04918033,
+,9,49,19,68,72.05882353,
+,10,41,8,49,83.67346939,
+Manuchehr,1,1,21,22,4.545454545,37.48353096
+,2,8,10,18,44.44444444,
+,3,9,32,41,21.95121951,
+,4,14,33,47,29.78723404,
+,5,25,35,60,41.66666667,
+,6,22,48,70,31.42857143,
+,7,37,37,74,50,
+,8,40,35,75,53.33333333,
+,9,32,23,55,58.18181818,
+,10,29,40,69,42.02898551,
+Mehrab,1,8,20,28,28.57142857,22.53968254
+,2,13,13,26,50,
+,3,16,29,45,35.55555556,
+,4,17,17,34,50,
+,5,21,24,45,46.66666667,
+,6,21,34,55,38.18181818,
+,7,22,29,51,43.1372549,
+,8,21,27,48,43.75,
+,9,21,27,48,43.75,
+,10,23,22,45,51.11111111,
+Merdas,1,7,23,30,23.33333333,19.21985816
+,2,9,27,36,25,
+,3,8,25,33,24.24242424,
+,4,17,31,48,35.41666667,
+,5,21,24,45,46.66666667,
+,6,13,26,39,33.33333333,
+,7,19,24,43,44.18604651,
+,8,10,38,48,20.83333333,
+,9,19,33,52,36.53846154,
+,10,20,27,47,42.55319149,
+Nozar,1,4,16,20,20,22.55319149
+,2,5,18,23,21.73913043,
+,3,8,35,43,18.60465116,
+,4,8,40,48,16.66666667,
+,5,11,29,40,27.5,
+,6,13,40,53,24.52830189,
+,7,15,35,50,30,
+,8,19,27,46,41.30434783,
+,9,18,38,56,32.14285714,
+,10,20,27,47,42.55319149,
+Pashang,1,5,27,32,15.625,44.02412281
+,2,7,44,51,13.7254902,
+,3,18,31,49,36.73469388,
+,4,16,27,43,37.20930233,
+,5,21,15,36,58.33333333,
+,6,16,37,53,30.18867925,
+,7,18,24,42,42.85714286,
+,8,23,21,44,52.27272727,
+,9,20,23,43,46.51162791,
+,10,34,23,57,59.64912281,
+Pashin,1,7,20,27,25.92592593,14.07407407
+,2,9,13,22,40.90909091,
+,3,11,30,41,26.82926829,
+,4,14,26,40,35,
+,5,12,24,36,33.33333333,
+,6,14,29,43,32.55813953,
+,7,11,31,42,26.19047619,
+,8,14,33,47,29.78723404,
+,9,13,37,50,26,
+,10,18,27,45,40,
+Rostam,1,9,20,29,31.03448276,16.88218391
+,2,12,24,36,33.33333333,
+,3,14,28,42,33.33333333,
+,4,18,23,41,43.90243902,
+,5,15,30,45,33.33333333,
+,6,20,29,49,40.81632653,
+,7,21,30,51,41.17647059,
+,8,24,24,48,50,
+,9,19,27,46,41.30434783,
+,10,23,25,48,47.91666667,
+Salm,1,7,21,28,25,12.20930233
+,2,10,26,36,27.77777778,
+,3,16,34,50,32,
+,4,18,31,49,36.73469388,
+,5,21,41,62,33.87096774,
+,6,26,34,60,43.33333333,
+,7,27,26,53,50.94339623,
+,8,32,30,62,51.61290323,
+,9,16,24,40,40,
+,10,16,27,43,37.20930233,
+Sam,1,8,20,28,28.57142857,13.65079365
+,2,11,21,32,34.375,
+,3,10,16,26,38.46153846,
+,4,11,28,39,28.20512821,
+,5,12,24,36,33.33333333,
+,6,14,20,34,41.17647059,
+,7,15,28,43,34.88372093,
+,8,9,22,31,29.03225806,
+,9,23,28,51,45.09803922,
+,10,19,26,45,42.22222222,
+Siavash,1,10,11,21,47.61904762,2.380952381
+,2,3,22,25,12,
+,3,7,32,39,17.94871795,
+,4,13,23,36,36.11111111,
+,5,12,23,35,34.28571429,
+,6,18,14,32,56.25,
+,7,18,27,45,40,
+,8,22,15,37,59.45945946,
+,9,18,18,36,50,
+,10,17,17,34,50,
+Sohrab,1,8,15,23,34.7826087,18.7057634
+,2,11,23,34,32.35294118,
+,3,15,30,45,33.33333333,
+,4,19,28,47,40.42553191,
+,5,18,32,50,36,
+,6,18,27,45,40,
+,7,16,29,45,35.55555556,
+,8,20,29,49,40.81632653,
+,9,14,18,32,43.75,
+,10,23,20,43,53.48837209,
+Tahmasb,1,7,25,32,21.875,14.19057377
+,2,11,27,38,28.94736842,
+,3,8,29,37,21.62162162,
+,4,7,19,26,26.92307692,
+,5,12,23,35,34.28571429,
+,6,13,26,39,33.33333333,
+,7,11,30,41,26.82926829,
+,8,15,37,52,28.84615385,
+,9,23,38,61,37.70491803,
+,10,22,39,61,36.06557377,
+Tur,1,6,33,39,15.38461538,30.26755853
+,2,11,25,36,30.55555556,
+,3,9,26,35,25.71428571,
+,4,16,29,45,35.55555556,
+,5,19,33,52,36.53846154,
+,6,14,33,47,29.78723404,
+,7,21,25,46,45.65217391,
+,8,,,0,#DIV/0!,
+,9,,,0,#DIV/0!,
+,10,,,0,#DIV/0!,
+Tus,1,4,28,32,12.5,32.5
+,2,9,30,39,23.07692308,
+,3,5,35,40,12.5,
+,4,15,27,42,35.71428571,
+,5,21,29,50,42,
+,6,16,36,52,30.76923077,
+,7,19,40,59,32.20338983,
+,8,28,29,57,49.12280702,
+,9,27,35,62,43.5483871,
+,10,27,33,60,45,
+Zal,1,5,29,34,14.70588235,22.43697479
+,2,12,24,36,33.33333333,
+,3,16,21,37,43.24324324,
+,4,18,22,40,45,
+,5,14,20,34,41.17647059,
+,6,15,26,41,36.58536585,
+,7,16,30,46,34.7826087,
+,8,15,29,44,34.09090909,
+,9,20,26,46,43.47826087,
+,10,18,32,50,36,
+Zav,1,4,24,28,14.28571429,28.57142857
+,2,7,30,37,18.91891892,
+,3,7,34,41,17.07317073,
+,4,4,28,32,12.5,
+,5,8,29,37,21.62162162,
+,6,4,33,37,10.81081081,
+,7,7,29,36,19.44444444,
+,8,15,41,56,26.78571429,
+,9,19,35,54,35.18518519,
+,10,21,28,49,42.85714286,

analysis/matlab/raw/Total_Success.csv

@@ -0,0 +1,25 @@
+Rat_Name,0,1,2,3,4,5,6,7,8,9,Hand,Group
+Arash,9,12,15,18,16,17,13,7,14,15,Right,Anodal
+Garsivaz,10,12,15,27,22,30,33,26,30,27,Right,Anodal
+Iraj,6,11,15,20,17,21,24,,,,Right,Anodal
+Khosrow,6,6,10,10,14,18,11,13,13,15,Left,Anodal
+Kiyanoush,11,22,31,30,27,45,32,47,49,41,Right,Anodal
+Manuchehr,1,8,9,14,26,22,36,40,32,28,Right,Anodal
+Pashang,5,8,19,16,20,16,19,23,20,35,Right,Anodal
+Rostam,9,12,14,18,14,19,21,25,19,22,Right,Anodal
+Sam,8,11,7,11,13,14,15,10,23,19,Left,Anodal
+Sohrab,8,12,15,18,17,18,16,20,14,23,Left,Anodal
+Tahmasb,7,12,8,7,13,13,12,16,23,22,Right,Anodal
+Tus,5,10,5,15,22,16,18,29,28,28,Right,Anodal
+Fariborz,8,10,9,11,13,6,12,,,,Left,Control
+Siavash,10,3,7,13,12,18,19,22,18,17,Left,Control
+Afrasyab,1,6,12,9,23,15,18,21,21,21,Left,Control
+Ashkas,1,11,11,9,9,10,12,6,6,14,Left,Control
+Mehrab,8,13,16,17,21,21,23,21,22,23,Left,Control
+Merdas,8,9,8,16,21,13,19,10,19,20,Left,Control
+Nozar,4,5,8,8,11,13,15,20,18,20,Right,Control
+Pashin,7,9,11,14,13,14,11,12,13,18,Left,Control
+Salm,7,10,15,18,21,26,26,33,17,15,Right,Control
+Tur,6,10,9,17,18,14,20,,,,Right,Control
+Zal,5,12,16,18,14,14,18,15,20,18,Left,Control
+Zav,3,8,7,3,8,4,7,15,19,22,Right,Control

analysis/matlab/raw/raw_success.csv

@@ -0,0 +1,35 @@
+Data,Success
+
+Group,—,—,Electrode-Box-A,Electrode-Box-A2,Electrode-Box-A2,Electrode-Box-A2,Electrode-Box-B2,Electrode-Box-B2,Electrode-Box-B2,Electrode-Box-B2,Left-Electrode-Cathodal,Naive,Naive,Naive,Right-Electrode
+# Days Reach,Flan-1,Vu-vuong,Khoai-tay-1,Banh-mi-2,Egg-tart-2,Root-beer-2,Banh-mi-1,Egg-tart-1,Flan-2,Root-beer-1,OM-1,Khoai-lang-2,Khoai-tay-2,OM-2,Khoai-lang-1
+-4,,,,,,,,,,,,,,,0
+-3,,,,,,,,,,,,,,,2
+-2,,,1,,,,,,,,,,,0,
+-1,,,3,2,,,,,,,,,,5,0
+0,,18,14,25,9,22,13,7,,11,0,0,,1,3
+1,,35,22,22,2,31,35,16,,18,1,0,21,11,18
+2,,61,6,22,31,49,47,23,,40,3,10,6,19,27
+3,,34,28,29,44,31,65,63,,55,1,11,0,29,46
+4,,61,60,27,54,60,70,69,,75,11,9,28,73,65
+5,,37,75,43,84,84,102,83,,64,5,34,31,53,76
+6,,49,81,74,85,,90,71,,104,9,21,62,73,79
+7,,65,78,70,79,,109,79,,98,,23,87,80,81
+8,,67,91,65,76,,104,98,,81,,64,105,91,98
+9,,73,99,79,88,,119,89,,89,,75,75,90,101
+10,,72,94,93,81,,121,96,,105,,63,101,95,103
+11,,89,110,,78,,121,96,,,,59,102,60,113
+12,,97,105,,96,,120,101,,,,51,95,58,117
+13,,93,106,,84,,135,103,,,,73,77,,114
+14,,75,91,,,,,97,,,,82,91,,119
+15,,68,,,,,,,,,,70,,,111
+16,,96,,,,,,,,,,76,,,74
+17,,68,,,,,,,,,,76,,,109
+18,,81,,,,,,,,,,63,,,88
+19,,66,,,,,,,,,,48,,,98
+20,,84,,,,,,,,,,65,,,118
+21,,66,,,,,,,,,,75,,,109
+22,,,,,,,,,,,,98,,,106
+23,,,,,,,,,,,,88,,,
+24,,,,,,,,,,,,94,,,
+25,,,,,,,,,,,,56,,,
+26,,,,,,,,,,,,75,,,

analysis/matlab/raw/raw_total.csv

@@ -0,0 +1,35 @@
+Data,Total attempts
+
+Group,—,—,Electrode-Box-A,Electrode-Box-A2,Electrode-Box-A2,Electrode-Box-A2,Electrode-Box-B2,Electrode-Box-B2,Electrode-Box-B2,Electrode-Box-B2,Left-Electrode-Cathodal,Naive,Naive,Naive,Right-Electrode
+# Days Reach,Flan-1,Vu-vuong,Khoai-tay-1,Banh-mi-2,Egg-tart-2,Root-beer-2,Banh-mi-1,Egg-tart-1,Flan-2,Root-beer-1,OM-1,Khoai-lang-2,Khoai-tay-2,OM-2,Khoai-lang-1
+-4,,,,,,,,,,,,,,,0
+-3,,,,,,,,,20,,,,,,7
+-2,,,18,,,,,,,,,,,0,
+-1,,,24,42,,,,,,,,,,18,0
+0,,61,62,97,32,74,84,56,,85,0,0,,7,69
+1,,91,83,101,38,87,86,78,,76,10,0,68,33,97
+2,,127,79,119,93,134,110,103,,105,5,47,79,62,84
+3,,146,97,118,101,89,140,120,,134,2,52,83,117,121
+4,,141,134,136,131,140,127,132,,136,44,56,87,126,143
+5,,151,138,146,139,147,142,136,,133,23,95,125,135,141
+6,,131,137,146,145,,131,142,,139,32,72,144,138,146
+7,,149,147,148,143,,148,138,,148,,99,141,131,153
+8,,141,132,130,131,,137,142,,145,,136,152,141,144
+9,,140,146,151,149,,150,139,,156,,131,148,135,149
+10,,131,143,152,151,,158,143,,158,,134,149,142,150
+11,,158,156,,152,,148,148,,,,139,154,133,154
+12,,154,143,,155,,149,156,,,,129,144,142,148
+13,,145,153,,155,,154,152,,,,143,149,,146
+14,,147,152,,,,,152,,,,136,153,,132
+15,,142,,,,,,,,,,145,,,156
+16,,162,,,,,,,,,,135,,,156
+17,,144,,,,,,,,,,150,,,139
+18,,134,,,,,,,,,,122,,,138
+19,,144,,,,,,,,,,116,,,140
+20,,127,,,,,,,,,,134,,,153
+21,,133,,,,,,,,,,131,,,146
+22,,,,,,,,,,,,146,,,141
+23,,,,,,,,,,,,139,,,
+24,,,,,,,,,,,,148,,,
+25,,,,,,,,,,,,102,,,
+26,,,,,,,,,,,,143,,,

analysis/matlab/results/lme_mergeA2_d0_10.txt

@@ -0,0 +1,76 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeA2_d0_10
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 83 sessions
+day coverage: Box-B2 0..10, Box-A2 0..10
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              83
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    673.84    688.35    -330.92          661.84  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}        15.599       3.7298     4.1822    79    7.4136e-05
+    {'day'        }        8.3258      0.66962     12.434    79    2.7592e-20
+    {'tDCS'       }        4.1737       5.2381    0.79679    79       0.42796
+    {'day:tDCS'   }        1.3833       0.9137      1.514    79       0.13402
+
+
+    Lower       Upper 
+       8.175    23.023
+      6.9929    9.6586
+     -6.2525      14.6
+    -0.43535     3.202
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        13.04       11.2     15.183
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(79)=  1.51 F(1)=  2.292  p=0.134    p=0.1338 (df=83)
+days (learning)             t(79)= 12.43 F(1)=154.593  p=2.759e-20    p=1.192e-20 (df=83)
+tDCS (main, at Day 1)       t(79)=  0.80 F(1)=  0.635  p=0.428    p=0.4278 (df=83)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,20.3)=1.35, p=0.258
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1340, slope diff=1.38) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=2.8e-20) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.4280) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/lme_mergeA2_d0_13.txt

@@ -0,0 +1,76 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeA2_d0_13
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 98 sessions
+day coverage: Box-B2 0..13, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              98
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    809.08    824.59    -398.54          797.08  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}        19.458       3.7166    5.2352    94    1.0011e-06
+    {'day'        }        7.1842      0.54693    13.135    94    5.4028e-23
+    {'tDCS'       }         5.841       5.1946    1.1244    94       0.26369
+    {'day:tDCS'   }        1.0024      0.73807    1.3581    94       0.17769
+
+
+    Lower      Upper 
+     12.078    26.837
+     6.0982    8.2701
+     -4.473    16.155
+    -0.4631    2.4678
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        14.123      12.278    16.245
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(94)=  1.36 F(1)=  1.844  p=0.1777    p=0.1776 (df=98)
+days (learning)             t(94)= 13.14 F(1)=172.537  p=5.403e-23    p=2.463e-23 (df=98)
+tDCS (main, at Day 1)       t(94)=  1.12 F(1)=  1.264  p=0.2637    p=0.2636 (df=98)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,16.2)=1.37, p=0.2593
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1777, slope diff=1.00) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=5.4e-23) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.2637) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/lme_mergeA2_full.txt

@@ -0,0 +1,79 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeA2_full
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 109 sessions
+day coverage: Box-B2 0..22, Box-A2 0..14
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+** WARNING: the groups' day coverage is UNEQUAL (differ by 8 days). The
+   interaction/slope over this window EXTRAPOLATES the shorter group's line and is
+   CONFOUNDED -- prefer the _d0_14 window (both groups have data throughout). **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             109
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    962.68    978.83    -475.34          950.68  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF     pValue    
+    {'(Intercept)'}         20.701      4.9127     4.2138    105    5.3256e-05
+    {'day'        }         6.8599     0.69872     9.8178    105    1.5726e-16
+    {'tDCS'       }         22.092      6.4372     3.4319    105    0.00085848
+    {'day:tDCS'   }        -2.1793     0.81821    -2.6635    105     0.0089525
+
+
+    Lower      Upper   
+      10.96      30.442
+     5.4745      8.2453
+     9.3279      34.855
+    -3.8016    -0.55692
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1043e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        18.954      16.597    21.644
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(105)= -2.66 F(1)=  7.094  p=0.008953    p=0.008906 (df=109)
+days (learning)             t(105)=  9.82 F(1)= 96.388  p=1.573e-16    p=1.113e-16 (df=109)
+tDCS (main, at Day 1)       t(105)=  3.43 F(1)= 11.778  p=0.0008585    p=0.000848 (df=109)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,9.7)=0.11, p=0.746
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: SIGNIFICANT (p=0.0090, slope diff=-2.18) -> the tDCS (Box-B2) group improves SLOWER -- groups CONVERGE (Box-B2 is ahead early, the gap narrows).
+  - days (learning): SIGNIFICANT (p=1.6e-16) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): SIGNIFICANT (p=0.0009) -> the groups already DIFFER on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/lme_mergeB2_d0_10.txt

@@ -0,0 +1,76 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_d0_10
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 83 sessions
+day coverage: Box-B2 0..10, Box-A2 0..10
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              83
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    669.06    683.57    -328.53          657.06  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue   
+    {'(Intercept)'}         17.318      5.1725      3.348    79    0.0012488
+    {'day'        }         7.9975     0.75795     10.551    79    9.547e-17
+    {'tDCS'       }        0.42782      6.5088    0.06573    79      0.94776
+    {'day:tDCS'   }         1.7407     0.91342     1.9057    79     0.060324
+
+
+    Lower       Upper 
+      7.0221    27.613
+      6.4888    9.5061
+     -12.528    13.383
+    -0.07739    3.5588
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7075  
+
+
+    Lower     Upper 
+    2.7829    11.705
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        11.955      10.184    14.034
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(79)=  1.91 F(1)=  3.632  p=0.06032    p=0.06034 (df=79)
+days (learning)             t(79)= 10.55 F(1)=111.334  p=9.547e-17    p=8.241e-17 (df=80)
+tDCS (main, at Day 1)       t(79)=  0.07 F(1)=  0.004  p=0.9478    p=0.9482 (df=21)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,30.6)=3.00, p=0.09319
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.0603, slope diff=1.74) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=9.5e-17) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.9478) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/lme_mergeB2_d0_13.txt

@@ -0,0 +1,76 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_d0_13
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 98 sessions
+day coverage: Box-B2 0..13, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              98
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    803.97    819.48    -395.99          791.97  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}        21.528       5.1581     4.1736    94    6.6907e-05
+    {'day'        }        6.6603      0.67208       9.91    94     2.855e-16
+    {'tDCS'       }        1.8428       6.4496    0.28572    94       0.77572
+    {'day:tDCS'   }        1.5375      0.78742     1.9525    94      0.053848
+
+
+    Lower        Upper 
+       11.286    31.769
+       5.3259    7.9948
+      -10.963    14.649
+    -0.025966    3.1009
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.4251  
+
+
+    Lower     Upper
+    2.6045    11.3 
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        13.148      11.362    15.215
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(94)=  1.95 F(1)=  3.812  p=0.05385    p=0.05377 (df=97)
+days (learning)             t(94)=  9.91 F(1)= 98.208  p=2.855e-16    p=1.981e-16 (df=98)
+tDCS (main, at Day 1)       t(94)=  0.29 F(1)=  0.082  p=0.7757    p=0.7776 (df=24)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,29.7)=3.25, p=0.08138
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.0538, slope diff=1.54) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=2.9e-16) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.7757) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/lme_mergeB2_full.txt

@@ -0,0 +1,79 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_full
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 109 sessions
+day coverage: Box-B2 0..22, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+** WARNING: the groups' day coverage is UNEQUAL (differ by 9 days). The
+   interaction/slope over this window EXTRAPOLATES the shorter group's line and is
+   CONFOUNDED -- prefer the _d0_13 window (both groups have data throughout). **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             109
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    967.24    983.39    -477.62          955.24  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF     pValue    
+    {'(Intercept)'}           21.7     5.9981     3.6178    105    0.00045861
+    {'day'        }         6.4863     0.9412     6.8916    105    4.2336e-10
+    {'tDCS'       }         17.493     7.1288     2.4538    105      0.015779
+    {'day:tDCS'   }        -1.4651      1.025    -1.4294    105       0.15586
+
+
+    Lower      Upper  
+     9.8069     33.593
+     4.6201     8.3526
+     3.3578     31.628
+    -3.4974    0.56726
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1487e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        19.354      16.948    22.101
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(105)= -1.43 F(1)=  2.043  p=0.1559    p=0.1558 (df=109)
+days (learning)             t(105)=  6.89 F(1)= 47.494  p=4.234e-10    p=3.734e-10 (df=109)
+tDCS (main, at Day 1)       t(105)=  2.45 F(1)=  6.021  p=0.01578    p=0.01572 (df=109)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,11.8)=0.02, p=0.9027
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1559, slope diff=-1.47) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=4.2e-10) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): SIGNIFICANT (p=0.0158) -> the groups already DIFFER on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/lme_unmerged_d0_10.txt

@@ -0,0 +1,76 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_d0_10
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 6 subjects, 61 sessions
+day coverage: Box-B2 0..10, Box-A2 0..10
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              61
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    499.59    512.25    -243.79          487.59  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}        17.372       5.1906     3.3469    57     0.0014518
+    {'day'        }        7.9666      0.79053     10.077    57    2.8317e-14
+    {'tDCS'       }        4.9763       7.2862    0.68298    57       0.49739
+    {'day:tDCS'   }        1.5577       1.0483     1.4858    57       0.14283
+
+
+    Lower      Upper 
+     6.9783    27.766
+     6.3836    9.5496
+     -9.614    19.567
+    -0.5416    3.6569
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.3536  
+
+
+    Lower     Upper 
+    2.1542    13.305
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        12.508      10.37    15.086
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(57)=  1.49 F(1)=  2.208  p=0.1428    p=0.1428 (df=57)
+days (learning)             t(57)= 10.08 F(1)=101.556  p=2.832e-14    p=1.931e-14 (df=59)
+tDCS (main, at Day 1)       t(57)=  0.68 F(1)=  0.466  p=0.4974    p=0.5037 (df=17)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,13.0)=1.46, p=0.2479
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1428, slope diff=1.56) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=2.8e-14) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.4974) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/lme_unmerged_d0_13.txt

@@ -0,0 +1,76 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_d0_13
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 6 subjects, 70 sessions
+day coverage: Box-B2 0..13, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              70
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    586.72    600.22    -287.36          574.72  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}          21.7       4.5484     4.7709    66    1.0537e-05
+    {'day'        }        6.4863      0.71372     9.0881    66    3.0346e-13
+    {'tDCS'       }        6.0226       6.3135    0.95392    66        0.3436
+    {'day:tDCS'   }        1.5467      0.93755     1.6497    66       0.10376
+
+
+    Lower       Upper 
+      12.619    30.781
+      5.0614    7.9113
+     -6.5827    18.628
+    -0.32523    3.4185
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper
+    {'Res Std'}        14.676      12.436    17.32
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(66)=  1.65 F(1)=  2.721  p=0.1038    p=0.1035 (df=70)
+days (learning)             t(66)=  9.09 F(1)= 82.593  p=3.035e-13    p=1.823e-13 (df=70)
+tDCS (main, at Day 1)       t(66)=  0.95 F(1)=  0.910  p=0.3436    p=0.3434 (df=70)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,12.8)=1.64, p=0.2227
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1038, slope diff=1.55) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=3e-13) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.3436) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/lme_unmerged_full.txt

@@ -0,0 +1,76 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_full
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 6 subjects, 71 sessions
+day coverage: Box-B2 0..14, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              71
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    596.33    609.9    -292.16          584.33  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}          21.53      5.5382    3.8874    67    0.00023506
+    {'day'        }         6.6587     0.72367    9.2014    67    1.6706e-13
+    {'tDCS'       }         8.0204      7.6977    1.0419    67        0.3012
+    {'day:tDCS'   }        0.93747      0.9187    1.0204    67        0.3112
+
+
+    Lower       Upper 
+      10.475    32.584
+      5.2143    8.1032
+     -7.3444    23.385
+    -0.89627    2.7712
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7931  
+
+
+    Lower     Upper 
+    2.4723    13.574
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        14.163      11.93    16.813
+
+
+effect                      t(df) / F(df1)       p (resid)    Satterthwaite: p (df)
+------------------------------------------------------------------------------
+days x tDCS (interaction)   t(67)=  1.02 F(1)=  1.041  p=0.3112    p=0.3111 (df=70)
+days (learning)             t(67)=  9.20 F(1)= 84.665  p=1.671e-13    p=1.04e-13 (df=71)
+tDCS (main, at Day 1)       t(67)=  1.04 F(1)=  1.086  p=0.3012    p=0.3116 (df=18)
+
+HONEST LME -- per-animal random slope (day|subject): interaction F(1,8.8)=0.61, p=0.456
+  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual
+  (the slope's error is at the session level), so it does NOT fix pseudoreplication.
+  Letting each animal have its OWN slope collapses the interaction DF toward the animal
+  count -- this, and the per-animal slope test, are the honest learning-rate inference.
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.3112, slope diff=0.94) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=1.7e-13) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.3012) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)

analysis/matlab/results/mergeA2_d0_10.txt

@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeA2_d0_10
+==============================================================================
+merge key: mergeA2   day window: 0..10   observations: 126
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            4         44          68.3      0.541       10
+Electrode-Box-A2            4         39          54.0      0.452       10
+Naive                       4         43          46.8      0.440       10
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    297.29    314.31    -142.65          285.29  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat       DF 
+    {'(Intercept)'           }           4.2656     0.092258      46.236    121
+    {'group_Electrode-Box-A2'}         -0.12664      0.12996    -0.97447    121
+    {'group_Naive'           }         -0.41983      0.12988     -3.2324    121
+    {'day_c'                 }          0.20187    0.0052718      38.293    121
+    {'day_c2'                }        -0.024068    0.0015714     -15.317    121
+
+
+    pValue        Lower        Upper    
+    9.3543e-79        4.083       4.4483
+       0.33177     -0.38392      0.13065
+     0.0015818     -0.67696      -0.1627
+    1.7547e-69      0.19144      0.21231
+    4.2028e-30    -0.027179    -0.020957
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17893 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             124
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    363.74    380.67    -175.87          351.74  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat      DF 
+    {'(Intercept)'           }          0.15282       0.1244     1.2285    119
+    {'group_Electrode-Box-A2'}         -0.28467      0.17474    -1.6291    119
+    {'group_Naive'           }         -0.57945      0.17473    -3.3162    119
+    {'day_c'                 }          0.21677    0.0069268     31.294    119
+    {'day_c2'                }        -0.013732    0.0021989    -6.2451    119
+
+
+    pValue        Lower        Upper     
+       0.22169      -0.0935       0.39915
+       0.10595     -0.63068      0.061343
+     0.0012101     -0.92543      -0.23346
+    2.8838e-59      0.20305       0.23048
+    6.7844e-09    -0.018086    -0.0093781
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.23963 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    304.66    327.35    -144.33          288.66  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate     SE           tStat  
+    {'(Intercept)'                 }           4.2968     0.091824     46.794
+    {'group_Electrode-Box-A2'      }         -0.15174      0.12947     -1.172
+    {'group_Naive'                 }         -0.50686      0.13029    -3.8902
+    {'day_c'                       }          0.18436    0.0072535     25.417
+    {'day_c2'                      }        -0.024459     0.001577    -15.509
+    {'group_Electrode-Box-A2:day_c'}         0.014228     0.010876     1.3082
+    {'group_Naive:day_c'           }         0.051203     0.011091     4.6167
+
+
+    DF     pValue        Lower         Upper    
+    119    1.7772e-78         4.115       4.4786
+    119       0.24354       -0.4081      0.10462
+    119    0.00016554      -0.76486     -0.24887
+    119    6.5517e-50          0.17      0.19872
+    119    2.3835e-30     -0.027581    -0.021336
+    119       0.19332    -0.0073071     0.035763
+    119    9.9271e-06      0.029242     0.073164
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17742 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=9.71, Box-A2 mean=9.18
+  Welch two-sided p=0.556, Mann-Whitney p=0.686 (nB=4, nA=4)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0001, but with just
+ observation-level DF (df2=119) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.14x Box-A2  (one-sided p=0.1649)  -> not supported
+  [rate/level ] Box-B2 = 1.33x Box-A2  (one-sided p=0.0517)  -> not supported
+
+Per-animal (Box-B2 n=4 vs Box-A2 n=4), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0230, Mann-Whitney one-sided (B2>A2) p=0.0143
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0689, Mann-Whitney one-sided (B2>A2) p=0.0571
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.

analysis/matlab/results/mergeA2_full.txt

@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeA2_full
+==============================================================================
+merge key: mergeA2   day window: 0..26   observations: 185
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            4         63          80.2      0.605       22
+Electrode-Box-A2            4         46          60.4      0.484       14
+Naive                       4         76          59.6      0.492       26
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    564.95    584.27    -276.47          552.95  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat      DF 
+    {'(Intercept)'           }            4.5232      0.058089     77.867    180
+    {'group_Electrode-Box-A2'}           -0.1219      0.083374    -1.4621    180
+    {'group_Naive'           }          -0.36897      0.082023    -4.4984    180
+    {'day_c'                 }           0.10078      0.002307     43.684    180
+    {'day_c2'                }        -0.0064744    0.00024857    -26.047    180
+
+
+    pValue         Lower         Upper     
+    1.4699e-140        4.4086        4.6379
+        0.14547      -0.28641      0.042619
+     1.2247e-05      -0.53082      -0.20712
+     9.6816e-98      0.096227       0.10533
+     5.8173e-63    -0.0069649    -0.0059839
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.11179 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             183
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    661.66    680.91    -324.83          649.66  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat      DF 
+    {'(Intercept)'           }           0.62325       0.10794     5.7739    178
+    {'group_Electrode-Box-A2'}          -0.33496       0.15306    -2.1884    178
+    {'group_Naive'           }          -0.64512       0.15201    -4.2441    178
+    {'day_c'                 }           0.12541     0.0034219     36.649    178
+    {'day_c2'                }        -0.0069611    0.00035344    -19.695    178
+
+
+    pValue        Lower         Upper     
+    3.3843e-08       0.41024       0.83626
+      0.029941      -0.63701     -0.032914
+    3.5275e-05      -0.94509      -0.34516
+    7.5329e-85       0.11866       0.13216
+    1.4145e-46    -0.0076585    -0.0062636
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.20951 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    557.88    583.64    -270.94          541.88  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate      SE           tStat  
+    {'(Intercept)'                 }            4.5419     0.060057     75.626
+    {'group_Electrode-Box-A2'      }          -0.13686     0.086298    -1.5859
+    {'group_Naive'                 }          -0.41002     0.085075    -4.8196
+    {'day_c'                       }          0.090967    0.0032463     28.021
+    {'day_c2'                      }        -0.0068957    0.0002769    -24.904
+    {'group_Electrode-Box-A2:day_c'}         0.0071293    0.0064228       1.11
+    {'group_Naive:day_c'           }          0.023329    0.0043517     5.3609
+
+
+    DF     pValue         Lower         Upper     
+    178    3.0366e-137        4.4233        4.6604
+    178        0.11453      -0.30716      0.033436
+    178     3.0699e-06      -0.57791      -0.24214
+    178     3.6033e-67      0.084561      0.097373
+    178     6.7558e-60    -0.0074422    -0.0063493
+    178         0.2685    -0.0055454      0.019804
+    178     2.5437e-07      0.014742      0.031917
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.11568 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=6.92, Box-A2 mean=7.95
+  Welch two-sided p=0.531, Mann-Whitney p=0.886 (nB=4, nA=4)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=178) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.13x Box-A2  (one-sided p=0.0719)  -> not supported
+  [rate/level ] Box-B2 = 1.40x Box-A2  (one-sided p=0.0143)  -> SUPPORTED
+
+Per-animal (Box-B2 n=4 vs Box-A2 n=4), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0348, Mann-Whitney one-sided (B2>A2) p=0.0286
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0555, Mann-Whitney one-sided (B2>A2) p=0.0286
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.

analysis/matlab/results/mergeB2_d0_10.txt

@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeB2_d0_10
+==============================================================================
+merge key: mergeB2   day window: 0..10   observations: 126
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            5         55          66.4      0.533       10
+Electrode-Box-A2            3         28          52.1      0.433       10
+Naive                       4         43          46.8      0.440       10
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    297.84    314.85    -142.92          285.84  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat       DF 
+    {'(Intercept)'           }           4.2369      0.08452      50.129    121
+    {'group_Electrode-Box-A2'}        -0.091837      0.13759    -0.66748    121
+    {'group_Naive'           }         -0.39133      0.12596     -3.1068    121
+    {'day_c'                 }           0.2019    0.0052735      38.287    121
+    {'day_c2'                }        -0.024069    0.0015714     -15.317    121
+
+
+    pValue        Lower       Upper    
+     8.553e-83      4.0696       4.4042
+       0.50574    -0.36423      0.18055
+     0.0023571    -0.64071     -0.14196
+     1.787e-69     0.19146      0.21235
+    4.1977e-30    -0.02718    -0.020958
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.18296 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             124
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    364.15    381.07    -176.07          352.15  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat      DF 
+    {'(Intercept)'           }          0.11026      0.11374    0.96938    119
+    {'group_Electrode-Box-A2'}         -0.26616      0.18448    -1.4427    119
+    {'group_Naive'           }         -0.53698      0.16912    -3.1751    119
+    {'day_c'                 }          0.21673    0.0069278     31.283    119
+    {'day_c2'                }        -0.013726    0.0021988    -6.2424    119
+
+
+    pValue        Lower       Upper    
+       0.33432    -0.11496      0.33548
+       0.15172    -0.63144      0.09913
+     0.0019073    -0.87186      -0.2021
+    2.9856e-59     0.20301      0.23044
+    6.8729e-09    -0.01808    -0.009372
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.24468 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    304.98    327.67    -144.49          288.98  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate      SE           tStat   
+    {'(Intercept)'                 }            4.2592     0.082682      51.513
+    {'group_Electrode-Box-A2'      }          -0.10296      0.13455    -0.76522
+    {'group_Naive'                 }          -0.46886       0.1243     -3.7722
+    {'day_c'                       }            0.1907    0.0067258      28.354
+    {'day_c2'                      }          -0.02454    0.0015791     -15.541
+    {'group_Electrode-Box-A2:day_c'}        -0.0023862     0.012062    -0.19783
+    {'group_Naive:day_c'           }          0.045036     0.010729      4.1975
+
+
+    DF     pValue        Lower        Upper    
+    119    3.2785e-83       4.0955        4.423
+    119       0.44566     -0.36937      0.16346
+    119    0.00025365     -0.71498     -0.22275
+    119     9.114e-55      0.17738      0.20402
+    119    2.0273e-30    -0.027667    -0.021413
+    119       0.84352     -0.02627     0.021498
+    119    5.2317e-05     0.023791     0.066281
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17816 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=9.74, Box-A2 mean=8.96
+  Welch two-sided p=0.517, Mann-Whitney p=0.571 (nB=5, nA=3)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0001, but with just
+ observation-level DF (df2=119) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.10x Box-A2  (one-sided p=0.2522)  -> not supported
+  [rate/level ] Box-B2 = 1.30x Box-A2  (one-sided p=0.0745)  -> not supported
+
+Per-animal (Box-B2 n=5 vs Box-A2 n=3), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0228, Mann-Whitney one-sided (B2>A2) p=0.0179
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0943, Mann-Whitney one-sided (B2>A2) p=0.0714
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.

analysis/matlab/results/mergeB2_full.txt

@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeB2_full
+==============================================================================
+merge key: mergeB2   day window: 0..26   observations: 185
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            5         78          78.4      0.596       22
+Electrode-Box-A2            3         31          55.4      0.448       13
+Naive                       4         76          59.6      0.492       26
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    565.84    585.16    -276.92          553.84  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE            tStat      DF 
+    {'(Intercept)'           }           4.4986      0.054066     83.206    180
+    {'group_Electrode-Box-A2'}        -0.096187       0.09052    -1.0626    180
+    {'group_Naive'           }         -0.34448      0.080981    -4.2538    180
+    {'day_c'                 }          0.10082     0.0023113     43.621    180
+    {'day_c2'                }         -0.00647    0.00024877    -26.008    180
+
+
+    pValue         Lower         Upper     
+    1.3263e-145        4.3919        4.6053
+        0.28939       -0.2748       0.08243
+     3.3739e-05      -0.50427      -0.18468
+     1.2306e-97      0.096261       0.10538
+     7.2052e-63    -0.0069609    -0.0059791
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.1166  
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             183
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    661.88    681.14    -324.94          649.88  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat      DF 
+    {'(Intercept)'           }           0.57975      0.098046      5.913    178
+    {'group_Electrode-Box-A2'}          -0.33262       0.16122    -2.0631    178
+    {'group_Naive'           }          -0.60174       0.14641    -4.1098    178
+    {'day_c'                 }           0.12534     0.0034238     36.607    178
+    {'day_c2'                }        -0.0069519    0.00035349    -19.666    178
+
+
+    pValue        Lower         Upper     
+    1.6792e-08       0.38627       0.77323
+      0.040554      -0.65077     -0.014466
+    6.0348e-05      -0.89067      -0.31281
+    9.0274e-85       0.11858       0.13209
+    1.6927e-46    -0.0076494    -0.0062543
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.21289 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    560.22    585.99    -272.11          544.22  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate     SE            tStat  
+    {'(Intercept)'                 }            4.516      0.055699     81.079
+    {'group_Electrode-Box-A2'      }         -0.11558      0.094376    -1.2247
+    {'group_Naive'                 }         -0.38342      0.083677    -4.5821
+    {'day_c'                       }         0.092386     0.0030001     30.794
+    {'day_c2'                      }        -0.006943    0.00027446    -25.297
+    {'group_Electrode-Box-A2:day_c'}        0.0036285     0.0081693    0.44417
+    {'group_Naive:day_c'           }         0.022361     0.0042611     5.2476
+
+
+    DF     pValue        Lower         Upper     
+    178    1.781e-142        4.4061        4.6259
+    178       0.22231      -0.30182      0.070658
+    178    8.6367e-06      -0.54854      -0.21829
+    178    3.1867e-73      0.086466      0.098307
+    178    7.6668e-61    -0.0074846    -0.0064014
+    178       0.65746     -0.012493       0.01975
+    178    4.3478e-07      0.013952       0.03077
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.12017 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=6.98, Box-A2 mean=8.19
+  Welch two-sided p=0.508, Mann-Whitney p=0.786 (nB=5, nA=3)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=178) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.10x Box-A2  (one-sided p=0.1440)  -> not supported
+  [rate/level ] Box-B2 = 1.39x Box-A2  (one-sided p=0.0196)  -> SUPPORTED
+
+Per-animal (Box-B2 n=5 vs Box-A2 n=3), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0204, Mann-Whitney one-sided (B2>A2) p=0.0179
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0440, Mann-Whitney one-sided (B2>A2) p=0.0179
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.

analysis/matlab/results/mergeNaive_full.txt

@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeNaive_full
+==============================================================================
+merge key: mergeNaive   day window: 0..26   observations: 185
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            4         63          80.2      0.605       22
+Electrode-Box-A             1         15          70.7      0.557       14
+Electrode-Box-A2            7        107          58.4      0.479       26
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    569.28    588.6    -278.64          557.28  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat   
+    {'(Intercept)'           }            4.5235      0.074032      61.102
+    {'group_Electrode-Box-A' }          -0.12346       0.16529    -0.74694
+    {'group_Electrode-Box-A2'}          -0.26481      0.093039     -2.8462
+    {'day_c'                 }           0.10065     0.0023081      43.608
+    {'day_c2'                }        -0.0064638    0.00024915     -25.944
+
+
+    DF     pValue         Lower         Upper     
+    180    2.6809e-122        4.3774        4.6696
+    180        0.45607      -0.44962        0.2027
+    180      0.0049387      -0.44839     -0.081217
+    180     1.2924e-97      0.096096       0.10521
+    180     1.0244e-62    -0.0069555    -0.0059722
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.14464 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             183
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    663.16    682.41    -325.58          651.16  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat   
+    {'(Intercept)'           }           0.62291       0.11692      5.3275
+    {'group_Electrode-Box-A' }          -0.21631       0.26048    -0.83042
+    {'group_Electrode-Box-A2'}           -0.5314       0.14632     -3.6318
+    {'day_c'                 }           0.12505      0.003415      36.618
+    {'day_c2'                }        -0.0069489    0.00035361     -19.651
+
+
+    DF     pValue        Lower         Upper     
+    178     2.981e-07       0.39218       0.85365
+    178       0.40742      -0.73034       0.29772
+    178    0.00036793      -0.82014      -0.24265
+    178    8.6013e-85       0.11831       0.13179
+    178    1.8602e-46    -0.0076467    -0.0062511
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.22797 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    561.97    587.73    -272.98          545.97  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate      SE            tStat   
+    {'(Intercept)'                 }            4.5372      0.083001      54.664
+    {'group_Electrode-Box-A'       }          -0.13394       0.18535    -0.72264
+    {'group_Electrode-Box-A2'      }          -0.27771       0.10424     -2.6641
+    {'day_c'                       }          0.090249     0.0032246      27.988
+    {'day_c2'                      }        -0.0066704    0.00025769     -25.885
+    {'group_Electrode-Box-A:day_c' }          0.010369     0.0086089      1.2044
+    {'group_Electrode-Box-A2:day_c'}          0.020125     0.0041158      4.8897
+
+
+    DF     pValue         Lower         Upper     
+    178    3.3741e-113        4.3734         4.701
+    178        0.47085       -0.4997       0.23182
+    178      0.0084289      -0.48341     -0.071999
+    178     4.2889e-67      0.083886      0.096613
+    178     3.0498e-62    -0.0071789    -0.0061619
+    178        0.23002    -0.0066197      0.027358
+    178     2.2462e-06      0.012003      0.028247
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.16287 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=6.92, Box-A2 mean=6.25
+  Welch two-sided p=0.689, Mann-Whitney p=0.527 (nB=4, nA=7)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=178) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.30x Box-A2  (one-sided p=0.0022)  -> SUPPORTED
+  [rate/level ] Box-B2 = 1.70x Box-A2  (one-sided p=0.0001)  -> SUPPORTED
+
+Per-animal (Box-B2 n=4 vs Box-A2 n=7), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0158, Mann-Whitney one-sided (B2>A2) p=0.0030
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0207, Mann-Whitney one-sided (B2>A2) p=0.0030
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.

analysis/matlab/results/paper_mergeA2.txt

@@ -0,0 +1,77 @@
+==============================================================================
+PAPER LME REPLICATION -- mergeA2 (full range)
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+behavior = successful reaches (COUNT per session)
+N = 8 rats, 109 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..22, control(A2) 0..14
+** WARNING: unequal day coverage -- the full-range stim:day interaction
+   extrapolates the control group's line and is CONFOUNDED here (the paper's
+   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             109
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    962.68    978.83    -475.34          950.68  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF     pValue    
+    {'(Intercept)'}         20.701      4.9127     4.2138    105    5.3256e-05
+    {'day'        }         6.8599     0.69872     9.8178    105    1.5726e-16
+    {'stim'       }         22.092      6.4372     3.4319    105    0.00085848
+    {'day:stim'   }        -2.1793     0.81821    -2.6635    105     0.0089525
+
+
+    Lower      Upper   
+      10.96      30.442
+     5.4745      8.2453
+     9.3279      34.855
+    -3.8016    -0.55692
+
+Random effects covariance parameters (95% CIs):
+Group: rat (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1043e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        18.954      16.597    21.644
+
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(105)= -2.66 F(1)=  7.094  p=0.008953    p=0.008906 (df=109)
+day (learning)             t(105)=  9.82 F(1)= 96.388  p=1.573e-16    p=1.113e-16 (df=109)
+stim (main, Day 1)         t(105)=  3.43 F(1)= 11.778  p=0.0008585    p=0.000848 (df=109)
+interaction 95% CI: [-3.80, -0.56]
+
+HONEST LME -- per-rat random slope (day|rat): interaction F(1,9.7)=0.11, p=0.746
+  Satterthwaite DF on the random-INTERCEPT model above stays ~= residual (the
+  slope's error is at session level), so it does NOT fix pseudoreplication. A per-animal
+  random slope collapses the interaction DF toward the animal count -- the honest test.
+
+INTERPRETATION
+  - stim x day interaction: SIGNIFICANT (p=0.0090, slope diff=-2.18) -> tDCS (Box-B2) improves SLOWER -- groups converge.
+  - stim main effect on Day 1 (our day 0): SIGNIFICANT (p=0.0009) -> groups already DIFFER on Day 1.
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.

analysis/matlab/results/paper_mergeB2.txt

@@ -0,0 +1,77 @@
+==============================================================================
+PAPER LME REPLICATION -- mergeB2 (full range)
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+behavior = successful reaches (COUNT per session)
+N = 8 rats, 109 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..22, control(A2) 0..13
+** WARNING: unequal day coverage -- the full-range stim:day interaction
+   extrapolates the control group's line and is CONFOUNDED here (the paper's
+   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             109
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    967.24    983.39    -477.62          955.24  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF     pValue    
+    {'(Intercept)'}           21.7     5.9981     3.6178    105    0.00045861
+    {'day'        }         6.4863     0.9412     6.8916    105    4.2336e-10
+    {'stim'       }         17.493     7.1288     2.4538    105      0.015779
+    {'day:stim'   }        -1.4651      1.025    -1.4294    105       0.15586
+
+
+    Lower      Upper  
+     9.8069     33.593
+     4.6201     8.3526
+     3.3578     31.628
+    -3.4974    0.56726
+
+Random effects covariance parameters (95% CIs):
+Group: rat (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1487e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        19.354      16.948    22.101
+
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(105)= -1.43 F(1)=  2.043  p=0.1559    p=0.1558 (df=109)
+day (learning)             t(105)=  6.89 F(1)= 47.494  p=4.234e-10    p=3.734e-10 (df=109)
+stim (main, Day 1)         t(105)=  2.45 F(1)=  6.021  p=0.01578    p=0.01572 (df=109)
+interaction 95% CI: [-3.50, +0.57]
+
+HONEST LME -- per-rat random slope (day|rat): interaction F(1,11.8)=0.02, p=0.9027
+  Satterthwaite DF on the random-INTERCEPT model above stays ~= residual (the
+  slope's error is at session level), so it does NOT fix pseudoreplication. A per-animal
+  random slope collapses the interaction DF toward the animal count -- the honest test.
+
+INTERPRETATION
+  - stim x day interaction: n.s. (p=0.1559, slope diff=-1.47) -> slopes parallel -- no differential learning rate over this window.
+  - stim main effect on Day 1 (our day 0): SIGNIFICANT (p=0.0158) -> groups already DIFFER on Day 1.
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.

analysis/matlab/results/paper_mergeNaive.txt

@@ -0,0 +1,77 @@
+==============================================================================
+PAPER LME REPLICATION -- mergeNaive (full range)
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+behavior = successful reaches (COUNT per session)
+N = 11 rats, 170 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..22, control(A2) 0..26
+** WARNING: unequal day coverage -- the full-range stim:day interaction
+   extrapolates the control group's line and is CONFOUNDED here (the paper's
+   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             170
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    1526.4    1545.2    -757.2           1514.4  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue    
+    {'(Intercept)'}        30.553       4.7932    6.3742    166    1.7469e-09
+    {'day'        }        3.5345      0.33399    10.583    166    2.5084e-20
+    {'stim'       }        10.155       8.0021    1.2691    166        0.2062
+    {'day:stim'   }        1.5835      0.58611    2.7018    166     0.0076138
+
+
+    Lower      Upper 
+     21.089    40.016
+     2.8751    4.1939
+    -5.6439    25.954
+    0.42633    2.7407
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.2449  
+
+
+    Lower    Upper 
+    5.388    15.863
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        19.866      17.802    22.168
+
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(166)=  2.70 F(1)=  7.299  p=0.007614    p=0.007598 (df=170)
+day (learning)             t(166)= 10.58 F(1)=111.990  p=2.508e-20    p=2.011e-20 (df=170)
+stim (main, Day 1)         t(166)=  1.27 F(1)=  1.611  p=0.2062    p=0.217 (df=23)
+interaction 95% CI: [+0.43, +2.74]
+
+HONEST LME -- per-rat random slope (day|rat): interaction F(1,11.4)=0.92, p=0.3565
+  Satterthwaite DF on the random-INTERCEPT model above stays ~= residual (the
+  slope's error is at session level), so it does NOT fix pseudoreplication. A per-animal
+  random slope collapses the interaction DF toward the animal count -- the honest test.
+
+INTERPRETATION
+  - stim x day interaction: SIGNIFICANT (p=0.0076, slope diff=+1.58) -> tDCS (Box-B2) improves FASTER -- benefit accumulates over training.
+  - stim main effect on Day 1 (our day 0): n.s. (p=0.2062) -> groups are comparable on Day 1.
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.

analysis/matlab/results/paper_mergeNaive_d0_10.txt

@@ -0,0 +1,76 @@
+==============================================================================
+PAPER LME REPLICATION -- mergeNaive (days 0-10)
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+behavior = successful reaches (COUNT per session)
+N = 11 rats, 115 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..10, control(A2) 0..10
+(Equal day coverage -- the stim:day interaction over this window is NOT
+ confounded by the full-range coverage imbalance.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             115
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    952.54    969.01    -470.27          940.54  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue    
+    {'(Intercept)'}         10.33       4.3573    2.3706    111      0.019482
+    {'day'        }        8.0907      0.51948    15.575    111    1.0806e-29
+    {'stim'       }        9.4432       7.1538      1.32    111       0.18954
+    {'day:stim'   }        1.6184       0.8216    1.9699    111      0.051345
+
+
+    Lower         Upper 
+        1.6953    18.964
+        7.0613      9.12
+       -4.7326    23.619
+    -0.0096134    3.2465
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        8.4877  
+
+
+    Lower     Upper 
+    5.0001    14.408
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        13.352      11.652    15.299
+
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(111)=  1.97 F(1)=  3.880  p=0.05134    p=0.05149 (df=105)
+day (learning)             t(111)= 15.57 F(1)=242.567  p=1.081e-29    p=3.188e-29 (df=107)
+stim (main, Day 1)         t(111)=  1.32 F(1)=  1.742  p=0.1895    p=0.2003 (df=22)
+interaction 95% CI: [-0.01, +3.25]
+
+HONEST LME -- per-rat random slope (day|rat): interaction F(1,10.1)=2.00, p=0.1875
+  Satterthwaite DF on the random-INTERCEPT model above stays ~= residual (the
+  slope's error is at session level), so it does NOT fix pseudoreplication. A per-animal
+  random slope collapses the interaction DF toward the animal count -- the honest test.
+
+INTERPRETATION
+  - stim x day interaction: n.s. (p=0.0513, slope diff=+1.62) -> slopes parallel -- no differential learning rate over this window.
+  - stim main effect on Day 1 (our day 0): n.s. (p=0.1895) -> groups are comparable on Day 1.
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.

analysis/matlab/results/paper_mergeNaive_d0_13.txt

@@ -0,0 +1,76 @@
+==============================================================================
+PAPER LME REPLICATION -- mergeNaive (days 0-13)
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+behavior = successful reaches (COUNT per session)
+N = 11 rats, 138 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..13, control(A2) 0..13
+(Equal day coverage -- the stim:day interaction over this window is NOT
+ confounded by the full-range coverage imbalance.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             138
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    1164.9    1182.4    -576.44          1152.9  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue    
+    {'(Intercept)'}        16.254       4.2865     3.792    134    0.00022519
+    {'day'        }        6.4041      0.42677    15.006    134    1.7662e-30
+    {'stim'       }        8.9845       7.0391    1.2764    134       0.20403
+    {'day:stim'   }        1.7992      0.67672    2.6587    134      0.008801
+
+
+    Lower      Upper 
+     7.7764    24.732
+     5.5601    7.2482
+    -4.9377    22.907
+    0.46073    3.1376
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        8.1481  
+
+
+    Lower     Upper 
+    4.7633    13.938
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        14.825      13.109    16.766
+
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(134)=  2.66 F(1)=  7.068  p=0.008801    p=0.008831 (df=130)
+day (learning)             t(134)= 15.01 F(1)=225.182  p=1.766e-30    p=2.418e-30 (df=132)
+stim (main, Day 1)         t(134)=  1.28 F(1)=  1.629  p=0.204    p=0.2142 (df=24)
+interaction 95% CI: [+0.46, +3.14]
+
+HONEST LME -- per-rat random slope (day|rat): interaction F(1,54.8)=6.46, p=0.01387
+  Satterthwaite DF on the random-INTERCEPT model above stays ~= residual (the
+  slope's error is at session level), so it does NOT fix pseudoreplication. A per-animal
+  random slope collapses the interaction DF toward the animal count -- the honest test.
+
+INTERPRETATION
+  - stim x day interaction: SIGNIFICANT (p=0.0088, slope diff=+1.80) -> tDCS (Box-B2) improves FASTER -- benefit accumulates over training.
+  - stim main effect on Day 1 (our day 0): n.s. (p=0.2040) -> groups are comparable on Day 1.
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.

analysis/matlab/results/paper_unmerged.txt

@@ -0,0 +1,76 @@
+==============================================================================
+PAPER LME REPLICATION -- unmerged (full range)
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+behavior = successful reaches (COUNT per session)
+N = 6 rats, 71 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..14, control(A2) 0..13
+(Equal day coverage -- the stim:day interaction over this window is NOT
+ confounded by the full-range coverage imbalance.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              71
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    596.33    609.9    -292.16          584.33  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}          21.53      5.5382    3.8874    67    0.00023506
+    {'day'        }         6.6587     0.72367    9.2014    67    1.6706e-13
+    {'stim'       }         8.0204      7.6977    1.0419    67        0.3012
+    {'day:stim'   }        0.93747      0.9187    1.0204    67        0.3112
+
+
+    Lower       Upper 
+      10.475    32.584
+      5.2143    8.1032
+     -7.3444    23.385
+    -0.89627    2.7712
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7931  
+
+
+    Lower     Upper 
+    2.4723    13.574
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        14.163      11.93    16.813
+
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(67)=  1.02 F(1)=  1.041  p=0.3112    p=0.3111 (df=70)
+day (learning)             t(67)=  9.20 F(1)= 84.665  p=1.671e-13    p=1.04e-13 (df=71)
+stim (main, Day 1)         t(67)=  1.04 F(1)=  1.086  p=0.3012    p=0.3116 (df=18)
+interaction 95% CI: [-0.90, +2.77]
+
+HONEST LME -- per-rat random slope (day|rat): interaction F(1,8.8)=0.61, p=0.456
+  Satterthwaite DF on the random-INTERCEPT model above stays ~= residual (the
+  slope's error is at session level), so it does NOT fix pseudoreplication. A per-animal
+  random slope collapses the interaction DF toward the animal count -- the honest test.
+
+INTERPRETATION
+  - stim x day interaction: n.s. (p=0.3112, slope diff=+0.94) -> slopes parallel -- no differential learning rate over this window.
+  - stim main effect on Day 1 (our day 0): n.s. (p=0.3012) -> groups are comparable on Day 1.
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.

analysis/matlab/results/phase_mergeA2.txt

@@ -0,0 +1,157 @@
+==============================================================================
+PHASED days x tDCS LME -- mergeA2 (Box-B2 vs Box-A2)
+==============================================================================
+model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
+
+phase    N A2slp B2slp  day p    tDCS p   intP(res) intP(Satt) intP(RS)  slopeDiff [95% CI]
+--------------------------------------------------------------------------------------------------------
+0-5     8 10.54 15.16  1.5e-10  0.693    0.014     0.014      0.089     +4.62 [+1.00,+8.24]
+6-10    7  3.17  4.83  0.035    0.127    0.390     0.390      0.466     +1.66 [-2.22,+5.53]
+6-13    7  2.92  4.47  0.00032  0.125    0.118     0.118      0.303     +1.55 [-0.41,+3.52]
+
+Columns: intP(res)=observation-level DF (anticonservative); intP(Satt)=Satterthwaite
+DF (~= residual on this random-intercept model); intP(RS)=per-animal random-slope
+(dayp|subject) model, the honest test (DF collapses toward the animal count; 'n/a' if it
+did not converge). The early phase carries the Box-B2 faster-acquisition signal; late
+phases converge.
+==============================================================================
+FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              48
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    381.01    392.23    -184.5           369.01  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF    pValue    
+    {'(Intercept)'}          10.06      4.465      2.253    44      0.029296
+    {'dayp'       }         10.543       1.27     8.3016    44    1.4943e-10
+    {'tDCS'       }        -2.5119     6.3144    -0.3978    44        0.6927
+    {'dayp:tDCS'  }         4.6214      1.796     2.5731    44      0.013526
+
+
+    Lower      Upper 
+      1.061    19.058
+     7.9834    13.102
+    -15.238    10.214
+     1.0018    8.2411
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        4.5394  
+
+
+    Lower     Upper 
+    1.7406    11.838
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.625      8.5345    13.229
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              35
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    265.24    274.57    -126.62          253.24  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF    pValue    
+    {'(Intercept)'}        75.867      4.9313     15.385    31    4.6291e-16
+    {'dayp'       }        3.1667      1.4366     2.2043    31      0.035054
+    {'tDCS'       }        10.233      6.5234     1.5687    31       0.12687
+    {'dayp:tDCS'  }        1.6583      1.9004    0.87262    31       0.38958
+
+
+    Lower      Upper 
+     65.809    85.924
+    0.23676    6.0966
+    -3.0713    23.538
+    -2.2176    5.5342
+
+Random effects covariance parameters (95% CIs):
+Group: subject (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.9837  
+
+
+    Lower     Upper
+    2.9396    12.18
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.8684      6.0554    10.224
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              50
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    367.22    378.69    -177.61          355.22  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}          76.4       4.9765    15.352    46    1.0096e-19
+    {'dayp'       }        2.9189      0.74973    3.8932    46    0.00031772
+    {'tDCS'       }        10.289       6.5773    1.5643    46       0.12459
+    {'dayp:tDCS'  }         1.554      0.97562    1.5928    46       0.11805
+
+
+    Lower       Upper 
+      66.382    86.417
+      1.4097     4.428
+     -2.9502    23.528
+    -0.40985    3.5178
+
+Random effects covariance parameters (95% CIs):
+Group: subject (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        7.1357  
+
+
+    Lower     Upper 
+    3.9247    12.974
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.3202      5.9279    9.0395
+
+

analysis/matlab/results/phase_mergeB2.txt

@@ -0,0 +1,157 @@
+==============================================================================
+PHASED days x tDCS LME -- mergeB2 (Box-B2 vs Box-A2)
+==============================================================================
+model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
+
+phase    N A2slp B2slp  day p    tDCS p   intP(res) intP(Satt) intP(RS)  slopeDiff [95% CI]
+--------------------------------------------------------------------------------------------------------
+0-5     8  9.86 14.65  2.9e-08  0.399    0.013     0.013      0.088     +4.79 [+1.06,+8.53]
+6-10    7  2.40  4.80  0.18     0.142    0.253     0.254      0.328     +2.40 [-1.80,+6.60]
+6-13    7  1.71  4.42  0.083    0.127    0.018     0.018      0.024     +2.71 [+0.50,+4.93]
+
+Columns: intP(res)=observation-level DF (anticonservative); intP(Satt)=Satterthwaite
+DF (~= residual on this random-intercept model); intP(RS)=per-animal random-slope
+(dayp|subject) model, the honest test (DF collapses toward the animal count; 'n/a' if it
+did not converge). The early phase carries the Box-B2 faster-acquisition signal; late
+phases converge.
+==============================================================================
+FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              48
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    383.06    394.29    -185.53          371.06  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}         12.524     5.5254      2.2666    44      0.028385
+    {'dayp'       }         9.8571     1.4654      6.7268    44    2.8783e-08
+    {'tDCS'       }        -5.9524     6.9892    -0.85166    44       0.39902
+    {'dayp:tDCS'  }         4.7943     1.8536      2.5865    44      0.013079
+
+
+    Lower      Upper 
+     1.3881     23.66
+     6.9039     12.81
+    -20.038    8.1334
+     1.0587    8.5299
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7044  
+
+
+    Lower     Upper 
+    2.6066    12.484
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.618      8.5282    13.219
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              35
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    264.22    273.56    -126.11          252.22  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}         74.2       5.8956    12.586    31    1.0133e-13
+    {'dayp'       }          2.4       1.7415    1.3781    31       0.17803
+    {'tDCS'       }        10.52       6.9757    1.5081    31       0.14166
+    {'dayp:tDCS'  }          2.4       2.0606    1.1647    31       0.25302
+
+
+    Lower      Upper 
+     62.176    86.224
+    -1.1518    5.9518
+    -3.7071    24.747
+    -1.8026    6.6026
+
+Random effects covariance parameters (95% CIs):
+Group: subject (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7551  
+
+
+    Lower     Upper 
+    2.7955    11.848
+
+Group: Error
+    Name               Estimate    Lower     Upper
+    {'Res Std'}        7.7883      5.9937    10.12
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              50
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    362.57    374.04    -175.28          350.57  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}        75.168       5.6188    13.378    46    1.8212e-17
+    {'dayp'       }         1.712      0.96716    1.7701    46      0.083338
+    {'tDCS'       }        10.291       6.6299    1.5522    46       0.12747
+    {'dayp:tDCS'  }        2.7116       1.1009    2.4631    46      0.017575
+
+
+    Lower       Upper 
+      63.858    86.478
+    -0.23482    3.6588
+     -3.0544    23.636
+     0.49565    4.9275
+
+Random effects covariance parameters (95% CIs):
+Group: subject (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        6.379   
+
+
+    Lower     Upper 
+    3.4793    11.695
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.0508      5.7111    8.7047
+
+

analysis/matlab/results/phase_mergeNaive.txt

@@ -0,0 +1,157 @@
+==============================================================================
+PHASED days x tDCS LME -- mergeNaive (Box-B2 vs Box-A2)
+==============================================================================
+model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
+
+phase    N A2slp B2slp  day p    tDCS p   intP(res) intP(Satt) intP(RS)  slopeDiff [95% CI]
+--------------------------------------------------------------------------------------------------------
+0-5     11  8.26 15.16  1.6e-10  0.852    0.000     0.000      0.013     +6.90 [+3.40,+10.41]
+6-10    10  5.97  4.83  3.9e-05  0.007    0.584     0.585      0.643     -1.14 [-5.31,+3.03]
+6-13    10  2.83  4.47  0.00071  0.012    0.196     0.196      0.333     +1.65 [-0.87,+4.17]
+
+Columns: intP(res)=observation-level DF (anticonservative); intP(Satt)=Satterthwaite
+DF (~= residual on this random-intercept model); intP(RS)=per-animal random-slope
+(dayp|subject) model, the honest test (DF collapses toward the animal count; 'n/a' if it
+did not converge). The early phase carries the Box-B2 faster-acquisition signal; late
+phases converge.
+==============================================================================
+FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              65
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    532.27    545.32    -260.14          520.27  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}          9.046     4.8672    1.8586    61      0.067913
+    {'dayp'       }         8.2602     1.0773    7.6677    61    1.6422e-10
+    {'tDCS'       }        -1.4984     7.9706    -0.188    61        0.8515
+    {'dayp:tDCS'  }         6.9041     1.7525    3.9397    61    0.00021248
+
+
+    Lower       Upper 
+    -0.68649    18.779
+       6.106    10.414
+     -17.437     14.44
+      3.3998    10.408
+
+Random effects covariance parameters (95% CIs):
+Group: subject (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.4501  
+
+
+    Lower     Upper 
+    5.5672    16.041
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        11.565      9.5751    13.968
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              50
+    Fixed effects coefficients           4
+    Random effects coefficients         10
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    404.12    415.59    -196.06          392.12  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}           62.1      5.345      11.618    46    2.7953e-15
+    {'dayp'       }         5.9667     1.3111      4.5509    46    3.8983e-05
+    {'tDCS'       }             24     8.4512      2.8398    46     0.0067003
+    {'dayp:tDCS'  }        -1.1417      2.073    -0.55073    46       0.58449
+
+
+    Lower      Upper 
+     51.341    72.859
+     3.3276    8.6058
+     6.9887    41.011
+    -5.3144    3.0311
+
+Random effects covariance parameters (95% CIs):
+Group: subject (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        10.466  
+
+
+    Lower    Upper 
+    6.207    17.646
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.156      8.1572    12.644
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              73
+    Fixed effects coefficients           4
+    Random effects coefficients         10
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    593.06    606.81    -290.53          581.06  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}        66.814       4.8749    13.706    69    2.2545e-21
+    {'dayp'       }        2.8256      0.79686    3.5459    69    0.00070793
+    {'tDCS'       }        19.873       7.7041    2.5796    69      0.012026
+    {'dayp:tDCS'  }         1.648       1.2621    1.3058    69       0.19595
+
+
+    Lower       Upper 
+      57.089    76.539
+      1.2359    4.4153
+       4.504    35.242
+    -0.86972    4.1658
+
+Random effects covariance parameters (95% CIs):
+Group: subject (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.246   
+
+
+    Lower     Upper 
+    5.4692    15.631
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        11.498      9.661    13.686
+
+

analysis/matlab/results/phase_unmerged.txt

@@ -0,0 +1,157 @@
+==============================================================================
+PHASED days x tDCS LME -- unmerged (Box-B2 vs Box-A2)
+==============================================================================
+model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
+
+phase    N A2slp B2slp  day p    tDCS p   intP(res) intP(Satt) intP(RS)  slopeDiff [95% CI]
+--------------------------------------------------------------------------------------------------------
+0-5     6  9.86 15.22  1.6e-07  0.689    0.015     0.015      0.133     +5.36 [+1.11,+9.62]
+6-10    5  2.40  4.17  0.23     0.098    0.486     0.486      0.555     +1.77 [-3.41,+6.95]
+6-13    5  1.71  3.98  0.12     0.101    0.097     0.097      0.138     +2.27 [-0.44,+4.97]
+
+Columns: intP(res)=observation-level DF (anticonservative); intP(Satt)=Satterthwaite
+DF (~= residual on this random-intercept model); intP(RS)=per-animal random-slope
+(dayp|subject) model, the honest test (DF collapses toward the animal count; 'n/a' if it
+did not converge). The early phase carries the Box-B2 faster-acquisition signal; late
+phases converge.
+==============================================================================
+FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              36
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    289.66    299.16    -138.83          277.66  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}         12.524     5.2775      2.3731    32      0.023816
+    {'dayp'       }         9.8571     1.4772      6.6729    32    1.5706e-07
+    {'tDCS'       }        -3.0159     7.4635    -0.40408    32       0.68884
+    {'dayp:tDCS'  }         5.3619      2.089      2.5667    32      0.015149
+
+
+    Lower      Upper 
+     1.7739    23.274
+     6.8482    12.866
+    -18.219    12.187
+     1.1067    9.6172
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        4.8527  
+
+
+    Lower     Upper 
+    1.7069    13.796
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.703      8.3104    13.785
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              25
+    Fixed effects coefficients           4
+    Random effects coefficients          5
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    196.97    204.29    -92.486          184.97  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF    pValue    
+    {'(Intercept)'}          74.2      6.4017     11.591    21    1.3774e-10
+    {'dayp'       }           2.4      1.9295     1.2439    21       0.22726
+    {'tDCS'       }        14.333      8.2646     1.7343    21      0.097522
+    {'dayp:tDCS'  }        1.7667       2.491    0.70923    21       0.48598
+
+
+    Lower      Upper 
+     60.887    87.513
+    -1.6126    6.4126
+    -2.8539    31.521
+    -3.4136    6.9469
+
+Random effects covariance parameters (95% CIs):
+Group: subject (5 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        6.1065  
+
+
+    Lower     Upper 
+    2.5428    14.665
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        8.6289      6.3295    11.764
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              34
+    Fixed effects coefficients           4
+    Random effects coefficients          5
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    257.04    266.2    -122.52          245.04  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}         75.17      6.2311    12.064    30    4.8879e-13
+    {'dayp'       }        1.7101       1.061    1.6117    30       0.11749
+    {'tDCS'       }        13.563      8.0252      1.69    30        0.1014
+    {'dayp:tDCS'  }         2.267      1.3237    1.7126    30      0.097101
+
+
+    Lower       Upper 
+      62.445    87.896
+    -0.45682     3.877
+     -2.8271    29.952
+    -0.43635    4.9703
+
+Random effects covariance parameters (95% CIs):
+Group: subject (5 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        7.1166  
+
+
+    Lower     Upper 
+    3.4834    14.539
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.7328      5.9847    9.9914
+
+

analysis/matlab/results/unmerged_d0_10.txt

@@ -0,0 +1,232 @@
+==============================================================================
+tDCS GLM report -- scenario: unmerged_d0_10
+==============================================================================
+merge key: unmerged   day window: 0..10   observations: 126
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            3         33          70.0      0.555       10
+Electrode-Box-A             1         11          58.9      0.499       10
+Electrode-Box-A2            3         28          52.1      0.433       10
+Naive                       4         43          46.8      0.440       10
+Right-Electrode             1         11          63.4      0.499       10
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    301.01    323.7    -142.51          285.01  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat       DF 
+    {'(Intercept)'           }           4.2892      0.10522      40.765    119
+    {'group_Electrode-Box-A' }         -0.16759      0.20935    -0.80052    119
+    {'group_Electrode-Box-A2'}         -0.14442      0.14887    -0.97013    119
+    {'group_Naive'           }         -0.44336      0.13882     -3.1938    119
+    {'group_Right-Electrode' }        -0.094696      0.20909    -0.45289    119
+    {'day_c'                 }          0.20188    0.0052727      38.287    119
+    {'day_c2'                }        -0.024069    0.0015714     -15.317    119
+
+
+    pValue        Lower       Upper    
+    9.1655e-72      4.0809       4.4976
+         0.425    -0.58213      0.24695
+       0.33395    -0.43919      0.15035
+     0.0017973    -0.71824     -0.16848
+       0.65145    -0.50872      0.31933
+    9.4346e-69     0.19144      0.21232
+    6.4425e-30    -0.02718    -0.020957
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17716 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             124
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    366.74    389.3    -175.37          350.74  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat       DF 
+    {'(Intercept)'           }          0.21772      0.13736       1.585    117
+    {'group_Electrode-Box-A' }         -0.27791       0.2726     -1.0195    117
+    {'group_Electrode-Box-A2'}         -0.37423      0.19356     -1.9334    117
+    {'group_Naive'           }         -0.64393      0.18075     -3.5625    117
+    {'group_Right-Electrode' }         -0.25889      0.27218    -0.95118    117
+    {'day_c'                 }          0.21668    0.0069264      31.283    117
+    {'day_c2'                }        -0.013737    0.0021988     -6.2475    117
+
+
+    pValue        Lower        Upper     
+       0.11567    -0.054325       0.48976
+       0.31008     -0.81779       0.26196
+        0.0556     -0.75756     0.0091015
+     0.0005325      -1.0019      -0.28596
+       0.34348     -0.79792       0.28014
+    1.1453e-58      0.20296        0.2304
+    6.9755e-09    -0.018092    -0.0093824
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.22892 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients          11
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    305.19    339.23    -140.6           281.19  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate     SE           tStat   
+    {'(Intercept)'                 }           4.3338      0.10278      42.165
+    {'group_Electrode-Box-A'       }         -0.23805      0.20569     -1.1574
+    {'group_Electrode-Box-A2'      }         -0.17701      0.14533      -1.218
+    {'group_Naive'                 }         -0.54258      0.13655     -3.9736
+    {'group_Right-Electrode'       }         -0.15185      0.20519    -0.74005
+    {'day_c'                       }          0.17638    0.0080904      21.802
+    {'day_c2'                      }        -0.024639    0.0015803     -15.591
+    {'group_Electrode-Box-A:day_c' }         0.044079     0.016893      2.6093
+    {'group_Electrode-Box-A2:day_c'}         0.011922     0.012896     0.92443
+    {'group_Naive:day_c'           }         0.059567     0.011689      5.0961
+    {'group_Right-Electrode:day_c' }         0.036369     0.016336      2.2263
+
+
+    DF     pValue        Lower        Upper    
+    115    8.6925e-72       4.1302       4.5374
+    115       0.24953     -0.64548      0.16937
+    115       0.22572     -0.46489      0.11086
+    115    0.00012391     -0.81305     -0.27211
+    115       0.46078      -0.5583      0.25459
+    115    1.1738e-42      0.16036      0.19241
+    115    3.8845e-30    -0.027769    -0.021509
+    115      0.010278     0.010617      0.07754
+    115        0.3572    -0.013623     0.037467
+    115    1.3744e-06     0.036414      0.08272
+    115      0.027945      0.00401     0.068729
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17214 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=9.52, Box-A2 mean=8.96
+  Welch two-sided p=0.643, Mann-Whitney p=0.700 (nB=3, nA=3)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=115) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.16x Box-A2  (one-sided p=0.1660)  -> not supported
+  [rate/level ] Box-B2 = 1.45x Box-A2  (one-sided p=0.0266)  -> SUPPORTED
+
+Per-animal (Box-B2 n=3 vs Box-A2 n=3), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0414, Mann-Whitney one-sided (B2>A2) p=0.0500
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0702, Mann-Whitney one-sided (B2>A2) p=0.0500
+
+Classification -- is each UNKNOWN condition A2-like or B2-like?
+(ratio >1 = above that anchor; p = differs from that anchor)
+
+  Electrode-Box-A:
+    [count/level] vs Box-A2: 0.98x p=0.912   vs Box-B2: 0.85x p=0.425
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+    [rate/level ] vs Box-A2: 1.10x p=0.725   vs Box-B2: 0.76x p=0.310
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+
+  Right-Electrode:
+    [count/level] vs Box-A2: 1.05x p=0.813   vs Box-B2: 0.91x p=0.651
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+    [rate/level ] vs Box-A2: 1.12x p=0.673   vs Box-B2: 0.77x p=0.343
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+
+  NOTE: each unknown has ONLY 1 subject. 'Matches' means 'not statistically
+  distinguishable', weak evidence at n=1, not proof of equivalence.
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.

analysis/matlab/results/unmerged_full.txt

@@ -0,0 +1,232 @@
+==============================================================================
+tDCS GLM report -- scenario: unmerged_full
+==============================================================================
+merge key: unmerged   day window: 0..26   observations: 185
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            3         40          77.0      0.591       14
+Electrode-Box-A             1         15          70.7      0.557       14
+Electrode-Box-A2            3         31          55.4      0.448       13
+Naive                       4         76          59.6      0.492       26
+Right-Electrode             1         23          85.8      0.627       22
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    567.38    593.14    -275.69          551.38  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat      DF 
+    {'(Intercept)'           }            4.5636      0.062818     72.648    178
+    {'group_Electrode-Box-A' }          -0.16366        0.1251    -1.3082    178
+    {'group_Electrode-Box-A2'}          -0.16301      0.090874    -1.7939    178
+    {'group_Naive'           }          -0.41017      0.083133    -4.9339    178
+    {'group_Right-Electrode' }          -0.15781       0.12382    -1.2745    178
+    {'day_c'                 }           0.10084     0.0023114     43.629    178
+    {'day_c2'                }        -0.0064669    0.00024857    -26.016    178
+
+
+    pValue         Lower         Upper     
+    3.0937e-134        4.4397        4.6876
+        0.19249      -0.41052      0.083214
+       0.074534      -0.34234      0.016314
+     1.8418e-06      -0.57422      -0.24612
+        0.20416      -0.40216      0.086543
+     5.5938e-97      0.096281        0.1054
+     1.4969e-62    -0.0069574    -0.0059764
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.10379 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             183
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    664.34    690.01    -324.17          648.34  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE           tStat       DF 
+    {'(Intercept)'           }           0.67246      0.11921      5.6411    176
+    {'group_Electrode-Box-A' }           -0.2656      0.23687     -1.1213    176
+    {'group_Electrode-Box-A2'}          -0.42592      0.16928     -2.5161    176
+    {'group_Naive'           }          -0.69483      0.15707     -4.4236    176
+    {'group_Right-Electrode' }          -0.19395      0.23547    -0.82369    176
+    {'day_c'                 }           0.12539    0.0034255      36.605    176
+    {'day_c2'                }        -0.0069542    0.0003534     -19.678    176
+
+
+    pValue        Lower         Upper     
+    6.6327e-08        0.4372       0.90772
+       0.26369      -0.73306       0.20187
+      0.012762      -0.75999     -0.091839
+    1.6969e-05       -1.0048      -0.38484
+       0.41123      -0.65866       0.27075
+    3.2383e-84       0.11863       0.13215
+    2.5332e-46    -0.0076517    -0.0062568
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.19922 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients          11
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    566.35    604.99    -271.17          542.35  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate      SE            tStat   
+    {'(Intercept)'                 }            4.5639      0.066952      68.167
+    {'group_Electrode-Box-A'       }           -0.1538       0.13339     -1.1531
+    {'group_Electrode-Box-A2'      }          -0.16353      0.097941     -1.6697
+    {'group_Naive'                 }           -0.4286      0.088752     -4.8292
+    {'group_Right-Electrode'       }          -0.10141       0.13294    -0.76285
+    {'day_c'                       }          0.084832     0.0050395      16.833
+    {'day_c2'                      }        -0.0071091    0.00030673     -23.177
+    {'group_Electrode-Box-A:day_c' }          0.015434     0.0094241      1.6377
+    {'group_Electrode-Box-A2:day_c'}          0.010314     0.0089464      1.1529
+    {'group_Naive:day_c'           }          0.031147     0.0065742      4.7377
+    {'group_Right-Electrode:day_c' }          0.011613      0.007034       1.651
+
+
+    DF     pValue         Lower         Upper     
+    174    1.9324e-127        4.4318        4.6961
+    174        0.25047      -0.41707       0.10946
+    174       0.096774      -0.35684      0.029772
+    174     2.9893e-06      -0.60377      -0.25343
+    174        0.44659       -0.3638       0.16097
+    174     2.3384e-38      0.074885      0.094778
+    174     4.4285e-55    -0.0077145    -0.0065037
+    174        0.10329    -0.0031665      0.034034
+    174        0.25055    -0.0073435      0.027971
+    174     4.4703e-06      0.018171      0.044122
+    174        0.10054    -0.0022698      0.025496
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.11116 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=7.96, Box-A2 mean=8.19
+  Welch two-sided p=0.896, Mann-Whitney p=1.000 (nB=3, nA=3)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=174) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.18x Box-A2  (one-sided p=0.0364)  -> SUPPORTED
+  [rate/level ] Box-B2 = 1.53x Box-A2  (one-sided p=0.0059)  -> SUPPORTED
+
+Per-animal (Box-B2 n=3 vs Box-A2 n=3), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0552, Mann-Whitney one-sided (B2>A2) p=0.0500
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0722, Mann-Whitney one-sided (B2>A2) p=0.0500
+
+Classification -- is each UNKNOWN condition A2-like or B2-like?
+(ratio >1 = above that anchor; p = differs from that anchor)
+
+  Electrode-Box-A:
+    [count/level] vs Box-A2: 1.00x p=0.996   vs Box-B2: 0.85x p=0.192
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+    [rate/level ] vs Box-A2: 1.17x p=0.501   vs Box-B2: 0.77x p=0.264
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+
+  Right-Electrode:
+    [count/level] vs Box-A2: 1.01x p=0.967   vs Box-B2: 0.85x p=0.204
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+    [rate/level ] vs Box-A2: 1.26x p=0.328   vs Box-B2: 0.82x p=0.411
+                 -> AMBIGUOUS (nearer Electrode-Box-B2)
+
+  NOTE: each unknown has ONLY 1 subject. 'Matches' means 'not statistically
+  distinguishable', weak evidence at n=1, not proof of equivalence.
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.

analysis/matlab/run_all.m

@@ -0,0 +1,22 @@
+%RUN_ALL Run all tDCS scenarios and write results/<scenario>.txt for each.
+%   Usage: matlab -batch "run_all"
+%   Covers the 6 GLMM scenarios and their 6 linear "days x tDCS" (lme_*) variants.
+
+scenarios = {'unmerged_full', 'unmerged_d0_10', ...
+             'mergeA2_full', 'mergeA2_d0_10', ...
+             'mergeB2_full', 'mergeB2_d0_10', ...
+             'lme_unmerged_full', 'lme_unmerged_d0_10', ...
+             'lme_mergeA2_full', 'lme_mergeA2_d0_10', ...
+             'lme_mergeB2_full', 'lme_mergeB2_d0_10', ...
+             'lme_unmerged_d0_13', 'lme_mergeA2_d0_13', 'lme_mergeB2_d0_13', ...
+             'phase_unmerged', 'phase_mergeA2', 'phase_mergeB2', ...
+             'paper_unmerged', 'paper_mergeA2', 'paper_mergeB2', ...
+             'mergeNaive_full', 'paper_mergeNaive', 'phase_mergeNaive', ...
+             'paper_mergeNaive_d0_10', 'paper_mergeNaive_d0_13'};
+
+for i = 1:numel(scenarios)
+    fprintf('\n\n### Running scenario: %s ###\n', scenarios{i});
+    tdcs_glm(scenarios{i});
+end
+
+fprintf('\n\nAll %d scenarios complete. See analysis/matlab/results/*.txt.\n', numel(scenarios));

analysis/matlab/run_export.m

@@ -0,0 +1,3 @@
+%RUN_EXPORT Export all data variations to CSV under analysis/matlab/export/.
+%   Usage: matlab -batch "run_export"
+tdcs_export_all();

analysis/matlab/run_power.m

@@ -0,0 +1,8 @@
+%RUN_POWER Monte-Carlo power analysis for the early-phase days x tDCS interaction.
+%   Usage: matlab -batch "run_power"
+%   Uses the fitted mergeA2 early-phase (0-5) LME as ground truth and reports
+%   power (cluster-honest per-animal test and the LME test) across a range of
+%   subjects-per-group, at the observed and half-observed interaction sizes.
+%   See tdcs_power_sim for parameters.
+
+tdcs_power_sim('mergeA2', [0 5]);

analysis/matlab/run_tests.m

@@ -0,0 +1,14 @@
+%RUN_TESTS Run the full matlab.unittest suite and exit nonzero on failure.
+%   Usage: matlab -batch "run_tests"
+%   (add the analysis/matlab directory to the path first if it is not
+%   already on it, e.g. matlab -batch "addpath('analysis/matlab'); run_tests")
+
+thisDir = fileparts(mfilename('fullpath'));
+results = runtests(fullfile(thisDir, 'tests'));
+disp(results);
+
+fprintf('PASS=%d FAIL=%d\n', sum([results.Passed]), sum([results.Failed]));
+
+if any([results.Failed])
+    error('run_tests:failed', 'One or more tests failed.');
+end

analysis/matlab/tdcs_config.m

@@ -0,0 +1,64 @@
+function cfg = tdcs_config()
+%TDCS_CONFIG Curated group map and merge maps for the tDCS reaching study.
+%   CFG = TDCS_CONFIG() returns a struct with:
+%     .groups     containers.Map, subject name (char) -> curated group name
+%                 (char), for the 12 curated animals only. All other
+%                 animals present in the raw export are not in this map
+%                 and must be dropped by callers.
+%     .ref        reference group used to code the GLME group factor,
+%                 'Electrode-Box-B2'.
+%     .mergeA2    containers.Map collapsing the two UNKNOWN conditions
+%                 toward the weaker anchor: Electrode-Box-A ->
+%                 Electrode-Box-A2, Right-Electrode -> Electrode-Box-B2.
+%     .mergeB2    containers.Map collapsing both UNKNOWN conditions onto
+%                 the stronger anchor: Electrode-Box-A -> Electrode-Box-B2,
+%                 Right-Electrode -> Electrode-Box-B2.
+%     .anchorLow  'Electrode-Box-A2', the weaker of the two known anchors.
+%     .anchorHigh 'Electrode-Box-B2', the stronger known anchor (== ref).
+%     .unknowns   cell array of the two ungrouped conditions that get
+%                 classified against the two anchors: {'Electrode-Box-A',
+%                 'Right-Electrode'}.
+%
+%   See docs/superpowers/plans/2026-07-20-matlab-tdcs-analysis.md (Global
+%   Constraints) for the source of these values.
+
+% Curated 12-animal group map (by animal name).
+subjectGroup = {
+    'Vu-vuong',      'Naive'
+    'Khoai-lang-2',  'Naive'
+    'Khoai-tay-2',   'Naive'
+    'OM-2',          'Naive'
+    'Khoai-tay-1',   'Electrode-Box-A'
+    'Banh-mi-2',     'Electrode-Box-A2'
+    'Egg-tart-2',    'Electrode-Box-A2'
+    'Root-beer-2',   'Electrode-Box-A2'
+    'Banh-mi-1',     'Electrode-Box-B2'
+    'Egg-tart-1',    'Electrode-Box-B2'
+    'Root-beer-1',   'Electrode-Box-B2'
+    'Khoai-lang-1',  'Right-Electrode'
+    };
+
+cfg = struct();
+cfg.groups = containers.Map(subjectGroup(:, 1), subjectGroup(:, 2));
+
+cfg.ref = 'Electrode-Box-B2';
+cfg.anchorLow = 'Electrode-Box-A2';
+cfg.anchorHigh = 'Electrode-Box-B2';
+cfg.unknowns = {'Electrode-Box-A', 'Right-Electrode'};
+
+cfg.mergeA2 = containers.Map( ...
+    {'Electrode-Box-A', 'Right-Electrode'}, ...
+    {'Electrode-Box-A2', 'Electrode-Box-B2'});
+
+cfg.mergeB2 = containers.Map( ...
+    {'Electrode-Box-A', 'Right-Electrode'}, ...
+    {'Electrode-Box-B2', 'Electrode-Box-B2'});
+
+% mergeNaive: pool Naive into the Box-A2 (control) side and Right-Electrode
+% into the Box-B2 (tDCS) side. Control = Box-A2 + Naive (n=7), tDCS = Box-B2 +
+% Right-Electrode (n=4); Electrode-Box-A is left unchanged.
+cfg.mergeNaive = containers.Map( ...
+    {'Naive', 'Right-Electrode'}, ...
+    {'Electrode-Box-A2', 'Electrode-Box-B2'});
+
+end

analysis/matlab/tdcs_export_all.m

@@ -0,0 +1,78 @@
+function files = tdcs_export_all(outRoot)
+%TDCS_EXPORT_ALL Export every data variation to CSV in an organized folder tree.
+%   FILES = TDCS_EXPORT_ALL() writes, under analysis/matlab/export/:
+%     curated_all.csv          the 12-animal curated long data (day >= 0)
+%     scenarios/<merge>_<win>.csv   each GLMM scenario table (all groups in that
+%                              merge/window): 3 merges x 3 windows = 9 files
+%     anchors/<merge>_B2vsA2.csv    the Box-B2(tDCS=1) vs Box-A2(tDCS=0) subset
+%                              (full range) with the tDCS factor: 3 files
+%     phases/<merge>_phase_<lo>-<hi>.csv   the anchor subset within each learning
+%                              phase (dayp = day - phaseStart): 3 x 3 = 9 files
+%   A MANIFEST.txt listing all files is also written. FILES is a cellstr of the
+%   absolute paths written.
+%
+%   TDCS_EXPORT_ALL(OUTROOT) writes under OUTROOT instead of the default.
+
+if nargin < 1 || isempty(outRoot)
+    outRoot = fullfile(fileparts(mfilename('fullpath')), 'export');
+end
+cfg = tdcs_config();
+merges  = {'unmerged', 'mergeA2', 'mergeB2'};
+windows = {'full', 'd0_10', 'd0_13'};
+phases  = {[0 5], [6 10], [6 13]};
+
+scDir = fullfile(outRoot, 'scenarios');
+anDir = fullfile(outRoot, 'anchors');
+phDir = fullfile(outRoot, 'phases');
+cellfun(@localMkdir, {outRoot, scDir, anDir, phDir});
+
+files = {};
+
+% 1. curated long data
+f = fullfile(outRoot, 'curated_all.csv');
+writetable(tdcs_load_data(), f); files{end+1} = f; %#ok<AGROW>
+
+% 2. scenario tables (merge x window)
+for mi = 1:numel(merges)
+    for wi = 1:numel(windows)
+        sc = [merges{mi} '_' windows{wi}];
+        S = tdcs_scenario_data(sc);
+        f = fullfile(scDir, [sc '.csv']);
+        writetable(S, f); files{end+1} = f; %#ok<AGROW>
+    end
+end
+
+% 3. anchor (Box-B2 vs Box-A2) subsets + tDCS factor, full range
+% 4. phase subsets of the same
+for mi = 1:numel(merges)
+    S = tdcs_scenario_data([merges{mi} '_full']);
+    A = S(ismember(S.group, {cfg.anchorLow, cfg.anchorHigh}), :);
+    A.tDCS = double(A.group == cfg.anchorHigh);   % Box-B2 = 1, Box-A2 = 0
+    f = fullfile(anDir, [merges{mi} '_B2vsA2.csv']);
+    writetable(A, f); files{end+1} = f; %#ok<AGROW>
+    for pi = 1:numel(phases)
+        p = phases{pi};
+        P = A(A.day >= p(1) & A.day <= p(2), :);
+        P.dayp = P.day - p(1);
+        f = fullfile(phDir, sprintf('%s_phase_%d-%d.csv', merges{mi}, p(1), p(2)));
+        writetable(P, f); files{end+1} = f; %#ok<AGROW>
+    end
+end
+
+% manifest
+mfid = fopen(fullfile(outRoot, 'MANIFEST.txt'), 'w');
+if mfid >= 0
+    cleanup = onCleanup(@() fclose(mfid)); %#ok<NASGU>
+    fprintf(mfid, 'tDCS data export -- %d files\n', numel(files));
+    for i = 1:numel(files)
+        fprintf(mfid, '  %s\n', strrep(files{i}, [outRoot filesep], ''));
+    end
+end
+files = files(:);
+fprintf('tdcs_export_all: wrote %d CSVs under %s\n', numel(files), outRoot);
+
+end
+
+function localMkdir(d)
+if ~exist(d, 'dir'); mkdir(d); end
+end

analysis/matlab/tdcs_glm.m

@@ -0,0 +1,157 @@
+function tdcs_glm(scenario)
+%TDCS_GLM Run one tDCS GLM scenario end-to-end and report it.
+%   TDCS_GLM(SCENARIO) runs the full pipeline for one named scenario --
+%   TDCS_SCENARIO_DATA -> TDCS_MODELS -> TDCS_REPORT -- printing the
+%   report to the console and writing it to
+%   analysis/matlab/results/<SCENARIO>.txt.
+%
+%   SCENARIO must be one of (case-sensitive):
+%     unmerged_full, unmerged_d0_10,
+%     mergeA2_full,  mergeA2_d0_10,
+%     mergeB2_full,  mergeB2_d0_10
+%   or a linear "days x tDCS" mixed model (Box-B2 vs Box-A2, per the paper):
+%     lme_unmerged_full, lme_unmerged_d0_10, lme_unmerged_d0_13,
+%     lme_mergeA2_full,  lme_mergeA2_d0_10,  lme_mergeA2_d0_13,
+%     lme_mergeB2_full,  lme_mergeB2_d0_10,  lme_mergeB2_d0_13
+%   The _d0_13 window ends where Box-A2 runs out of data, giving both anchor
+%   groups equal day coverage (the fair window for the interaction).
+%
+%   Each case below is a self-contained, independently invocable call of
+%   the same three-function pipeline; they are enumerated explicitly
+%   (rather than sharing one generic branch) so each scenario name is
+%   greppable on its own, per the project's spec.
+%
+%   Example:
+%     tdcs_glm('mergeB2_full')
+%
+%   See analysis/tdcs_glm.py (the Python this ports) and
+%   docs/superpowers/plans/2026-07-20-matlab-tdcs-analysis.md (Task 4).
+
+cfg = tdcs_config();
+
+switch scenario
+    case 'unmerged_full'
+        [S, meta] = tdcs_scenario_data('unmerged_full');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'unmerged_full');
+
+    case 'unmerged_d0_10'
+        [S, meta] = tdcs_scenario_data('unmerged_d0_10');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'unmerged_d0_10');
+
+    case 'mergeA2_full'
+        [S, meta] = tdcs_scenario_data('mergeA2_full');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'mergeA2_full');
+
+    case 'mergeA2_d0_10'
+        [S, meta] = tdcs_scenario_data('mergeA2_d0_10');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'mergeA2_d0_10');
+
+    case 'mergeB2_full'
+        [S, meta] = tdcs_scenario_data('mergeB2_full');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'mergeB2_full');
+
+    case 'mergeB2_d0_10'
+        [S, meta] = tdcs_scenario_data('mergeB2_d0_10');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'mergeB2_d0_10');
+
+    % --- mergeNaive: control = Box-A2 + Naive, tDCS = Box-B2 + Right-Electrode ---
+    case 'mergeNaive_full'
+        [S, meta] = tdcs_scenario_data('mergeNaive_full');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'mergeNaive_full');
+
+    case 'mergeNaive_d0_10'
+        [S, meta] = tdcs_scenario_data('mergeNaive_d0_10');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'mergeNaive_d0_10');
+
+    case 'mergeNaive_d0_13'
+        [S, meta] = tdcs_scenario_data('mergeNaive_d0_13');
+        R = tdcs_models(S, meta, cfg);
+        tdcs_report(S, R, meta, cfg, 'mergeNaive_d0_13');
+
+    % --- Linear "days x tDCS" mixed model (Box-B2 vs Box-A2), per the paper ---
+    case 'lme_unmerged_full'
+        L = tdcs_lme(tdcs_scenario_data('unmerged_full'), cfg);
+        tdcs_lme_report(L, 'lme_unmerged_full', cfg);
+
+    case 'lme_unmerged_d0_10'
+        L = tdcs_lme(tdcs_scenario_data('unmerged_d0_10'), cfg);
+        tdcs_lme_report(L, 'lme_unmerged_d0_10', cfg);
+
+    case 'lme_mergeA2_full'
+        L = tdcs_lme(tdcs_scenario_data('mergeA2_full'), cfg);
+        tdcs_lme_report(L, 'lme_mergeA2_full', cfg);
+
+    case 'lme_mergeA2_d0_10'
+        L = tdcs_lme(tdcs_scenario_data('mergeA2_d0_10'), cfg);
+        tdcs_lme_report(L, 'lme_mergeA2_d0_10', cfg);
+
+    case 'lme_mergeB2_full'
+        L = tdcs_lme(tdcs_scenario_data('mergeB2_full'), cfg);
+        tdcs_lme_report(L, 'lme_mergeB2_full', cfg);
+
+    case 'lme_mergeB2_d0_10'
+        L = tdcs_lme(tdcs_scenario_data('mergeB2_d0_10'), cfg);
+        tdcs_lme_report(L, 'lme_mergeB2_d0_10', cfg);
+
+    case 'lme_unmerged_d0_13'
+        L = tdcs_lme(tdcs_scenario_data('unmerged_d0_13'), cfg);
+        tdcs_lme_report(L, 'lme_unmerged_d0_13', cfg);
+
+    case 'lme_mergeA2_d0_13'
+        L = tdcs_lme(tdcs_scenario_data('mergeA2_d0_13'), cfg);
+        tdcs_lme_report(L, 'lme_mergeA2_d0_13', cfg);
+
+    case 'lme_mergeB2_d0_13'
+        L = tdcs_lme(tdcs_scenario_data('mergeB2_d0_13'), cfg);
+        tdcs_lme_report(L, 'lme_mergeB2_d0_13', cfg);
+
+    % --- Phased days x tDCS LME (fast/slow learning phases), Box-B2 vs Box-A2 ---
+    case 'phase_unmerged'
+        tdcs_phase_lme('unmerged', cfg);
+
+    case 'phase_mergeA2'
+        tdcs_phase_lme('mergeA2', cfg);
+
+    case 'phase_mergeB2'
+        tdcs_phase_lme('mergeB2', cfg);
+
+    % --- Paper replication: behavior ~ stim + day + stim:day + (1|rat) ---
+    case 'paper_unmerged'
+        tdcs_paper_lme('unmerged', cfg);
+
+    case 'paper_mergeA2'
+        tdcs_paper_lme('mergeA2', cfg);
+
+    case 'paper_mergeB2'
+        tdcs_paper_lme('mergeB2', cfg);
+
+    case 'paper_mergeNaive'
+        tdcs_paper_lme('mergeNaive', cfg);
+
+    case 'paper_mergeNaive_d0_10'
+        tdcs_paper_lme('mergeNaive', cfg, 'd0_10');
+
+    case 'paper_mergeNaive_d0_13'
+        tdcs_paper_lme('mergeNaive', cfg, 'd0_13');
+
+    case 'phase_mergeNaive'
+        tdcs_phase_lme('mergeNaive', cfg);
+
+    otherwise
+        error('tdcs_glm:badScenario', ...
+            ['Unrecognized scenario "%s". Valid scenarios are: ' ...
+            'unmerged_full, unmerged_d0_10, mergeA2_full, mergeA2_d0_10, ' ...
+            'mergeB2_full, mergeB2_d0_10, their lme_* variants ' ...
+            '(lme_unmerged_full, ..., lme_mergeB2_d0_13), and the phased LMEs ' ...
+            'phase_unmerged, phase_mergeA2, phase_mergeB2.'], scenario);
+end
+
+end

analysis/matlab/tdcs_lme.m

@@ -0,0 +1,63 @@
+function L = tdcs_lme(S, cfg)
+%TDCS_LME Linear mixed-effects "days x tDCS" model on the Box-B2 vs Box-A2 arm.
+%   L = TDCS_LME(S, CFG) subsets one scenario table S to the two anchor groups,
+%   codes a binary tDCS factor (Box-B2 = 1, Box-A2 = 0), and fits the LINEAR
+%   mixed model
+%
+%       success ~ day * tDCS + (1|subject)
+%
+%   replicating the published formulation (a linear mixed-effects model for the
+%   number of successful reaches with a days x tDCS interaction, a main effect
+%   of days, and a main effect of tDCS). `day` is raw and 0-indexed, and OUR
+%   day 0 corresponds to the paper's "Day 1", so the tDCS main effect is the
+%   group difference on Day 1 -- directly comparable to the paper's "equal on
+%   Day 1" test. (Do not re-index day to 1-based: that would move the main
+%   effect's evaluation point off Day 1.)
+%
+%   L fields:
+%     .lme         the LinearMixedModel object
+%     .nSubjects   number of subjects (Box-A2 + Box-B2)
+%     .nObs        number of sessions
+%     .interaction / .day / .tDCS   effect structs, each with .estimate .se
+%                  .t .df .p (from Coefficients) and .F .df1 .df2 .Fp (from
+%                  ANOVA). For these single-df terms F == t^2 and Fp == p.
+%
+%   NOTE: this is a LINEAR mixed model (Gaussian on the raw count), matching
+%   the paper's method, unlike the Poisson/Binomial GLMMs in tdcs_models.
+
+T = S(ismember(S.group, {cfg.anchorLow, cfg.anchorHigh}), :);
+T.group = removecats(T.group);
+T.tDCS = double(T.group == cfg.anchorHigh);   % Box-B2 = 1, Box-A2 = 0
+
+lme = fitlme(T, 'success ~ day*tDCS + (1|subject)');
+C = lme.Coefficients;
+A = anova(lme);
+As = anova(lme, 'DFMethod', 'satterthwaite');   % Satterthwaite denominator DF
+
+L.lme = lme;
+L.nSubjects = numel(unique(T.subject));
+L.nObs = height(T);
+L.maxDayA = max(T.day(T.group == cfg.anchorLow));    % last day Box-A2 has data
+L.maxDayB = max(T.day(T.group == cfg.anchorHigh));   % last day Box-B2 has data
+L.interaction = localTerm(C, A, As, 'day:tDCS');
+L.day = localTerm(C, A, As, 'day');
+L.tDCS = localTerm(C, A, As, 'tDCS');
+% Honest test: refit with a per-subject random SLOPE (see
+% tdcs_random_slope_interaction) so the interaction DF collapses toward n.
+L.interRS = tdcs_random_slope_interaction(T, ...
+    'success ~ day*tDCS + (day|subject)', 'day:tDCS');
+
+end
+
+function e = localTerm(C, A, As, name)
+%LOCALTERM Pull one term's coefficient (t/df/p), residual-DF ANOVA (F/df/p),
+%   and Satterthwaite denominator DF + p (.dfSatt, .pSatt).
+ci = strcmp(C.Name, name);
+ai = strcmp(A.Term, name);
+si = strcmp(As.Term, name);
+e = struct( ...
+    'estimate', C.Estimate(ci), 'se', C.SE(ci), ...
+    't', C.tStat(ci), 'df', C.DF(ci), 'p', C.pValue(ci), ...
+    'F', A.FStat(ai), 'df1', A.DF1(ai), 'df2', A.DF2(ai), 'Fp', A.pValue(ai), ...
+    'dfSatt', As.DF2(si), 'pSatt', As.pValue(si));
+end

analysis/matlab/tdcs_lme_report.m

@@ -0,0 +1,96 @@
+function tdcs_lme_report(L, scenario, cfg)
+%TDCS_LME_REPORT Print + save the days x tDCS linear-mixed-model report.
+%   TDCS_LME_REPORT(L, SCENARIO, CFG) formats the result of TDCS_LME to the
+%   console and to analysis/matlab/results/<SCENARIO>.txt, in the style of the
+%   published result (interaction, days, tDCS effects) plus interpretation and
+%   the paper's reference numbers.
+
+bar = repmat('=', 1, 78);
+s = sprintf('%s\n', bar);
+s = [s sprintf('LINEAR MIXED MODEL (days x tDCS) -- scenario: %s\n', scenario)];
+s = [s sprintf('%s\n', bar)];
+s = [s sprintf('model: success ~ day * tDCS + (1|subject)   [tDCS: %s = 1 vs %s = 0]\n', ...
+    cfg.anchorHigh, cfg.anchorLow)];
+s = [s sprintf('N = %d subjects, %d sessions\n', L.nSubjects, L.nObs)];
+s = [s sprintf('day coverage: Box-B2 0..%d, Box-A2 0..%d\n', L.maxDayB, L.maxDayA)];
+s = [s sprintf('(day is raw and 0-indexed: our day 0 = the paper''s "Day 1", so the tDCS\n')];
+s = [s sprintf(' main effect below is the group difference on Day 1 -- comparable to the paper.)\n')];
+if abs(L.maxDayB - L.maxDayA) > 2
+    s = [s sprintf(['** WARNING: the groups'' day coverage is UNEQUAL (differ by %d days). The\n' ...
+        '   interaction/slope over this window EXTRAPOLATES the shorter group''s line and is\n' ...
+        '   CONFOUNDED -- prefer the _d0_%d window (both groups have data throughout). **\n'], ...
+        abs(L.maxDayB - L.maxDayA), min(L.maxDayB, L.maxDayA))];
+end
+s = [s sprintf('\n')];
+
+s = [s tdcs_model_summary(L.lme, 'fitlme: success ~ day*tDCS + (1|subject)') sprintf('\n')];
+
+s = [s sprintf('%-27s %-20s %-12s %s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)')];
+s = [s sprintf('%s\n', repmat('-', 1, 78))];
+s = [s localRow('days x tDCS (interaction)', L.interaction)];
+s = [s localRow('days (learning)', L.day)];
+s = [s localRow('tDCS (main, at Day 1)', L.tDCS)];
+s = [s localHonest(L.interRS)];
+
+s = [s sprintf('\nINTERPRETATION\n')];
+if L.interaction.p >= 0.05
+    interTxt = 'slopes are parallel (no differential change over training)';
+elseif L.interaction.estimate > 0
+    interTxt = 'the tDCS (Box-B2) group improves FASTER -- benefit ACCUMULATES over training';
+else
+    interTxt = 'the tDCS (Box-B2) group improves SLOWER -- groups CONVERGE (Box-B2 is ahead early, the gap narrows)';
+end
+s = [s sprintf('  - days x tDCS interaction: %s (p=%.4f, slope diff=%.2f) -> %s.\n', ...
+    localSig(L.interaction.p), L.interaction.p, L.interaction.estimate, interTxt)];
+s = [s sprintf('  - days (learning): %s (p=%.2g) -> performance improves with training.\n', ...
+    localSig(L.day.p), L.day.p)];
+s = [s sprintf('  - tDCS main effect on Day 1 (our day 0): %s (p=%.4f) -> the groups %s on Day 1.\n', ...
+    localSig(L.tDCS.p), L.tDCS.p, ...
+    localPick(L.tDCS.p < 0.05, 'already DIFFER', 'are comparable (as in the paper)'))];
+
+s = [s sprintf('\nPaper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;\n')];
+s = [s sprintf('  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.\n')];
+s = [s sprintf('  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)\n')];
+
+fprintf('%s', s);
+
+thisDir = fileparts(mfilename('fullpath'));
+resDir = fullfile(thisDir, 'results');
+if ~exist(resDir, 'dir'); mkdir(resDir); end
+fid = fopen(fullfile(resDir, [scenario '.txt']), 'w');
+if fid < 0
+    error('tdcs_lme_report:fopen', 'Cannot open results file for "%s".', scenario);
+end
+cleanup = onCleanup(@() fclose(fid)); %#ok<NASGU>
+fprintf(fid, '%s', s);
+
+end
+
+function r = localRow(name, e)
+r = sprintf('%-27s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', ...
+    name, e.df, e.t, e.df1, e.F, e.p, e.pSatt, e.dfSatt);
+end
+
+function s = localHonest(rs)
+%LOCALHONEST Report the random-slope interaction (the honest LME test) + why
+%   Satterthwaite on the random-intercept model above barely changes the DF.
+if rs.ok
+    s = sprintf(['\nHONEST LME -- per-animal random slope (day|subject): ' ...
+        'interaction F(%d,%.1f)=%.2f, p=%.4g\n'], rs.df1, rs.df2, rs.F, rs.p);
+else
+    s = sprintf(['\nHONEST LME -- per-animal random slope (day|subject): ' ...
+        'model did not converge for this window.\n']);
+end
+s = [s sprintf(['  Note: Satterthwaite DF on the random-INTERCEPT model above stays ~= residual\n' ...
+    '  (the slope''s error is at the session level), so it does NOT fix pseudoreplication.\n' ...
+    '  Letting each animal have its OWN slope collapses the interaction DF toward the animal\n' ...
+    '  count -- this, and the per-animal slope test, are the honest learning-rate inference.\n'])];
+end
+
+function t = localSig(p)
+if p < 0.05; t = 'SIGNIFICANT'; else; t = 'n.s.'; end
+end
+
+function t = localPick(b, yes, no)
+if b; t = yes; else; t = no; end
+end

analysis/matlab/tdcs_load_data.m

@@ -0,0 +1,117 @@
+function T = tdcs_load_data()
+%TDCS_LOAD_DATA Load, join, and curate the raw tDCS reaching matrices.
+%   T = TDCS_LOAD_DATA() reads analysis/matlab/raw/raw_success.csv and
+%   raw_total.csv (app-exported matrix format: metric header line, blank
+%   line, file "Group" line, "# Days Reach" subject-header line, then one
+%   row per day with one value per subject column), reshapes each to long
+%   form, joins them on (subject, day), applies the curated 12-animal
+%   group map from tdcs_config (the file's own Group row is ignored),
+%   and restricts to day >= 0.
+%
+%   Returns a table with variables:
+%     subject  (string)      animal name
+%     group    (categorical) curated group (Naive, Electrode-Box-A,
+%                             Electrode-Box-A2, Electrode-Box-B2,
+%                             Right-Electrode)
+%     day      (double)      "# Days Reach" value, >= 0
+%     success  (double)      successful reaches in the session
+%     total    (double)      total attempts in the session
+%
+%   Only the 12 curated animals (per tdcs_config().groups) are retained;
+%   any other animal present in the raw export is dropped.
+
+thisDir = fileparts(mfilename('fullpath'));
+successFile = fullfile(thisDir, 'raw', 'raw_success.csv');
+totalFile = fullfile(thisDir, 'raw', 'raw_total.csv');
+
+Tsuccess = localReadMatrix(successFile, 'success');
+Ttotal = localReadMatrix(totalFile, 'total');
+
+% Left join keyed on (subject, day): every non-blank Success cell becomes
+% a row; Total is looked up for the same (subject, day). Per the plan's
+% Global Constraints, Total is fully populated wherever Success is, so
+% this should never introduce a missing total — but the join is a left
+% join (not an inner join) specifically so that an unmatched Success row
+% would still be kept (with total = NaN) rather than silently dropped.
+T = outerjoin(Tsuccess, Ttotal, 'Keys', {'subject', 'day'}, ...
+    'MergeKeys', true, 'Type', 'left');
+
+% Apply the curated map: keep only the 12 curated animals, and assign
+% group from the map (overriding the file's own Group row).
+cfg = tdcs_config();
+subjectsCell = cellstr(T.subject);
+keep = isKey(cfg.groups, subjectsCell);
+T = T(keep, :);
+subjectsCell = subjectsCell(keep);
+
+groupVals = cell(height(T), 1);
+for i = 1:height(T)
+    groupVals{i} = cfg.groups(subjectsCell{i});
+end
+T.group = categorical(groupVals);
+
+% Restrict to day >= 0 (analysis window).
+T = T(T.day >= 0, :);
+
+% Final variable order and a deterministic row order.
+T = T(:, {'subject', 'group', 'day', 'success', 'total'});
+T = sortrows(T, {'subject', 'day'});
+
+end
+
+function Tlong = localReadMatrix(filePath, valueName)
+%LOCALREADMATRIX Parse one app-exported day-by-subject matrix to long form.
+%   Line 1 = "Data,<metric>" (skipped), line 2 blank (skipped), line 3 =
+%   "Group,<...>" (skipped -- grouping comes from the curated map, not
+%   this row), line 4 = "# Days Reach,<subject headers>" (columns 2:end
+%   are the subject names), lines 5+ = "<day>,<value per subject>" with
+%   blank cells meaning "no session that day for that subject".
+
+lines = readlines(filePath);
+% Defensive: drop any wholly-blank trailing line (e.g. if the file ends
+% with a newline). Real data rows always start with a day number and are
+% never zero-length.
+while numel(lines) > 0 && strlength(strip(lines(end))) == 0
+    lines(end) = [];
+end
+
+headerParts = strsplit(lines(4), ',', 'CollapseDelimiters', false);
+subjects = string(headerParts(2:end));
+nSubjects = numel(subjects);
+
+dataLines = lines(5:end);
+dataLines = dataLines(strlength(strip(dataLines)) > 0);
+nRows = numel(dataLines);
+
+day = nan(nRows * nSubjects, 1);
+subjectCol = strings(nRows * nSubjects, 1);
+value = nan(nRows * nSubjects, 1);
+
+idx = 0;
+for r = 1:nRows
+    parts = strsplit(dataLines(r), ',', 'CollapseDelimiters', false);
+    dayVal = str2double(parts(1));
+    for c = 1:nSubjects
+        col = c + 1;
+        if col <= numel(parts)
+            cellStr = strtrim(parts(col));
+        else
+            cellStr = "";
+        end
+        if strlength(cellStr) == 0
+            continue
+        end
+        idx = idx + 1;
+        day(idx) = dayVal;
+        subjectCol(idx) = subjects(c);
+        value(idx) = str2double(cellStr);
+    end
+end
+
+day = day(1:idx);
+subjectCol = subjectCol(1:idx);
+value = value(1:idx);
+
+Tlong = table(subjectCol, day, value, 'VariableNames', {'subject', 'day', valueName});
+
+end

analysis/matlab/tdcs_model_summary.m

@@ -0,0 +1,28 @@
+function s = tdcs_model_summary(model, label)
+%TDCS_MODEL_SUMMARY Full native fitlme/fitglme model summary as plain text.
+%   S = TDCS_MODEL_SUMMARY(MODEL) captures DISP(MODEL) -- the complete summary
+%   MATLAB prints for a fitted LinearMixedModel / GeneralizedLinearMixedModel
+%   (model information and fit statistics, the formula, the fixed-effects
+%   coefficient table with 95% CIs, and the random-effects covariance) -- and
+%   strips the Command-Window-only <strong>...</strong> bold markup that EVALC
+%   would otherwise emit as literal text, so the result is plain text suitable
+%   for the console and the results/*.txt files.
+%
+%   S = TDCS_MODEL_SUMMARY(MODEL, LABEL) prefixes a titled banner naming the
+%   model (typically its formula), so each scenario's output carries a
+%   self-describing copy of the raw GLM/LME summary.
+%
+%   This is the single place that renders a fitted model verbatim; every report
+%   (tdcs_report, tdcs_lme_report, tdcs_paper_lme, tdcs_phase_lme) routes
+%   through it so that ALL scenarios print the full summary generated by the
+%   MATLAB model-fitting functions.
+
+raw = evalc('disp(model)');
+s = regexprep(raw, '</?strong>', '');
+
+if nargin > 1 && ~isempty(label)
+    bar = repmat('=', 1, 78);
+    s = sprintf('%s\nFULL MODEL SUMMARY -- %s\n%s\n%s', bar, label, bar, s);
+end
+
+end

analysis/matlab/tdcs_models.m

@@ -0,0 +1,253 @@
+function R = tdcs_models(S, meta, cfg)
+%TDCS_MODELS Fit the tDCS GLMMs, per-animal tests, and (unmerged) classification.
+%   R = TDCS_MODELS(S, META, CFG) consumes one scenario table S and its
+%   META (from TDCS_SCENARIO_DATA) plus CFG (from TDCS_CONFIG), and
+%   returns a struct R:
+%
+%     R.countGLME   GeneralizedLinearMixedModel, Poisson count-level model:
+%                     success ~ group + day_c + day_c2 + (1|subject)
+%     R.rateGLME    GeneralizedLinearMixedModel, Binomial rate-level model
+%                   (total==0 sessions dropped first):
+%                     success ~ group + day_c + day_c2 + (1|subject)
+%     R.learnGLME   GeneralizedLinearMixedModel, Poisson learning-rate
+%                   model with a group x day_c interaction:
+%                     success ~ group*day_c + day_c2 + (1|subject)
+%     R.learn       learning-rate comparison, Box-B2 vs Box-A2. The reported
+%                   result is a PER-ANIMAL slope test (cluster-honest, like
+%                   R.perAnimal): each subject's OLS slope of success on day,
+%                   then Welch t / two-sided Mann-Whitney across groups:
+%                   .slopeWelchP, .slopeMWUp, .nB, .nA, .meanSlopeB,
+%                   .meanSlopeA. The GLMM group x day_c joint F-test is also
+%                   kept for reference (.glmeJointP, .glmeJointF, .glmeJointDF1,
+%                   .glmeJointDF2) but is ANTICONSERVATIVE here: fitglme's
+%                   coefTest offers only observation-level DF, which overstates
+%                   significance for this few-subject design (it reads p~=0
+%                   even when the per-animal slopes are clearly parallel), so
+%                   it is NOT the basis for the conclusion.
+%     R.perAnimal   per-subject Box-B2 vs Box-A2 comparison (pure stats,
+%                   no GLME involved): .countWelchP, .countMWUp,
+%                   .rateWelchP, .rateMWUp, .nB, .nA. "count" = per-subject
+%                   mean success; "rate" = per-subject pooled
+%                   sum(success)/sum(total). Welch = TTEST2(...,
+%                   'Vartype','unequal') (two-sided); MWU = RANKSUM(...,
+%                   'tail','right') (one-sided, B2 > A2).
+%     R.h2          anchor check (Box-A2 vs Box-B2) read off the
+%                   count/rate GLMEs' group_<anchorLow> fixed effect
+%                   (coded vs the cfg.ref = anchorHigh reference level):
+%                   .countIRR, .countOneSidedP, .rateOR, .rateOneSidedP.
+%                   IRR/OR = 1/exp(coef) is the Box-B2-over-Box-A2
+%                   advantage (so > 1 supports H2); one-sided p =
+%                   NORMCDF(coef/se), testing coef < 0 (B2 above A2).
+%     R.classify    (unmerged scenarios only, i.e. meta.isUnmerged) struct
+%                   with one field per unknown in cfg.unknowns (field name
+%                   via matlab.lang.makeValidName), each holding .count and
+%                   .rate sub-structs with .vsA2 (.ratio, .p), .vsB2
+%                   (.ratio, .p), and .label (one of 'B2-like', 'A2-like',
+%                   'AMBIGUOUS (nearer <anchor>)', 'UNLIKE BOTH'), per
+%                   METHODS.md's classification logic. For merged
+%                   scenarios (meta.isUnmerged == false), R.classify is an
+%                   empty (no-field) struct.
+%
+%   See analysis/tdcs_glm.py (the Python this ports) and
+%   docs/superpowers/plans/2026-07-20-matlab-tdcs-analysis.md (Task 3).
+
+countFormula = 'success ~ group + day_c + day_c2 + (1|subject)';
+R.countGLME = fitglme(S, countFormula, 'Distribution', 'Poisson', 'Link', 'log');
+
+Sr = S(S.total > 0, :);
+R.rateGLME = fitglme(Sr, countFormula, 'Distribution', 'Binomial', ...
+    'BinomialSize', Sr.total, 'Link', 'logit');
+
+learnFormula = 'success ~ group*day_c + day_c2 + (1|subject)';
+R.learnGLME = fitglme(S, learnFormula, 'Distribution', 'Poisson');
+
+R.perAnimal = localPerAnimal(S, cfg);
+R.h2 = localH2(R.countGLME, R.rateGLME, cfg);
+R.learn = localLearn(R.learnGLME, S, cfg);
+
+if meta.isUnmerged
+    R.classify = localClassifyAll(R.countGLME, R.rateGLME, cfg);
+else
+    R.classify = struct();
+end
+
+end
+
+% --------------------------------------------------------------- per-animal
+function out = localPerAnimal(S, cfg)
+%LOCALPERANIMAL Per-subject Box-B2 vs Box-A2 Welch t-test / MWU (pure stats).
+b = S(S.group == cfg.anchorHigh, :);
+a = S(S.group == cfg.anchorLow, :);
+
+[bCount, bRate, nB] = localSubjectSummary(b);
+[aCount, aRate, nA] = localSubjectSummary(a);
+
+[~, countWelchP] = ttest2(bCount, aCount, 'Vartype', 'unequal');
+countMWUp = ranksum(bCount, aCount, 'tail', 'right');
+[~, rateWelchP] = ttest2(bRate, aRate, 'Vartype', 'unequal');
+rateMWUp = ranksum(bRate, aRate, 'tail', 'right');
+
+out = struct( ...
+    'countWelchP', countWelchP, 'countMWUp', countMWUp, ...
+    'rateWelchP', rateWelchP, 'rateMWUp', rateMWUp, ...
+    'nB', nB, 'nA', nA);
+end
+
+function [meanSuccess, pooledRate, nSubj] = localSubjectSummary(T)
+%LOCALSUBJECTSUMMARY Per-subject mean success and pooled success/total.
+subj = unique(T.subject);
+nSubj = numel(subj);
+meanSuccess = zeros(nSubj, 1);
+pooledRate = zeros(nSubj, 1);
+for i = 1:nSubj
+    rows = T.subject == subj(i);
+    meanSuccess(i) = mean(T.success(rows));
+    pooledRate(i) = sum(T.success(rows)) / sum(T.total(rows));
+end
+end
+
+% --------------------------------------------------------------------- H2
+function h2 = localH2(countGLME, rateGLME, cfg)
+%LOCALH2 Anchor check: Box-A2 vs Box-B2, read off each GLME's group term.
+[countCoef, countSE] = localCoefAndSE(countGLME, cfg.anchorLow, cfg.ref);
+[rateCoef, rateSE] = localCoefAndSE(rateGLME, cfg.anchorLow, cfg.ref);
+
+h2 = struct( ...
+    'countIRR', 1 / exp(countCoef), ...
+    'countOneSidedP', normcdf(countCoef / countSE), ...
+    'rateOR', 1 / exp(rateCoef), ...
+    'rateOneSidedP', normcdf(rateCoef / rateSE));
+end
+
+% -------------------------------------------------------------- classify
+function out = localClassifyAll(countGLME, rateGLME, cfg)
+%LOCALCLASSIFYALL Classify every unknown in cfg.unknowns vs both anchors.
+out = struct();
+for i = 1:numel(cfg.unknowns)
+    unknown = cfg.unknowns{i};
+    fieldName = matlab.lang.makeValidName(unknown);
+    out.(fieldName) = struct( ...
+        'count', localClassifyOne(countGLME, unknown, cfg), ...
+        'rate', localClassifyOne(rateGLME, unknown, cfg));
+end
+end
+
+function verdict = localClassifyOne(model, unknown, cfg)
+%LOCALCLASSIFYONE Compare UNKNOWN vs both anchors on one GLME; label it.
+%   Mirrors classify_one() in analysis/tdcs_glm.py / METHODS.md's
+%   "Classification logic (unknowns)": indistinguishable (p >= 0.05) from
+%   one anchor and different from the other => that anchor's label;
+%   indistinguishable from both => AMBIGUOUS (report the numerically
+%   nearer one); different from both => UNLIKE BOTH.
+[coefLo, pLo] = localContrast(model, unknown, cfg.anchorLow, cfg.ref);
+[coefHi, pHi] = localContrast(model, unknown, cfg.anchorHigh, cfg.ref);
+
+vsA2 = struct('ratio', exp(coefLo), 'p', pLo);
+vsB2 = struct('ratio', exp(coefHi), 'p', pHi);
+
+likeLo = pLo >= 0.05;
+likeHi = pHi >= 0.05;
+if likeHi && ~likeLo
+    label = 'B2-like';
+elseif likeLo && ~likeHi
+    label = 'A2-like';
+elseif likeLo && likeHi
+    if abs(coefHi) < abs(coefLo)
+        nearer = cfg.anchorHigh;
+    else
+        nearer = cfg.anchorLow;
+    end
+    label = sprintf('AMBIGUOUS (nearer %s)', nearer);
+else
+    label = 'UNLIKE BOTH';
+end
+
+verdict = struct('vsA2', vsA2, 'vsB2', vsB2, 'label', label);
+end
+
+% ------------------------------------------------------------- contrasts
+function [coef, se] = localCoefAndSE(model, aName, refName)
+%LOCALCOEFANDSE Coefficient + SE for group level A_NAME vs the reference.
+v = localContrastVector(model, aName, refName, refName);
+beta = model.Coefficients.Estimate;
+Cov = model.CoefficientCovariance;
+coef = v * beta;
+se = sqrt(v * Cov * v');
+end
+
+function [coef, p] = localContrast(model, aName, bName, refName)
+%LOCALCONTRAST Linear contrast A_NAME vs B_NAME (both coded vs REF_NAME).
+%   Two-sided p via COEFTEST (an F-test on one row = the two-sided
+%   Wald/t-test); works for any pair, including B_NAME == REF_NAME.
+v = localContrastVector(model, aName, bName, refName);
+beta = model.Coefficients.Estimate;
+coef = v * beta;
+p = coefTest(model, v);
+end
+
+function v = localContrastVector(model, aName, bName, refName)
+%LOCALCONTRASTVECTOR +1 at group_A_NAME (if not the reference), -1 at
+%   group_B_NAME (if not the reference), over MODEL.CoefficientNames.
+cn = model.CoefficientNames;
+v = zeros(1, numel(cn));
+if ~strcmp(aName, refName)
+    v(localCoefIndex(cn, aName)) = v(localCoefIndex(cn, aName)) + 1;
+end
+if ~strcmp(bName, refName)
+    v(localCoefIndex(cn, bName)) = v(localCoefIndex(cn, bName)) - 1;
+end
+end
+
+function idx = localCoefIndex(coefNames, groupName)
+%LOCALCOEFINDEX Index into CoefficientNames for a "group_<groupName>" term.
+idx = find(strcmp(coefNames, ['group_' groupName]), 1);
+if isempty(idx)
+    error('tdcs_models:missingGroupTerm', ...
+        'No "group_%s" coefficient in this GLME (CoefficientNames: %s).', ...
+        groupName, strjoin(coefNames, ', '));
+end
+end
+
+% -------------------------------------------------------------- learning
+function out = localLearn(learnGLME, S, cfg)
+%LOCALLEARN Learning-rate result: per-animal slope test (primary, cluster-
+%   honest) plus the GLMM interaction joint F-test (reference, anticonservative).
+bSlopes = localSubjectSlopes(S(S.group == cfg.anchorHigh, :));
+aSlopes = localSubjectSlopes(S(S.group == cfg.anchorLow, :));
+[~, slopeWelchP] = ttest2(bSlopes, aSlopes, 'Vartype', 'unequal');
+slopeMWUp = ranksum(bSlopes, aSlopes);   % two-sided: slope-diff direction unknown
+
+j = localJointInteractionTest(learnGLME);
+out = struct( ...
+    'slopeWelchP', slopeWelchP, 'slopeMWUp', slopeMWUp, ...
+    'nB', numel(bSlopes), 'nA', numel(aSlopes), ...
+    'meanSlopeB', mean(bSlopes), 'meanSlopeA', mean(aSlopes), ...
+    'glmeJointP', j.jointP, 'glmeJointF', j.jointF, ...
+    'glmeJointDF1', j.jointDF1, 'glmeJointDF2', j.jointDF2);
+end
+
+function slopes = localSubjectSlopes(T)
+%LOCALSUBJECTSLOPES Per-subject OLS slope of success on day.
+subj = unique(T.subject);
+slopes = zeros(numel(subj), 1);
+for i = 1:numel(subj)
+    rows = T.subject == subj(i);
+    coeffs = polyfit(T.day(rows), T.success(rows), 1);
+    slopes(i) = coeffs(1);
+end
+end
+
+function out = localJointInteractionTest(learnGLME)
+%LOCALJOINTINTERACTIONTEST Joint Wald test that all group x day_c
+%   interaction fixed effects are zero (do groups learn at different
+%   rates?), mirroring the GEE wald_test in analysis/tdcs_glm.py.
+cn = learnGLME.CoefficientNames;
+isInteraction = contains(cn, ':day_c') & ~contains(cn, 'day_c2');
+idx = find(isInteraction);
+H = zeros(numel(idx), numel(cn));
+for i = 1:numel(idx)
+    H(i, idx(i)) = 1;
+end
+[p, F, df1, df2] = coefTest(learnGLME, H);
+out = struct('jointP', p, 'jointF', F, 'jointDF1', df1, 'jointDF2', df2);
+end

analysis/matlab/tdcs_paper_lme.m

@@ -0,0 +1,167 @@
+function L = tdcs_paper_lme(mergeKey, cfg, windowKey)
+%TDCS_PAPER_LME Replicate the paper's linear mixed model, verbatim formula:
+%       behavior ~ stim + day + stim:day + (1|rat)
+%   fit with fitlme on the Box-B2 (stim = 1, tDCS) vs Box-A2 (stim = 0, control)
+%   subset of the MERGEKEY grouping ('unmerged'|'mergeA2'|'mergeB2'|'mergeNaive').
+%   `behavior` = successful reaches (COUNT), `day` = training day (raw; day 0 =
+%   the paper's "Day 1"), `rat` = subject. Reports the three effects (stim x day
+%   interaction, day, stim) as t(df) / F(1) / p with the interaction 95% CI,
+%   alongside the paper's reference values, and writes a results/ txt file.
+%
+%   L = TDCS_PAPER_LME(MERGEKEY, CFG) uses the full training range (default) and
+%   writes results/paper_<MERGEKEY>.txt.
+%
+%   L = TDCS_PAPER_LME(MERGEKEY, CFG, WINDOWKEY) restricts to a day window --
+%   WINDOWKEY is 'full' (day <= 26), 'd0_10' (day <= 10), or 'd0_13' (day <= 13,
+%   the last day Box-A2 has data). The windowed fits give the two anchor groups
+%   EQUAL day coverage, so the stim:day interaction is not confounded by the
+%   full-range coverage imbalance; windowed runs write
+%   results/paper_<MERGEKEY>_<WINDOWKEY>.txt.
+%
+%   (This is the same model as tdcs_lme -- success ~ day*tDCS + (1|subject) --
+%   written with the paper's exact term order and variable names.)
+%
+%   L fields: .model .nRats .nObs .windowKey and .stim/.day/.interaction effect
+%   structs (.estimate .se .t .df .p from Coefficients; .F .df1 .df2 .Fp from
+%   ANOVA; .ci = coefficient 95% CI).
+
+if nargin < 3 || isempty(windowKey)
+    windowKey = 'full';
+end
+if ~ismember(windowKey, {'full', 'd0_10', 'd0_13'})
+    error('tdcs_paper_lme:badWindow', ...
+        'windowKey must be ''full'', ''d0_10'', or ''d0_13'' (got "%s").', windowKey);
+end
+
+S = tdcs_scenario_data([mergeKey '_' windowKey]);
+A = S(ismember(S.group, {cfg.anchorLow, cfg.anchorHigh}), :);
+
+tbl = table();
+tbl.behavior = A.success;
+tbl.day = A.day;
+tbl.stim = double(A.group == cfg.anchorHigh);   % Box-B2 = 1, Box-A2 = 0
+tbl.rat = A.subject;
+
+model = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
+C = model.Coefficients; An = anova(model); ci = coefCI(model);
+Ans = anova(model, 'DFMethod', 'satterthwaite');   % Satterthwaite denominator DF
+
+L.model = model;
+L.windowKey = windowKey;
+L.nRats = numel(unique(tbl.rat));
+L.nObs = height(tbl);
+L.maxDayCtrl = max(tbl.day(tbl.stim == 0));
+L.maxDayStim = max(tbl.day(tbl.stim == 1));
+L.stim = localTerm(C, An, Ans, ci, 'stim');
+L.day = localTerm(C, An, Ans, ci, 'day');
+L.interaction = localTerm(C, An, Ans, ci, 'day:stim');   % MATLAB canonicalizes stim:day -> day:stim
+% Honest test: refit with a per-rat random SLOPE so the interaction DF
+% collapses toward the animal count (see tdcs_random_slope_interaction).
+L.interRS = tdcs_random_slope_interaction(tbl, ...
+    'behavior ~ stim + day + stim:day + (day|rat)', 'day:stim');
+
+localReport(L, mergeKey, cfg);
+
+end
+
+function e = localTerm(C, An, Ans, ci, name)
+i = strcmp(C.Name, name);
+if ~any(i)
+    error('tdcs_paper_lme:missingTerm', 'No "%s" coefficient (have: %s).', ...
+        name, strjoin(C.Name, ', '));
+end
+ai = strcmp(An.Term, name);
+si = strcmp(Ans.Term, name);
+e = struct('estimate', C.Estimate(i), 'se', C.SE(i), 't', C.tStat(i), ...
+    'df', C.DF(i), 'p', C.pValue(i), 'F', An.FStat(ai), 'df1', An.DF1(ai), ...
+    'df2', An.DF2(ai), 'Fp', An.pValue(ai), 'ci', ci(i, :), ...
+    'dfSatt', Ans.DF2(si), 'pSatt', Ans.pValue(si));
+end
+
+function localReport(L, mergeKey, cfg)
+window = L.windowKey;
+if strcmp(window, 'full')
+    scenarioName = ['paper_' mergeKey];
+    windowLabel = 'full range';
+else
+    scenarioName = ['paper_' mergeKey '_' window];
+    windowLabel = strrep(window, 'd0_10', 'days 0-10');
+    windowLabel = strrep(windowLabel, 'd0_13', 'days 0-13');
+end
+
+bar = repmat('=', 1, 78);
+s = sprintf('%s\n', bar);
+s = [s sprintf('PAPER LME REPLICATION -- %s (%s)\n', mergeKey, windowLabel)];
+s = [s sprintf('%s\n', bar)];
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   [stim: %s=1 vs %s=0]\n', ...
+    cfg.anchorHigh, cfg.anchorLow)];
+s = [s sprintf('behavior = successful reaches (COUNT per session)\n')];
+s = [s sprintf('N = %d rats, %d sessions   (day raw; day 0 = paper "Day 1")\n', L.nRats, L.nObs)];
+s = [s sprintf('day coverage: stim(B2) 0..%d, control(A2) 0..%d\n', L.maxDayStim, L.maxDayCtrl)];
+if abs(L.maxDayStim - L.maxDayCtrl) > 2
+    s = [s sprintf(['** WARNING: unequal day coverage -- the full-range stim:day interaction\n' ...
+        '   extrapolates the control group''s line and is CONFOUNDED here (the paper''s\n' ...
+        '   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **\n'])];
+else
+    s = [s sprintf(['(Equal day coverage -- the stim:day interaction over this window is NOT\n' ...
+        ' confounded by the full-range coverage imbalance.)\n'])];
+end
+s = [s sprintf('\n')];
+s = [s tdcs_model_summary(L.model, 'fitlme: behavior ~ stim + day + stim:day + (1|rat)') sprintf('\n')];
+s = [s sprintf('\n%-26s %-18s %-12s %s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)')];
+s = [s sprintf('%s\n', repmat('-', 1, 76))];
+s = [s localRow('stim x day (interaction)', L.interaction)];
+s = [s localRow('day (learning)', L.day)];
+s = [s localRow('stim (main, Day 1)', L.stim)];
+s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', L.interaction.ci(1), L.interaction.ci(2))];
+s = [s localHonest(L.interRS)];
+s = [s sprintf('\nINTERPRETATION\n')];
+if L.interaction.p >= 0.05
+    interTxt = 'slopes parallel -- no differential learning rate over this window';
+elseif L.interaction.estimate > 0
+    interTxt = 'tDCS (Box-B2) improves FASTER -- benefit accumulates over training';
+else
+    interTxt = 'tDCS (Box-B2) improves SLOWER -- groups converge';
+end
+s = [s sprintf('  - stim x day interaction: %s (p=%.4f, slope diff=%+.2f) -> %s.\n', ...
+    localSigTxt(L.interaction.p), L.interaction.p, L.interaction.estimate, interTxt)];
+s = [s sprintf('  - stim main effect on Day 1 (our day 0): %s (p=%.4f) -> groups %s on Day 1.\n', ...
+    localSigTxt(L.stim.p), L.stim.p, localPick(L.stim.p < 0.05, 'already DIFFER', 'are comparable'))];
+s = [s sprintf(['\nPaper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; ' ...
+    'day t(227)=9.64,\n  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.\n'])];
+
+fprintf('%s', s);
+thisDir = fileparts(mfilename('fullpath'));
+resDir = fullfile(thisDir, 'results');
+if ~exist(resDir, 'dir'); mkdir(resDir); end
+fid = fopen(fullfile(resDir, [scenarioName '.txt']), 'w');
+if fid < 0; error('tdcs_paper_lme:fopen', 'Cannot open results file.'); end
+cleanup = onCleanup(@() fclose(fid)); %#ok<NASGU>
+fprintf(fid, '%s', s);
+end
+
+function r = localRow(name, e)
+r = sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', ...
+    name, e.df, e.t, e.df1, e.F, e.p, e.pSatt, e.dfSatt);
+end
+
+function s = localHonest(rs)
+if rs.ok
+    s = sprintf(['\nHONEST LME -- per-rat random slope (day|rat): ' ...
+        'interaction F(%d,%.1f)=%.2f, p=%.4g\n'], rs.df1, rs.df2, rs.F, rs.p);
+else
+    s = sprintf(['\nHONEST LME -- per-rat random slope (day|rat): ' ...
+        'model did not converge for this window.\n']);
+end
+s = [s sprintf(['  Satterthwaite DF on the random-INTERCEPT model above stays ~= residual (the\n' ...
+    '  slope''s error is at session level), so it does NOT fix pseudoreplication. A per-animal\n' ...
+    '  random slope collapses the interaction DF toward the animal count -- the honest test.\n'])];
+end
+
+function t = localSigTxt(p)
+if p < 0.05; t = 'SIGNIFICANT'; else; t = 'n.s.'; end
+end
+
+function t = localPick(b, yes, no)
+if b; t = yes; else; t = no; end
+end

analysis/matlab/tdcs_phase_lme.m

@@ -0,0 +1,96 @@
+function P = tdcs_phase_lme(mergeKey, cfg, phases)
+%TDCS_PHASE_LME Refit the days x tDCS LME within learning phases (Box-B2 vs A2).
+%   P = TDCS_PHASE_LME(MERGEKEY, CFG) fits the same linear mixed model as the
+%   lme_* scenarios -- success ~ dayp*tDCS + (1|subject) -- SEPARATELY within
+%   each learning phase, for the Box-B2 (tDCS=1) vs Box-A2 (tDCS=0) subset of
+%   the MERGEKEY grouping ('unmerged' | 'mergeA2' | 'mergeB2'). `dayp` is `day`
+%   centered at each phase's start, so the tDCS main effect reads as the group
+%   difference on the phase's first day (the interaction and slopes are
+%   invariant to this centering). Prints a per-phase table and writes
+%   results/phase_<MERGEKEY>.txt.
+%
+%   P = TDCS_PHASE_LME(MERGEKEY, CFG, PHASES) uses custom phase windows
+%   (default {[0 5],[6 10],[6 13]}), each a [loDay hiDay] pair.
+%
+%   P.phases{k} has: .phase .nSub .a2Slope .b2Slope .dayP (learning) .tDCSlevelP
+%   (between-group level at phase start) .interP .interEst .interCI (the
+%   between-group learning-rate difference and its 95% CI).
+
+if nargin < 3 || isempty(phases)
+    phases = {[0 5], [6 10], [6 13]};
+end
+
+Sfull = tdcs_scenario_data([mergeKey '_full']);
+T = Sfull(ismember(Sfull.group, {cfg.anchorLow, cfg.anchorHigh}), :);
+
+P = struct('mergeKey', mergeKey, 'phases', {cell(1, numel(phases))});
+
+bar = repmat('=', 1, 78);
+s = sprintf('%s\n', bar);
+s = [s sprintf('PHASED days x tDCS LME -- %s (Box-B2 vs Box-A2)\n', mergeKey)];
+s = [s sprintf('%s\n', bar)];
+s = [s sprintf('model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]\n\n')];
+s = [s sprintf('%-8s N A2slp B2slp  day p    tDCS p   intP(res) intP(Satt) intP(RS)  slopeDiff [95%% CI]\n', 'phase')];
+s = [s sprintf('%s\n', repmat('-', 1, 104))];
+
+rawSummaries = '';
+for k = 1:numel(phases)
+    ph = phases{k};
+    Tp = T(T.day >= ph(1) & T.day <= ph(2), :);
+    Tp.dayp = Tp.day - ph(1);
+    Tp.tDCS = double(Tp.group == cfg.anchorHigh);
+
+    lme = fitlme(Tp, 'success ~ dayp*tDCS + (1|subject)');
+    C = lme.Coefficients; A = anova(lme); ci = coefCI(lme);
+    As = anova(lme, 'DFMethod', 'satterthwaite');
+    rs = tdcs_random_slope_interaction(Tp, ...
+        'success ~ dayp*tDCS + (dayp|subject)', 'dayp:tDCS');
+    ii = strcmp(C.Name, 'dayp:tDCS');
+    di = strcmp(C.Name, 'dayp');
+
+    e = struct();
+    e.phase = ph;
+    e.nSub = numel(unique(Tp.subject));
+    e.a2Slope = C.Estimate(di);
+    e.b2Slope = C.Estimate(di) + C.Estimate(ii);
+    e.dayP = A.pValue(strcmp(A.Term, 'dayp'));
+    e.tDCSlevelP = A.pValue(strcmp(A.Term, 'tDCS'));
+    e.interP = A.pValue(strcmp(A.Term, 'dayp:tDCS'));
+    e.interPsatt = As.pValue(strcmp(As.Term, 'dayp:tDCS'));   % Satterthwaite DF
+    e.interPrs = rs.p; e.rsOk = rs.ok;                        % honest random-slope
+    e.interEst = C.Estimate(ii);
+    e.interCI = ci(ii, :);
+    P.phases{k} = e;
+
+    if rs.ok; rsStr = sprintf('%.3f', rs.p); else; rsStr = 'n/a'; end
+    s = [s sprintf('%d-%-5d %d %5.2f %5.2f  %-8.2g %-8.3f %-9.3f %-10.3f %-9s %+.2f [%+.2f,%+.2f]\n', ...
+        ph(1), ph(2), e.nSub, e.a2Slope, e.b2Slope, e.dayP, e.tDCSlevelP, ...
+        e.interP, e.interPsatt, rsStr, e.interEst, e.interCI(1), e.interCI(2))]; %#ok<AGROW>
+
+    rawSummaries = [rawSummaries tdcs_model_summary(lme, ...
+        sprintf('phase %d-%d: success ~ dayp*tDCS + (1|subject)', ph(1), ph(2))) ...
+        sprintf('\n')]; %#ok<AGROW>
+end
+
+s = [s sprintf(['\nColumns: intP(res)=observation-level DF (anticonservative); intP(Satt)=Satterthwaite\n' ...
+    'DF (~= residual on this random-intercept model); intP(RS)=per-animal random-slope\n' ...
+    '(dayp|subject) model, the honest test (DF collapses toward the animal count; ''n/a'' if it\n' ...
+    'did not converge). The early phase carries the Box-B2 faster-acquisition signal; late\n' ...
+    'phases converge.\n'])];
+
+s = [s rawSummaries];
+
+fprintf('%s', s);
+localWrite(['phase_' mergeKey], s);
+
+end
+
+function localWrite(name, s)
+thisDir = fileparts(mfilename('fullpath'));
+resDir = fullfile(thisDir, 'results');
+if ~exist(resDir, 'dir'); mkdir(resDir); end
+fid = fopen(fullfile(resDir, [name '.txt']), 'w');
+if fid < 0; error('tdcs_phase_lme:fopen', 'Cannot open results file for "%s".', name); end
+cleanup = onCleanup(@() fclose(fid)); %#ok<NASGU>
+fprintf(fid, '%s', s);
+end

analysis/matlab/tdcs_power_sim.m

@@ -0,0 +1,101 @@
+function PW = tdcs_power_sim(mergeKey, window, Ns, effMuls, nrep, cfg)
+%TDCS_POWER_SIM Monte-Carlo power for the paper's stim x day interaction.
+%   Uses the paper's exact model -- fitlme(tbl, 'behavior ~ stim + day +
+%   stim:day + (1|rat)') -- throughout. The fitted model for MERGEKEY over the
+%   WINDOW = [lo hi] day range (Box-B2 = stim = 1 vs Box-A2 = stim = 0) is the
+%   ground truth (its fixed effects, rat random-intercept SD, residual SD);
+%   NREP datasets are simulated at each rats-per-group in NS, for each
+%   true-effect multiplier in EFFMULS (e.g. [1 0.5] = observed and half the
+%   observed stim:day slope). Each dataset is scored at alpha = 0.05 two ways:
+%     - per-animal (cluster-honest): per-rat behavior~day slope, Welch t (stim)
+%     - LME: the fitlme stim:day interaction p (observation-level DF)
+%   PW is a table (effMul, N, powerPerAnimal, powerLME); also printed. A fixed
+%   RNG seed makes the estimate reproducible.
+%
+%   Defaults: WINDOW=[0 5] (early phase), NS=[3 5 8 12 16 24 30],
+%   EFFMULS=[1 0.5], NREP=200.
+
+if nargin < 1 || isempty(mergeKey); mergeKey = 'mergeA2'; end
+if nargin < 2 || isempty(window);   window = [0 5]; end
+if nargin < 3 || isempty(Ns);       Ns = [3 5 8 12 16 24 30]; end
+if nargin < 4 || isempty(effMuls);  effMuls = [1 0.5]; end
+if nargin < 5 || isempty(nrep);     nrep = 200; end
+if nargin < 6 || isempty(cfg);      cfg = tdcs_config(); end
+
+warnState = warning('off', 'all'); cleanupW = onCleanup(@() warning(warnState)); %#ok<NASGU>
+rng(1);
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
+
+% Ground truth = fitted paper model over the window.
+Sfull = tdcs_scenario_data([mergeKey '_full']);
+A = Sfull(ismember(Sfull.group, {cfg.anchorLow, cfg.anchorHigh}), :);
+A = A(A.day >= window(1) & A.day <= window(2), :);
+tbl0 = table();
+tbl0.behavior = A.success;
+tbl0.day = A.day - window(1);      % 0-based within the window
+tbl0.stim = double(A.group == cfg.anchorHigh);
+tbl0.rat = A.subject;
+lme = fitlme(tbl0, FORMULA);
+cn = lme.CoefficientNames; be = lme.fixedEffects;
+b0 = be(strcmp(cn,'(Intercept)')); bStim = be(strcmp(cn,'stim'));
+bDay = be(strcmp(cn,'day'));       bInt = be(strcmp(cn,'day:stim'));
+psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
+days = (window(1):window(2))' - window(1);
+
+fprintf('Power sim (paper formula): truth=%s window %d-%d | stim:day=%.2f ratSD=%.2f resSD=%.2f | nrep=%d\n', ...
+    mergeKey, window(1), window(2), bInt, sRat, sRes, nrep);
+
+rows = {};
+for eMul = effMuls
+    bI = bInt * eMul;
+    fprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul*100);
+    fprintf('  %-8s | per-animal power | LME power\n', 'N/group');
+    for N = Ns
+        sigPA = 0; sigL = 0;
+        for r = 1:nrep
+            tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes);
+            sigPA = sigPA + (localPerAnimalP(tbl) < 0.05);
+            try
+                m = fitlme(tbl, FORMULA);
+                Cm = m.Coefficients;
+                sigL = sigL + (Cm.pValue(strcmp(Cm.Name,'day:stim')) < 0.05);
+            catch
+            end
+        end
+        pPA = sigPA / nrep; pL = sigL / nrep;
+        fprintf('  %-8d | %5.2f            | %5.2f\n', N, pPA, pL);
+        rows(end+1, :) = {eMul, N, pPA, pL}; %#ok<AGROW>
+    end
+end
+PW = cell2table(rows, 'VariableNames', {'effMul','N','powerPerAnimal','powerLME'});
+
+end
+
+function tbl = localSim(N, days, b0, bStim, bDay, bI, sRat, sRes)
+nd = numel(days); rows = 2*N*nd;
+rat = strings(rows,1); day = zeros(rows,1); stim = zeros(rows,1); behavior = zeros(rows,1);
+k = 0;
+for g = 0:1
+    for sIdx = 1:N
+        re = sRat * randn;
+        rid = sprintf('g%d_r%d', g, sIdx);
+        for d = 1:nd
+            k = k+1;
+            rat(k) = rid; day(k) = days(d); stim(k) = g;
+            behavior(k) = b0 + bStim*g + bDay*days(d) + bI*days(d)*g + re + sRes*randn;
+        end
+    end
+end
+tbl = table(categorical(rat), day, stim, behavior, ...
+    'VariableNames', {'rat','day','stim','behavior'});
+end
+
+function p = localPerAnimalP(tbl)
+rats = unique(tbl.rat); sl = zeros(numel(rats),1); gr = zeros(numel(rats),1);
+for i = 1:numel(rats)
+    r = tbl.rat == rats(i);
+    c = polyfit(tbl.day(r), tbl.behavior(r), 1); sl(i) = c(1);
+    gr(i) = tbl.stim(find(r,1));
+end
+[~, p] = ttest2(sl(gr==1), sl(gr==0), 'Vartype', 'unequal');
+end

analysis/matlab/tdcs_random_slope_interaction.m

@@ -0,0 +1,33 @@
+function rs = tdcs_random_slope_interaction(T, formula, term)
+%TDCS_RANDOM_SLOPE_INTERACTION Honest LME test of a slope-interaction TERM.
+%   RS = TDCS_RANDOM_SLOPE_INTERACTION(T, FORMULA, TERM) refits the linear mixed
+%   model FORMULA (which must include a per-subject random SLOPE, e.g.
+%   'success ~ day*tDCS + (day|subject)') on table T and returns the
+%   Satterthwaite-DF marginal F-test of TERM (e.g. 'day:tDCS').
+%
+%   Why this is the honest test. The paper's model has only a random INTERCEPT,
+%   so it assumes every animal shares one true slope and estimates the slope /
+%   interaction with session-level precision -- Satterthwaite DF on that model
+%   stays ~= residual DF and does NOT fix the pseudoreplication. Giving each
+%   animal its OWN slope (a random slope) lets the between-animal slope variance
+%   enter the standard error, and the Satterthwaite denominator DF then collapses
+%   toward the number of animals -- matching the per-animal (cluster-honest)
+%   test. RS fields: .ok (false if the richer model failed to fit), .F, .df1,
+%   .df2 (Satterthwaite), .p.
+
+rs = struct('ok', false, 'F', NaN, 'df1', NaN, 'df2', NaN, 'p', NaN);
+w = warning('off', 'all'); cleanup = onCleanup(@() warning(w)); %#ok<NASGU>
+try
+    lme = fitlme(T, formula);
+    A = anova(lme, 'DFMethod', 'satterthwaite');
+    i = strcmp(A.Term, term);
+    if any(i)
+        rs = struct('ok', true, 'F', A.FStat(i), 'df1', A.DF1(i), ...
+            'df2', A.DF2(i), 'p', A.pValue(i));
+    end
+catch
+    % Random-slope model unidentifiable / non-convergent (common in short
+    % windows or with very few animals): leave rs.ok = false.
+end
+
+end

analysis/matlab/tdcs_report.m

@@ -0,0 +1,181 @@
+function tdcs_report(S, R, meta, cfg, scenario)
+%TDCS_REPORT Print + persist the tDCS GLM report for one scenario.
+%   TDCS_REPORT(S, R, META, CFG, SCENARIO) prints a three-part report to
+%   the console (DESCRIPTIVES, RAW GLM, INTERPRETATION) and writes the
+%   same text to analysis/matlab/results/<SCENARIO>.txt (the results/
+%   folder is created if it does not already exist).
+%
+%     S         scenario table, from TDCS_SCENARIO_DATA.
+%     R         model results struct, from TDCS_MODELS.
+%     META      scenario metadata struct, from TDCS_SCENARIO_DATA
+%               (.mergeKey, .maxDay, .isUnmerged).
+%     CFG       config struct, from TDCS_CONFIG.
+%     SCENARIO  scenario name (char/string), used only for the report
+%               header and the output filename.
+%
+%   The INTERPRETATION section mirrors verdicts()/classify() in
+%   analysis/tdcs_glm.py and the "Classification logic (unknowns)" /
+%   "Caveats" sections of analysis/METHODS.md: the H2 anchor-check
+%   verdict (IRR/OR + one-sided p + SUPPORTED/not supported), the
+%   per-animal Welch/Mann-Whitney p-values, the unknown-condition
+%   classification (unmerged scenarios only), and the standing caveats
+%   (tiny groups, single-subject classification, count-vs-rate).
+%
+%   See analysis/tdcs_glm.py, analysis/METHODS.md, and
+%   docs/superpowers/plans/2026-07-20-matlab-tdcs-analysis.md (Task 4).
+
+scenario = char(scenario);
+txt = '';
+
+txt = [txt localHeader(sprintf('tDCS GLM report -- scenario: %s', scenario))];
+txt = [txt sprintf('merge key: %s   day window: 0..%d   observations: %d\n', ...
+    meta.mergeKey, meta.maxDay, height(S))];
+
+txt = [txt localDescriptives(S)];
+txt = [txt localRawGLM(R)];
+txt = [txt localInterpretation(R, meta, cfg)];
+
+fprintf('%s', txt);
+
+thisDir = fileparts(mfilename('fullpath'));
+resultsDir = fullfile(thisDir, 'results');
+if ~exist(resultsDir, 'dir')
+    mkdir(resultsDir);
+end
+outFile = fullfile(resultsDir, [scenario '.txt']);
+fid = fopen(outFile, 'w');
+if fid == -1
+    error('tdcs_report:cannotWrite', 'Could not open "%s" for writing.', outFile);
+end
+cleanupObj = onCleanup(@() fclose(fid)); %#ok<NASGU>
+fprintf(fid, '%s', txt);
+
+end
+
+% ------------------------------------------------------------------ header
+function s = localHeader(title)
+bar = repmat('=', 1, 78);
+s = sprintf('%s\n%s\n%s\n', bar, title, bar);
+end
+
+% -------------------------------------------------------------- descriptives
+function s = localDescriptives(S)
+s = localHeader('DESCRIPTIVES');
+h = sprintf('%-20s %8s %10s %13s %10s %8s', ...
+    'group', 'n_subj', 'n_sessions', 'mean_success', 'mean_rate', 'max_day');
+s = [s h sprintf('\n') repmat('-', 1, length(h)) sprintf('\n')];
+
+grps = categories(S.group);
+for i = 1:numel(grps)
+    rows = S.group == grps{i};
+    nSubj = numel(unique(S.subject(rows)));
+    nSessions = sum(rows);
+    meanSuccess = mean(S.success(rows));
+    meanRate = sum(S.success(rows)) / sum(S.total(rows));
+    maxDay = max(S.day(rows));
+    s = [s sprintf('%-20s %8d %10d %13.1f %10.3f %8d\n', ...
+        grps{i}, nSubj, nSessions, meanSuccess, meanRate, maxDay)]; %#ok<AGROW>
+end
+s = [s sprintf('\n')];
+end
+
+% ---------------------------------------------------------------- raw GLM
+function s = localRawGLM(R)
+s = localHeader('(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)');
+s = [s localCleanDisp(R.countGLME) sprintf('\n')];
+
+s = [s localHeader('(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)')];
+s = [s localCleanDisp(R.rateGLME) sprintf('\n')];
+
+s = [s localHeader('(C) LEARNING RATE -- Poisson GLMM (group x day interaction)')];
+s = [s localCleanDisp(R.learnGLME) sprintf('\n')];
+s = [s sprintf('Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:\n')];
+if R.learn.slopeMWUp < 0.05
+    verdict = 'DIFFERENT learning rates';
+else
+    verdict = 'parallel learning (no slope difference detected)';
+end
+s = [s sprintf(['Per-subject OLS slope of success vs day: Box-B2 mean=%.2f, Box-A2 mean=%.2f\n' ...
+    '  Welch two-sided p=%.3f, Mann-Whitney p=%.3f (nB=%d, nA=%d)  -> %s\n'], ...
+    R.learn.meanSlopeB, R.learn.meanSlopeA, R.learn.slopeWelchP, R.learn.slopeMWUp, ...
+    R.learn.nB, R.learn.nA, verdict)];
+s = [s sprintf(['(Reference only: the GLMM group x day_c joint F-test gives p=%.4f, but with just\n' ...
+    ' observation-level DF (df2=%.0f) it is ANTICONSERVATIVE for this few-subject design and is\n' ...
+    ' NOT the basis for the conclusion above.)\n\n'], R.learn.glmeJointP, R.learn.glmeJointDF2)];
+end
+
+function s = localCleanDisp(model)
+%LOCALCLEANDISP Full native model summary (disp(MODEL)) as plain text.
+%   Thin wrapper over the shared TDCS_MODEL_SUMMARY so the count, rate, and
+%   learning GLMMs here render the same verbatim summary used by the LME
+%   reports.
+s = tdcs_model_summary(model);
+end
+
+% ------------------------------------------------------------ interpretation
+function s = localInterpretation(R, meta, cfg)
+s = localHeader('INTERPRETATION');
+s = [s sprintf(['Note: these are subject-level GLMMs (Laplace-approximated ' ...
+    'fitglme), not the\nPython reference''s population-average GEE -- ' ...
+    'directions/magnitudes should agree,\nexact ratios and p-values will ' ...
+    'differ.\n\n'])];
+
+s = [s sprintf('Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)\n')];
+s = [s localH2Line('count/level', R.h2.countIRR, R.h2.countOneSidedP)];
+s = [s localH2Line('rate/level ', R.h2.rateOR, R.h2.rateOneSidedP)];
+
+s = [s sprintf('\nPer-animal (Box-B2 n=%d vs Box-A2 n=%d), pure stats (no GLME):\n', ...
+    R.perAnimal.nB, R.perAnimal.nA)];
+s = [s sprintf('  count (per-subject mean success): Welch two-sided p=%.4f, Mann-Whitney one-sided (B2>A2) p=%.4f\n', ...
+    R.perAnimal.countWelchP, R.perAnimal.countMWUp)];
+s = [s sprintf('  rate  (per-subject pooled success/total): Welch two-sided p=%.4f, Mann-Whitney one-sided (B2>A2) p=%.4f\n', ...
+    R.perAnimal.rateWelchP, R.perAnimal.rateMWUp)];
+
+if meta.isUnmerged
+    s = [s sprintf('\nClassification -- is each UNKNOWN condition A2-like or B2-like?\n')];
+    s = [s sprintf('(ratio >1 = above that anchor; p = differs from that anchor)\n')];
+    for i = 1:numel(cfg.unknowns)
+        unknown = cfg.unknowns{i};
+        fieldName = matlab.lang.makeValidName(unknown);
+        entry = R.classify.(fieldName);
+        s = [s sprintf('\n  %s:\n', unknown)]; %#ok<AGROW>
+        s = [s localClassifyLine('count/level', entry.count)]; %#ok<AGROW>
+        s = [s localClassifyLine('rate/level ', entry.rate)]; %#ok<AGROW>
+    end
+    s = [s sprintf(['\n  NOTE: each unknown has ONLY 1 subject. ''Matches'' means ' ...
+        '''not statistically\n  distinguishable'', weak evidence at n=1, not proof ' ...
+        'of equivalence.\n'])];
+else
+    s = [s sprintf(['\n(No unknown groups -- they were merged into the anchors; ' ...
+        'only the H2 anchor\ncontrast applies.)\n'])];
+end
+
+s = [s sprintf('\n') localHeader('CAVEATS')];
+s = [s sprintf(['  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; ' ...
+    'anchor arms n=3-5\n    depending on merge), and -- in unmerged scenarios -- ' ...
+    'each unknown condition\n    (Electrode-Box-A, Right-Electrode) has only ' ...
+    'n=1 subject. Treat every group\n    comparison here as preliminary.\n'])];
+s = [s sprintf(['  - Single-subject classification: for a 1-subject unknown, ' ...
+    '''matches anchor X''\n    means ''not statistically distinguishable from X'', ' ...
+    'NOT proof of equivalence;\n    inference with a single subject in a group ' ...
+    'is fragile.\n'])];
+s = [s sprintf(['  - Count vs rate: ''success'' alone is a raw count; the rate ' ...
+    'model\n    (success/attempts) is the fairer accuracy comparison when attempt ' ...
+    'counts differ\n    between groups.\n'])];
+end
+
+function s = localH2Line(label, ratio, p)
+tag = 'not supported';
+if ratio > 1 && p < 0.05
+    tag = 'SUPPORTED';
+end
+s = sprintf('  [%s] Box-B2 = %.2fx Box-A2  (one-sided p=%.4f)  -> %s\n', ...
+    label, ratio, p, tag);
+end
+
+function s = localClassifyLine(label, verdict)
+s = sprintf(['    [%s] vs Box-A2: %.2fx p=%.3f   vs Box-B2: %.2fx p=%.3f\n' ...
+    '                 -> %s\n'], ...
+    label, verdict.vsA2.ratio, verdict.vsA2.p, ...
+    verdict.vsB2.ratio, verdict.vsB2.p, verdict.label);
+end

analysis/matlab/tdcs_scenario_data.m

@@ -0,0 +1,118 @@
+function [S, meta] = tdcs_scenario_data(scenario)
+%TDCS_SCENARIO_DATA Build one merge/window scenario from the curated tDCS data.
+%   [S, META] = TDCS_SCENARIO_DATA(SCENARIO) loads the curated 12-animal
+%   table via TDCS_LOAD_DATA, applies the requested group-merge map from
+%   TDCS_CONFIG, restricts to day <= maxDay per the scenario's window
+%   suffix, adds mean-centered day covariates DAY_C / DAY_C2, and recodes
+%   GROUP as a categorical with CFG.REF ('Electrode-Box-B2') as the FIRST
+%   (reference) category via REORDERCATS. S is also written to
+%   analysis/matlab/derived/<SCENARIO>.csv via WRITETABLE (the derived/
+%   folder is created if it does not already exist).
+%
+%   SCENARIO must be one of:
+%     unmerged_full, unmerged_d0_10, mergeA2_full, mergeA2_d0_10,
+%     mergeB2_full, mergeB2_d0_10
+%
+%   The scenario name is split into:
+%     - a merge key: 'unmerged' | 'mergeA2' | 'mergeB2', selecting which
+%       containers.Map from TDCS_CONFIG (if any) to apply to relabel
+%       GROUP. 'unmerged' applies no relabeling (identity) -- it is not
+%       backed by a config map at all, since only the two "unknown"
+%       conditions (Electrode-Box-A, Right-Electrode) are ever merge
+%       targets, and the unmerged scenario reports them unmerged/as-is.
+%     - a day window: '_full' -> maxDay = 26, '_d0_10' -> maxDay = 10.
+%
+%   META is a struct with fields:
+%     .mergeKey    'unmerged' | 'mergeA2' | 'mergeB2'
+%     .maxDay      26 or 10
+%     .isUnmerged  true only when mergeKey == 'unmerged'
+
+[mergeKey, maxDay] = localParseScenario(scenario);
+
+cfg = tdcs_config();
+T = tdcs_load_data();
+
+switch mergeKey
+    case 'unmerged'
+        % Identity: no relabeling. An empty map means isKey(...) is
+        % always false below, so every group passes through unchanged.
+        mergeMap = containers.Map('KeyType', 'char', 'ValueType', 'char');
+    case 'mergeA2'
+        mergeMap = cfg.mergeA2;
+    case 'mergeB2'
+        mergeMap = cfg.mergeB2;
+    case 'mergeNaive'
+        mergeMap = cfg.mergeNaive;
+    otherwise
+        error('tdcs_scenario_data:badScenario', ...
+            'Unrecognized scenario "%s".', scenario);
+end
+
+% Relabel GROUP per the merge map. Groups that are not a key in the map
+% (e.g. Naive, Electrode-Box-A2, Electrode-Box-B2 under mergeA2/mergeB2,
+% or every group under the empty 'unmerged' map) are left unchanged --
+% only the two "unknown" conditions are ever merge-map keys.
+groupCell = cellstr(T.group);
+for i = 1:numel(groupCell)
+    if isKey(mergeMap, groupCell{i})
+        groupCell{i} = mergeMap(groupCell{i});
+    end
+end
+
+% Day window (applied after relabeling, before centering, per the plan).
+dayMask = T.day <= maxDay;
+S = T(dayMask, :);
+groupCell = groupCell(dayMask);
+
+% Recode GROUP as categorical with cfg.ref first (the GLME reference
+% level). Only categories actually present after merge+window survive.
+S.group = categorical(groupCell);
+allCats = categories(S.group);
+assert(ismember(cfg.ref, allCats), 'tdcs_scenario_data:refMissing', ...
+    'Reference group "%s" is absent from scenario "%s".', cfg.ref, char(scenario));
+otherCats = allCats(~strcmp(allCats, cfg.ref));
+S.group = reordercats(S.group, [{cfg.ref}; otherCats]);
+
+% Mean-centered day covariates, computed on the post-window data.
+S.day_c = S.day - mean(S.day);
+S.day_c2 = S.day_c .^ 2;
+
+% Persist the derived scenario table.
+thisDir = fileparts(mfilename('fullpath'));
+derivedDir = fullfile(thisDir, 'derived');
+if ~exist(derivedDir, 'dir')
+    mkdir(derivedDir);
+end
+outFile = fullfile(derivedDir, [char(scenario) '.csv']);
+writetable(S, outFile);
+
+meta = struct();
+meta.mergeKey = mergeKey;
+meta.maxDay = maxDay;
+meta.isUnmerged = strcmp(mergeKey, 'unmerged');
+
+end
+
+function [mergeKey, maxDay] = localParseScenario(scenario)
+%LOCALPARSESCENARIO Split "<mergeKey>_full" / "_d0_10" / "_d0_13" apart.
+scenario = char(scenario);
+if endsWith(scenario, '_full')
+    maxDay = 26;
+    mergeKey = scenario(1:end - numel('_full'));
+elseif endsWith(scenario, '_d0_10')
+    maxDay = 10;
+    mergeKey = scenario(1:end - numel('_d0_10'));
+elseif endsWith(scenario, '_d0_13')
+    maxDay = 13;   % day 13 = last day Box-A2 has data (fair A2-vs-B2 window)
+    mergeKey = scenario(1:end - numel('_d0_13'));
+else
+    error('tdcs_scenario_data:badScenario', ...
+        'Scenario "%s" does not end in "_full", "_d0_10", or "_d0_13".', scenario);
+end
+
+if ~ismember(mergeKey, {'unmerged', 'mergeA2', 'mergeB2', 'mergeNaive'})
+    error('tdcs_scenario_data:badScenario', ...
+        'Scenario "%s" has unrecognized merge key "%s".', scenario, mergeKey);
+end
+
+end

analysis/matlab/tests/tExport.m

@@ -0,0 +1,33 @@
+classdef tExport < matlab.unittest.TestCase
+    %TEXPORT Tests for tdcs_export_all (organized CSV export of all variations).
+
+    methods (Test)
+
+        function testExportTreeAndContents(testCase)
+            outRoot = tempname;
+            files = tdcs_export_all(outRoot);
+            testCase.verifyGreaterThanOrEqual(numel(files), 22);  % 1 + 9 + 3 + 9
+
+            % curated: 12 animals
+            Tc = readtable(fullfile(outRoot, 'curated_all.csv'), 'TextType', 'string');
+            testCase.verifyEqual(numel(unique(Tc.subject)), 12);
+
+            % scenario mergeB2_full: Box-B2 has 5 subjects
+            Sb = readtable(fullfile(outRoot, 'scenarios', 'mergeB2_full.csv'), 'TextType', 'string');
+            nB2 = numel(unique(Sb.subject(Sb.group == "Electrode-Box-B2")));
+            testCase.verifyEqual(nB2, 5);
+
+            % anchor file carries a binary tDCS factor with both levels
+            A = readtable(fullfile(outRoot, 'anchors', 'mergeA2_B2vsA2.csv'));
+            testCase.verifyEqual(sort(unique(A.tDCS))', [0 1]);
+
+            % phases folder holds 3 merges x 3 phases = 9 files, each with dayp
+            ph = dir(fullfile(outRoot, 'phases', '*.csv'));
+            testCase.verifyEqual(numel(ph), 9);
+            P = readtable(fullfile(outRoot, 'phases', 'mergeA2_phase_6-13.csv'));
+            testCase.verifyTrue(ismember('dayp', P.Properties.VariableNames));
+            testCase.verifyGreaterThanOrEqual(min(P.dayp), 0);
+        end
+
+    end
+end