From 8a18c894dd5d49bbc96be27e08b0ea1dc5fd6940 Mon Sep 17 00:00:00 2001
From: Experiments DB Dev <dev@experiments-db.local>
Date: Wed, 22 Jul 2026 15:45:19 -0400
Subject: [PATCH] feat(matlab): add Satterthwaite DF + honest random-slope test
 to LME reports

Every fitlme-based report (lme_*, paper_*, phase_*, and the variations'
analyze.m) now shows, per effect: residual-DF p, Satterthwaite-DF p, and --
for the interaction -- an HONEST test from a per-animal random-SLOPE model
(day|rat), whose Satterthwaite DF collapses toward the animal count.

New: tdcs_random_slope_interaction.m (shared helper). Wired into tdcs_lme,
tdcs_paper_lme, tdcs_phase_lme, variation_analyze; SUMMARY.csv gains
interaction_p_satt / interaction_p_rs. Regenerated all results/, variations/,
matched-effort outputs.

Key point this surfaces: Satterthwaite ~= residual on the random-INTERCEPT
model (the slope's error is at session level), so it does NOT fix
pseudoreplication; the random-slope model does. Effect: full-range mergeA2
interaction 0.009 -> 0.75 (collapses); unmerge_d0_5 0.015 -> 0.13 (n.s.);
the pooled-control early windows survive honestly (naive_a2_d0_5 0.001 ->
0.028; naive_boxa_d0_5 0.003 -> 0.036). Suite 42/42.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 BACKUP.md                                     |  75 ++++
 N8N_WORKFLOWS.md                              | 293 +++++++++++++++
 analysis/matlab/make_variations.m             |   7 +-
 analysis/matlab/results/lme_mergeA2_d0_10.txt |  16 +-
 analysis/matlab/results/lme_mergeA2_d0_13.txt |  16 +-
 analysis/matlab/results/lme_mergeA2_full.txt  |  16 +-
 analysis/matlab/results/lme_mergeB2_d0_10.txt |  16 +-
 analysis/matlab/results/lme_mergeB2_d0_13.txt |  16 +-
 analysis/matlab/results/lme_mergeB2_full.txt  |  16 +-
 .../matlab/results/lme_unmerged_d0_10.txt     |  16 +-
 .../matlab/results/lme_unmerged_d0_13.txt     |  16 +-
 analysis/matlab/results/lme_unmerged_full.txt |  16 +-
 analysis/matlab/results/paper_mergeA2.txt     |  15 +-
 analysis/matlab/results/paper_mergeB2.txt     |  22 +-
 analysis/matlab/results/paper_mergeNaive.txt  |  22 +-
 .../matlab/results/paper_mergeNaive_d0_10.txt |  15 +-
 .../matlab/results/paper_mergeNaive_d0_13.txt |  15 +-
 analysis/matlab/results/paper_unmerged.txt    |  24 +-
 analysis/matlab/results/phase_mergeA2.txt     |  18 +-
 analysis/matlab/results/phase_mergeB2.txt     |  18 +-
 analysis/matlab/results/phase_mergeNaive.txt  |  18 +-
 analysis/matlab/results/phase_unmerged.txt    |  18 +-
 analysis/matlab/tdcs_lme.m                    |  20 +-
 analysis/matlab/tdcs_lme_report.m             |  24 +-
 analysis/matlab/tdcs_paper_lme.m              |  38 +-
 analysis/matlab/tdcs_phase_lme.m              |  22 +-
 .../matlab/tdcs_random_slope_interaction.m    |  33 ++
 analysis/matlab/tests/tLme.m                  |  21 ++
 analysis/matlab/variation_analyze.m           |  30 +-
 analysis/matlab/variations/SUMMARY.csv        |  42 +--
 .../variations/matched_current_d0_3/analyze.m |  30 +-
 .../matched_current_d0_3/result.txt           |  13 +-
 .../variations/naive_a2_d0_10/analyze.m       |  30 +-
 .../variations/naive_a2_d0_10/result.txt      |  13 +-
 .../variations/naive_a2_d0_13/analyze.m       |  30 +-
 .../variations/naive_a2_d0_13/result.txt      |  13 +-
 .../matlab/variations/naive_a2_d0_5/analyze.m |  32 +-
 .../variations/naive_a2_d0_5/result.txt       |  38 +-
 .../variations/naive_a2_d6_10/analyze.m       |  30 +-
 .../variations/naive_a2_d6_10/result.txt      |  13 +-
 .../variations/naive_a2_d6_13/analyze.m       |  30 +-
 .../variations/naive_a2_d6_13/result.txt      |  13 +-
 .../variations/naive_boxa_d0_10/analyze.m     |  30 +-
 .../variations/naive_boxa_d0_10/result.txt    |  13 +-
 .../variations/naive_boxa_d0_13/analyze.m     |  30 +-
 .../variations/naive_boxa_d0_13/result.txt    |  13 +-
 .../variations/naive_boxa_d0_5/analyze.m      |  30 +-
 .../variations/naive_boxa_d0_5/result.txt     |  13 +-
 .../variations/naive_boxa_d6_10/analyze.m     |  30 +-
 .../variations/naive_boxa_d6_10/result.txt    |  13 +-
 .../variations/naive_boxa_d6_13/analyze.m     |  30 +-
 .../variations/naive_boxa_d6_13/result.txt    |  13 +-
 .../matlab/variations/prev_f_full/analyze.m   |  30 +-
 .../matlab/variations/prev_f_full/result.txt  |  13 +-
 .../variations/right_only_d0_10/analyze.m     |  30 +-
 .../variations/right_only_d0_10/result.txt    |  13 +-
 .../variations/right_only_d0_13/analyze.m     |  30 +-
 .../variations/right_only_d0_13/result.txt    |  13 +-
 .../variations/right_only_d0_5/analyze.m      |  30 +-
 .../variations/right_only_d0_5/result.txt     |  13 +-
 .../variations/right_only_d6_10/analyze.m     |  30 +-
 .../variations/right_only_d6_10/result.txt    |  13 +-
 .../variations/right_only_d6_13/analyze.m     |  30 +-
 .../variations/right_only_d6_13/result.txt    |  13 +-
 .../matlab/variations/unmerge_d0_10/analyze.m |  30 +-
 .../variations/unmerge_d0_10/result.txt       |  13 +-
 .../matlab/variations/unmerge_d0_13/analyze.m |  30 +-
 .../variations/unmerge_d0_13/result.txt       |  13 +-
 .../matlab/variations/unmerge_d0_5/analyze.m  |  30 +-
 .../matlab/variations/unmerge_d0_5/result.txt |  13 +-
 .../matlab/variations/unmerge_d6_10/analyze.m |  30 +-
 .../variations/unmerge_d6_10/result.txt       |  13 +-
 .../matlab/variations/unmerge_d6_13/analyze.m |  30 +-
 .../variations/unmerge_d6_13/result.txt       |  13 +-
 n8n_create_workflows.py                       | 345 ++++++++++++++++++
 scripts/backup.sh                             | 130 +++++++
 scripts/setup_cron.sh                         |  29 ++
 77 files changed, 2066 insertions(+), 345 deletions(-)
 create mode 100644 BACKUP.md
 create mode 100644 N8N_WORKFLOWS.md
 create mode 100644 analysis/matlab/tdcs_random_slope_interaction.m
 create mode 100644 n8n_create_workflows.py
 create mode 100755 scripts/backup.sh
 create mode 100755 scripts/setup_cron.sh

diff --git a/BACKUP.md b/BACKUP.md
new file mode 100644
index 0000000..57076a4
--- /dev/null
+++ b/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
+```
diff --git a/N8N_WORKFLOWS.md b/N8N_WORKFLOWS.md
new file mode 100644
index 0000000..e192824
--- /dev/null
+++ b/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.
diff --git a/analysis/matlab/make_variations.m b/analysis/matlab/make_variations.m
index 4b425bf..381bf53 100644
--- a/analysis/matlab/make_variations.m
+++ b/analysis/matlab/make_variations.m
@@ -74,7 +74,8 @@ for gi = 1:size(groupings, 1)
         try
             r = localRun(fullfile(folder, 'analyze.m'));
             rows(end + 1, :) = {vname, gname, wname, r.nRats, r.nObs, ...
-                r.covEqual, r.interP, r.interEst, r.stimP, r.dayP}; %#ok<AGROW>
+                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'));
@@ -87,8 +88,8 @@ end
 
 % Top-level index of all variations.
 S = cell2table(rows, 'VariableNames', {'variation', 'grouping', 'window', ...
-    'nRats', 'nObs', 'covEqual', 'interaction_p', 'interaction_est', ...
-    'stim_p', 'day_p'});
+    '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);
diff --git a/analysis/matlab/results/lme_mergeA2_d0_10.txt b/analysis/matlab/results/lme_mergeA2_d0_10.txt
index 209c1ed..535b773 100644
--- a/analysis/matlab/results/lme_mergeA2_d0_10.txt
+++ b/analysis/matlab/results/lme_mergeA2_d0_10.txt
@@ -54,11 +54,17 @@ Group: Error
     {'Res Std'}        13.04       11.2     15.183
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(79)=  1.51  F(1)=   2.292  p=0.134
-days (learning)             t(79)= 12.43  F(1)= 154.593  p=2.759e-20
-tDCS (main, at Day 1)       t(79)=  0.80  F(1)=   0.635  p=0.428
+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).
diff --git a/analysis/matlab/results/lme_mergeA2_d0_13.txt b/analysis/matlab/results/lme_mergeA2_d0_13.txt
index 9eeca59..9c3d83a 100644
--- a/analysis/matlab/results/lme_mergeA2_d0_13.txt
+++ b/analysis/matlab/results/lme_mergeA2_d0_13.txt
@@ -54,11 +54,17 @@ Group: Error
     {'Res Std'}        14.123      12.278    16.245
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(94)=  1.36  F(1)=   1.844  p=0.1777
-days (learning)             t(94)= 13.14  F(1)= 172.537  p=5.403e-23
-tDCS (main, at Day 1)       t(94)=  1.12  F(1)=   1.264  p=0.2637
+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).
diff --git a/analysis/matlab/results/lme_mergeA2_full.txt b/analysis/matlab/results/lme_mergeA2_full.txt
index 120a120..2466b1f 100644
--- a/analysis/matlab/results/lme_mergeA2_full.txt
+++ b/analysis/matlab/results/lme_mergeA2_full.txt
@@ -57,11 +57,17 @@ Group: Error
     {'Res Std'}        18.954      16.597    21.644
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(105)= -2.66  F(1)=   7.094  p=0.008953
-days (learning)             t(105)=  9.82  F(1)=  96.388  p=1.573e-16
-tDCS (main, at Day 1)       t(105)=  3.43  F(1)=  11.778  p=0.0008585
+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).
diff --git a/analysis/matlab/results/lme_mergeB2_d0_10.txt b/analysis/matlab/results/lme_mergeB2_d0_10.txt
index b7971b8..c2c2599 100644
--- a/analysis/matlab/results/lme_mergeB2_d0_10.txt
+++ b/analysis/matlab/results/lme_mergeB2_d0_10.txt
@@ -54,11 +54,17 @@ Group: Error
     {'Res Std'}        11.955      10.184    14.034
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(79)=  1.91  F(1)=   3.632  p=0.06032
-days (learning)             t(79)= 10.55  F(1)= 111.334  p=9.547e-17
-tDCS (main, at Day 1)       t(79)=  0.07  F(1)=   0.004  p=0.9478
+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).
diff --git a/analysis/matlab/results/lme_mergeB2_d0_13.txt b/analysis/matlab/results/lme_mergeB2_d0_13.txt
index 29be387..5a2f0c6 100644
--- a/analysis/matlab/results/lme_mergeB2_d0_13.txt
+++ b/analysis/matlab/results/lme_mergeB2_d0_13.txt
@@ -54,11 +54,17 @@ Group: Error
     {'Res Std'}        13.148      11.362    15.215
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(94)=  1.95  F(1)=   3.812  p=0.05385
-days (learning)             t(94)=  9.91  F(1)=  98.208  p=2.855e-16
-tDCS (main, at Day 1)       t(94)=  0.29  F(1)=   0.082  p=0.7757
+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).
diff --git a/analysis/matlab/results/lme_mergeB2_full.txt b/analysis/matlab/results/lme_mergeB2_full.txt
index be7ca25..72a4a27 100644
--- a/analysis/matlab/results/lme_mergeB2_full.txt
+++ b/analysis/matlab/results/lme_mergeB2_full.txt
@@ -57,11 +57,17 @@ Group: Error
     {'Res Std'}        19.354      16.948    22.101
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(105)= -1.43  F(1)=   2.043  p=0.1559
-days (learning)             t(105)=  6.89  F(1)=  47.494  p=4.234e-10
-tDCS (main, at Day 1)       t(105)=  2.45  F(1)=   6.021  p=0.01578
+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).
diff --git a/analysis/matlab/results/lme_unmerged_d0_10.txt b/analysis/matlab/results/lme_unmerged_d0_10.txt
index 87e83fc..aae198b 100644
--- a/analysis/matlab/results/lme_unmerged_d0_10.txt
+++ b/analysis/matlab/results/lme_unmerged_d0_10.txt
@@ -54,11 +54,17 @@ Group: Error
     {'Res Std'}        12.508      10.37    15.086
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(57)=  1.49  F(1)=   2.208  p=0.1428
-days (learning)             t(57)= 10.08  F(1)= 101.556  p=2.832e-14
-tDCS (main, at Day 1)       t(57)=  0.68  F(1)=   0.466  p=0.4974
+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).
diff --git a/analysis/matlab/results/lme_unmerged_d0_13.txt b/analysis/matlab/results/lme_unmerged_d0_13.txt
index 324970d..57077c0 100644
--- a/analysis/matlab/results/lme_unmerged_d0_13.txt
+++ b/analysis/matlab/results/lme_unmerged_d0_13.txt
@@ -54,11 +54,17 @@ Group: Error
     {'Res Std'}        14.676      12.436    17.32
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(66)=  1.65  F(1)=   2.721  p=0.1038
-days (learning)             t(66)=  9.09  F(1)=  82.593  p=3.035e-13
-tDCS (main, at Day 1)       t(66)=  0.95  F(1)=   0.910  p=0.3436
+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).
diff --git a/analysis/matlab/results/lme_unmerged_full.txt b/analysis/matlab/results/lme_unmerged_full.txt
index a6f2630..0ef2900 100644
--- a/analysis/matlab/results/lme_unmerged_full.txt
+++ b/analysis/matlab/results/lme_unmerged_full.txt
@@ -54,11 +54,17 @@ Group: Error
     {'Res Std'}        14.163      11.93    16.813
 
 
-effect                      t (df)         F (df1)       p
-----------------------------------------------------------------------
-days x tDCS (interaction)   t(67)=  1.02  F(1)=   1.041  p=0.3112
-days (learning)             t(67)=  9.20  F(1)=  84.665  p=1.671e-13
-tDCS (main, at Day 1)       t(67)=  1.04  F(1)=   1.086  p=0.3012
+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).
diff --git a/analysis/matlab/results/paper_mergeA2.txt b/analysis/matlab/results/paper_mergeA2.txt
index e89c4c4..2ac32c4 100644
--- a/analysis/matlab/results/paper_mergeA2.txt
+++ b/analysis/matlab/results/paper_mergeA2.txt
@@ -57,13 +57,18 @@ Group: Error
 
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(105)= -2.66  F(1)=   7.094  p=0.008953
-day (learning)             t(105)=  9.82  F(1)=  96.388  p=1.573e-16
-stim (main, Day 1)         t(105)=  3.43  F(1)=  11.778  p=0.0008585
+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.
diff --git a/analysis/matlab/results/paper_mergeB2.txt b/analysis/matlab/results/paper_mergeB2.txt
index b72684c..eae15e5 100644
--- a/analysis/matlab/results/paper_mergeB2.txt
+++ b/analysis/matlab/results/paper_mergeB2.txt
@@ -1,7 +1,8 @@
 ==============================================================================
-PAPER LME REPLICATION -- mergeB2
+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
@@ -56,12 +57,21 @@ Group: Error
 
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(105)= -1.43  F(1)=   2.043  p=0.1559
-day (learning)             t(105)=  6.89  F(1)=  47.494  p=4.234e-10
-stim (main, Day 1)         t(105)=  2.45  F(1)=   6.021  p=0.01578
+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.
diff --git a/analysis/matlab/results/paper_mergeNaive.txt b/analysis/matlab/results/paper_mergeNaive.txt
index 965a056..e58d588 100644
--- a/analysis/matlab/results/paper_mergeNaive.txt
+++ b/analysis/matlab/results/paper_mergeNaive.txt
@@ -1,7 +1,8 @@
 ==============================================================================
-PAPER LME REPLICATION -- mergeNaive
+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
@@ -56,12 +57,21 @@ Group: Error
 
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(166)=  2.70  F(1)=   7.299  p=0.007614
-day (learning)             t(166)= 10.58  F(1)= 111.990  p=2.508e-20
-stim (main, Day 1)         t(166)=  1.27  F(1)=   1.611  p=0.2062
+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.
diff --git a/analysis/matlab/results/paper_mergeNaive_d0_10.txt b/analysis/matlab/results/paper_mergeNaive_d0_10.txt
index ba95638..e5cb90c 100644
--- a/analysis/matlab/results/paper_mergeNaive_d0_10.txt
+++ b/analysis/matlab/results/paper_mergeNaive_d0_10.txt
@@ -56,13 +56,18 @@ Group: Error
 
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(111)=  1.97  F(1)=   3.880  p=0.05134
-day (learning)             t(111)= 15.57  F(1)= 242.567  p=1.081e-29
-stim (main, Day 1)         t(111)=  1.32  F(1)=   1.742  p=0.1895
+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.
diff --git a/analysis/matlab/results/paper_mergeNaive_d0_13.txt b/analysis/matlab/results/paper_mergeNaive_d0_13.txt
index 69f8ade..fcfbf76 100644
--- a/analysis/matlab/results/paper_mergeNaive_d0_13.txt
+++ b/analysis/matlab/results/paper_mergeNaive_d0_13.txt
@@ -56,13 +56,18 @@ Group: Error
 
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(134)=  2.66  F(1)=   7.068  p=0.008801
-day (learning)             t(134)= 15.01  F(1)= 225.182  p=1.766e-30
-stim (main, Day 1)         t(134)=  1.28  F(1)=   1.629  p=0.204
+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.
diff --git a/analysis/matlab/results/paper_unmerged.txt b/analysis/matlab/results/paper_unmerged.txt
index 4c25bb6..81fa275 100644
--- a/analysis/matlab/results/paper_unmerged.txt
+++ b/analysis/matlab/results/paper_unmerged.txt
@@ -1,9 +1,12 @@
 ==============================================================================
-PAPER LME REPLICATION -- unmerged
+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)
@@ -53,12 +56,21 @@ Group: Error
 
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(67)=  1.02  F(1)=   1.041  p=0.3112
-day (learning)             t(67)=  9.20  F(1)=  84.665  p=1.671e-13
-stim (main, Day 1)         t(67)=  1.04  F(1)=   1.086  p=0.3012
+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.
diff --git a/analysis/matlab/results/phase_mergeA2.txt b/analysis/matlab/results/phase_mergeA2.txt
index cb5d9f4..b55d168 100644
--- a/analysis/matlab/results/phase_mergeA2.txt
+++ b/analysis/matlab/results/phase_mergeA2.txt
@@ -3,15 +3,17 @@ PHASED days x tDCS LME -- mergeA2 (Box-B2 vs Box-A2)
 ==============================================================================
 model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
 
-phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
---------------------------------------------------------------------------------------------
-0-5      8   10.54   15.16  1.5e-10   0.693        0.014         +4.62 [+1.00, +8.24]
-6-10     7    3.17    4.83  0.035     0.127        0.390         +1.66 [-2.22, +5.53]
-6-13     7    2.92    4.47  0.00032   0.125        0.118         +1.55 [-0.41, +3.52]
+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]
 
-Note: the interaction p (and CI) use fitlme observation-level DF and are
-ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
-phase carries the Box-B2 faster-acquisition signal; late phases converge.
+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)
 ==============================================================================
diff --git a/analysis/matlab/results/phase_mergeB2.txt b/analysis/matlab/results/phase_mergeB2.txt
index ae4b8c7..3cf832e 100644
--- a/analysis/matlab/results/phase_mergeB2.txt
+++ b/analysis/matlab/results/phase_mergeB2.txt
@@ -3,15 +3,17 @@ PHASED days x tDCS LME -- mergeB2 (Box-B2 vs Box-A2)
 ==============================================================================
 model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
 
-phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
---------------------------------------------------------------------------------------------
-0-5      8    9.86   14.65  2.9e-08   0.399        0.013         +4.79 [+1.06, +8.53]
-6-10     7    2.40    4.80  0.18      0.142        0.253         +2.40 [-1.80, +6.60]
-6-13     7    1.71    4.42  0.083     0.127        0.018         +2.71 [+0.50, +4.93]
+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]
 
-Note: the interaction p (and CI) use fitlme observation-level DF and are
-ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
-phase carries the Box-B2 faster-acquisition signal; late phases converge.
+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)
 ==============================================================================
diff --git a/analysis/matlab/results/phase_mergeNaive.txt b/analysis/matlab/results/phase_mergeNaive.txt
index ab25f10..1a5ec7a 100644
--- a/analysis/matlab/results/phase_mergeNaive.txt
+++ b/analysis/matlab/results/phase_mergeNaive.txt
@@ -3,15 +3,17 @@ PHASED days x tDCS LME -- mergeNaive (Box-B2 vs Box-A2)
 ==============================================================================
 model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
 
-phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
---------------------------------------------------------------------------------------------
-0-5      11    8.26   15.16  1.6e-10   0.852        0.000         +6.90 [+3.40, +10.41]
-6-10     10    5.97    4.83  3.9e-05   0.007        0.584         -1.14 [-5.31, +3.03]
-6-13     10    2.83    4.47  0.00071   0.012        0.196         +1.65 [-0.87, +4.17]
+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]
 
-Note: the interaction p (and CI) use fitlme observation-level DF and are
-ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
-phase carries the Box-B2 faster-acquisition signal; late phases converge.
+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)
 ==============================================================================
diff --git a/analysis/matlab/results/phase_unmerged.txt b/analysis/matlab/results/phase_unmerged.txt
index c4659d1..68100df 100644
--- a/analysis/matlab/results/phase_unmerged.txt
+++ b/analysis/matlab/results/phase_unmerged.txt
@@ -3,15 +3,17 @@ PHASED days x tDCS LME -- unmerged (Box-B2 vs Box-A2)
 ==============================================================================
 model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
 
-phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
---------------------------------------------------------------------------------------------
-0-5      6    9.86   15.22  1.6e-07   0.689        0.015         +5.36 [+1.11, +9.62]
-6-10     5    2.40    4.17  0.23      0.098        0.486         +1.77 [-3.41, +6.95]
-6-13     5    1.71    3.98  0.12      0.101        0.097         +2.27 [-0.44, +4.97]
+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]
 
-Note: the interaction p (and CI) use fitlme observation-level DF and are
-ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
-phase carries the Box-B2 faster-acquisition signal; late phases converge.
+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)
 ==============================================================================
diff --git a/analysis/matlab/tdcs_lme.m b/analysis/matlab/tdcs_lme.m
index 923ac4b..5fc1983 100644
--- a/analysis/matlab/tdcs_lme.m
+++ b/analysis/matlab/tdcs_lme.m
@@ -32,24 +32,32 @@ 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, 'day:tDCS');
-L.day = localTerm(C, A, 'day');
-L.tDCS = localTerm(C, A, 'tDCS');
+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, name)
-%LOCALTERM Pull one term's coefficient (t/df/p) and ANOVA (F/df/p) stats.
+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));
+    '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
diff --git a/analysis/matlab/tdcs_lme_report.m b/analysis/matlab/tdcs_lme_report.m
index 9b8a1bd..549f14d 100644
--- a/analysis/matlab/tdcs_lme_report.m
+++ b/analysis/matlab/tdcs_lme_report.m
@@ -25,11 +25,12 @@ s = [s sprintf('\n')];
 
 s = [s tdcs_model_summary(L.lme, 'fitlme: success ~ day*tDCS + (1|subject)') sprintf('\n')];
 
-s = [s sprintf('%-27s %-14s %-13s %s\n', 'effect', 't (df)', 'F (df1)', 'p')];
-s = [s sprintf('%s\n', repmat('-', 1, 70))];
+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
@@ -66,7 +67,24 @@ fprintf(fid, '%s', s);
 end
 
 function r = localRow(name, e)
-r = sprintf('%-27s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', name, e.df, e.t, e.df1, e.F, e.p);
+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)
diff --git a/analysis/matlab/tdcs_paper_lme.m b/analysis/matlab/tdcs_paper_lme.m
index d2ed67d..cfb35b7 100644
--- a/analysis/matlab/tdcs_paper_lme.m
+++ b/analysis/matlab/tdcs_paper_lme.m
@@ -44,6 +44,7 @@ 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;
@@ -51,24 +52,30 @@ 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, ci, 'stim');
-L.day = localTerm(C, An, ci, 'day');
-L.interaction = localTerm(C, An, ci, 'day:stim');   % MATLAB canonicalizes stim:day -> day:stim
+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, ci, name)
+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, :));
+    '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)
@@ -101,12 +108,13 @@ else
 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 %-13s %-13s %s\n', 'effect', 't (df)', 'F (df1)', 'p')];
-s = [s sprintf('%s\n', repmat('-', 1, 66))];
+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';
@@ -133,7 +141,21 @@ fprintf(fid, '%s', s);
 end
 
 function r = localRow(name, e)
-r = sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', name, e.df, e.t, e.df1, e.F, e.p);
+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)
diff --git a/analysis/matlab/tdcs_phase_lme.m b/analysis/matlab/tdcs_phase_lme.m
index 3eb66f0..6f2274f 100644
--- a/analysis/matlab/tdcs_phase_lme.m
+++ b/analysis/matlab/tdcs_phase_lme.m
@@ -30,8 +30,8 @@ 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('%-9s N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95%% CI]\n', 'phase')];
-s = [s sprintf('%s\n', repmat('-', 1, 92))];
+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)
@@ -42,6 +42,9 @@ for k = 1:numel(phases)
 
     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');
 
@@ -53,22 +56,27 @@ for k = 1:numel(phases)
     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;
 
-    s = [s sprintf('%d-%-6d %d  %6.2f  %6.2f  %-9.2g %-11.3f  %-13.3f %+.2f [%+.2f, %+.2f]\n', ...
+    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.interEst, e.interCI(1), e.interCI(2))]; %#ok<AGROW>
+        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(['\nNote: the interaction p (and CI) use fitlme observation-level DF and are\n' ...
-    'ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early\n' ...
-    'phase carries the Box-B2 faster-acquisition signal; late phases converge.\n'])];
+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];
 
diff --git a/analysis/matlab/tdcs_random_slope_interaction.m b/analysis/matlab/tdcs_random_slope_interaction.m
new file mode 100644
index 0000000..fd4e7bb
--- /dev/null
+++ b/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
diff --git a/analysis/matlab/tests/tLme.m b/analysis/matlab/tests/tLme.m
index d8f2457..941afde 100644
--- a/analysis/matlab/tests/tLme.m
+++ b/analysis/matlab/tests/tLme.m
@@ -123,6 +123,27 @@ classdef tLme < matlab.unittest.TestCase
             testCase.verifyFalse(any(sg.group == 'Naive'));
         end
 
+        function testLmeReportsHaveSatterthwaiteAndHonest(testCase)
+            % Every fitlme report adds Satterthwaite DF and the honest
+            % random-slope interaction test.
+            cfg = tdcs_config();
+            L = tdcs_lme(tdcs_scenario_data('mergeA2_full'), cfg);
+            testCase.verifyTrue(isfield(L.interaction, 'pSatt'));
+            testCase.verifyTrue(isfield(L.interaction, 'dfSatt'));
+            testCase.verifyTrue(isfield(L, 'interRS') && islogical(L.interRS.ok));
+            % The full-range mergeA2 interaction is significant under obs-level
+            % DF but NOT under the honest per-animal random slope (it collapses).
+            testCase.verifyLessThan(L.interaction.p, 0.05);
+            testCase.verifyTrue(L.interRS.ok);
+            testCase.verifyGreaterThan(L.interRS.p, 0.05);
+            % Report text carries the Satterthwaite column and honest block.
+            here = fileparts(fileparts(mfilename('fullpath')));
+            evalc("tdcs_glm('lme_mergeA2_full')");
+            txt = fileread(fullfile(here, 'results', 'lme_mergeA2_full.txt'));
+            testCase.verifyTrue(contains(txt, 'Satterthwaite'));
+            testCase.verifyTrue(contains(txt, 'HONEST LME'));
+        end
+
         function testPaperFormulaReplicatesInteraction(testCase)
             % The paper's exact formula (behavior ~ stim + day + stim:day +
             % (1|rat)) on mergeA2 reproduces the paper's interaction
diff --git a/analysis/matlab/variation_analyze.m b/analysis/matlab/variation_analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variation_analyze.m
+++ b/analysis/matlab/variation_analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/SUMMARY.csv b/analysis/matlab/variations/SUMMARY.csv
index d615b84..d156d26 100644
--- a/analysis/matlab/variations/SUMMARY.csv
+++ b/analysis/matlab/variations/SUMMARY.csv
@@ -1,21 +1,21 @@
-variation,grouping,window,nRats,nObs,covEqual,interaction_p,interaction_est,stim_p,day_p
-unmerge_d0_10,unmerge,d0_10,6,61,1,0.142831730250263,1.55766564374789,0.497389134925754,2.83166776054496e-14
-unmerge_d0_13,unmerge,d0_13,6,70,1,0.1037612192755,1.54665472676189,0.343602740986316,3.0346358823857e-13
-unmerge_d0_5,unmerge,d0_5,6,36,1,0.015149263054839,5.36190476190475,0.688839435920596,1.57055423419133e-07
-unmerge_d6_10,unmerge,d6_10,5,25,1,0.485980598616448,1.76666666666666,0.0975215172907211,0.227259314093653
-unmerge_d6_13,unmerge,d6_13,5,34,1,0.0971009786566259,2.26696773140328,0.101395665857404,0.117493504965036
-right_only_d0_10,right_only,d0_10,7,72,1,0.072180139606096,1.73004431141938,0.715879551990348,5.94289273347193e-16
-right_only_d0_13,right_only,d0_13,7,84,1,0.0480416099228058,1.7001712792199,0.528517264593511,1.17078898575779e-14
-right_only_d0_5,right_only,d0_5,7,42,1,0.00589730538358453,5.30714285714286,0.469556667463413,1.45827052706827e-08
-right_only_d6_10,right_only,d6_10,6,30,1,0.288979620167748,2.425,0.119238243812818,0.200957769624237
-right_only_d6_13,right_only,d6_13,6,42,1,0.0258497485171371,2.75792254325755,0.109557181635453,0.0976080018855909
-naive_a2_d0_10,naive_a2,d0_10,10,104,1,0.125891264174292,1.43371876298297,0.141836960529428,1.59796354184479e-27
-naive_a2_d0_13,naive_a2,d0_13,10,124,1,0.0397967517409217,1.61366204723722,0.153633906173184,2.5469330031931e-28
-naive_a2_d0_5,naive_a2,d0_5,10,59,1,0.00107331575782455,6.96223661591522,0.960550975833492,1.1254222201539e-09
-naive_a2_d6_10,naive_a2,d6_10,9,45,1,0.449922139297732,-1.8,0.00929038977362001,8.02730473236241e-05
-naive_a2_d6_13,naive_a2,d6_13,9,65,1,0.439759713746994,1.15527349799111,0.0165675905421826,0.00123453301903328
-naive_boxa_d0_10,naive_boxa,d0_10,11,115,1,0.194116831474294,1.17792772470162,0.124842156542362,1.7431506758907e-32
-naive_boxa_d0_13,naive_boxa,d0_13,11,138,1,0.0848056366333164,1.30637615965008,0.140502409490992,1.21292542612342e-34
-naive_boxa_d0_5,naive_boxa,d0_5,11,65,1,0.00254470765200637,6.40329932104724,0.881419104187055,2.50618640106882e-11
-naive_boxa_d6_10,naive_boxa,d6_10,10,50,1,0.46817073850354,-1.61904761904762,0.014689379915428,1.87688778586107e-05
-naive_boxa_d6_13,naive_boxa,d6_13,10,73,1,0.530807487326757,0.873689202983884,0.0303916991304334,6.44631091768978e-05
+variation,grouping,window,nRats,nObs,covEqual,interaction_p,interaction_p_satt,interaction_p_rs,interaction_est,stim_p,day_p
+unmerge_d0_10,unmerge,d0_10,6,61,1,0.142831730250263,0.142797566084653,0.247946685371652,1.55766564374789,0.497389134925754,2.83166776054496e-14
+unmerge_d0_13,unmerge,d0_13,6,70,1,0.1037612192755,0.103490077847449,0.222686826261486,1.54665472676189,0.343602740986316,3.0346358823857e-13
+unmerge_d0_5,unmerge,d0_5,6,36,1,0.015149263054839,0.0154996899553134,0.133145634512884,5.36190476190475,0.688839435920596,1.57055423419133e-07
+unmerge_d6_10,unmerge,d6_10,5,25,1,0.485980598616448,0.486366669144796,0.554919025308352,1.76666666666666,0.0975215172907211,0.227259314093653
+unmerge_d6_13,unmerge,d6_13,5,34,1,0.0971009786566259,0.0970128901807668,0.13816578257917,2.26696773140328,0.101395665857404,0.117493504965036
+right_only_d0_10,right_only,d0_10,7,72,1,0.072180139606096,0.0721794737454714,0.124574209202325,1.73004431141938,0.715879551990348,5.94289273347193e-16
+right_only_d0_13,right_only,d0_13,7,84,1,0.0480416099228058,0.0478799998667326,0.102857124294903,1.7001712792199,0.528517264593511,1.17078898575779e-14
+right_only_d0_5,right_only,d0_5,7,42,1,0.00589730538358453,0.00612750828678172,0.0876259332262465,5.30714285714286,0.469556667463413,1.45827052706827e-08
+right_only_d6_10,right_only,d6_10,6,30,1,0.288979620167748,0.289798539517805,0.378365014630273,2.425,0.119238243812818,0.200957769624237
+right_only_d6_13,right_only,d6_13,6,42,1,0.0258497485171371,0.0259134063416617,0.0369743190514673,2.75792254325755,0.109557181635453,0.0976080018855909
+naive_a2_d0_10,naive_a2,d0_10,10,104,1,0.125891264174292,0.126071775330177,0.30932733005532,1.43371876298297,0.141836960529428,1.59796354184479e-27
+naive_a2_d0_13,naive_a2,d0_13,10,124,1,0.0397967517409217,0.0398599319703312,0.0578149015961064,1.61366204723722,0.153633906173184,2.5469330031931e-28
+naive_a2_d0_5,naive_a2,d0_5,10,59,1,0.00107331575782455,0.00115218904921896,0.028152310883297,6.96223661591522,0.960550975833492,1.1254222201539e-09
+naive_a2_d6_10,naive_a2,d6_10,9,45,1,0.449922139297732,0.450525162691322,0.522263670801849,-1.8,0.00929038977362001,8.02730473236241e-05
+naive_a2_d6_13,naive_a2,d6_13,9,65,1,0.439759713746994,0.439915577313227,0.563543413702517,1.15527349799111,0.0165675905421826,0.00123453301903328
+naive_boxa_d0_10,naive_boxa,d0_10,11,115,1,0.194116831474294,0.194284662755081,0.383551190839722,1.17792772470162,0.124842156542362,1.7431506758907e-32
+naive_boxa_d0_13,naive_boxa,d0_13,11,138,1,0.0848056366333164,0.0848864932608599,0.140324990567599,1.30637615965008,0.140502409490992,1.21292542612342e-34
+naive_boxa_d0_5,naive_boxa,d0_5,11,65,1,0.00254470765200637,0.00267934065308424,0.0356145839564805,6.40329932104724,0.881419104187055,2.50618640106882e-11
+naive_boxa_d6_10,naive_boxa,d6_10,10,50,1,0.46817073850354,0.468724498567948,0.534407300553877,-1.61904761904762,0.014689379915428,1.87688778586107e-05
+naive_boxa_d6_13,naive_boxa,d6_13,10,73,1,0.530807487326757,0.530942127564996,0.696589180707779,0.873689202983884,0.0303916991304334,6.44631091768978e-05
diff --git a/analysis/matlab/variations/matched_current_d0_3/analyze.m b/analysis/matlab/variations/matched_current_d0_3/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/matched_current_d0_3/analyze.m
+++ b/analysis/matlab/variations/matched_current_d0_3/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/matched_current_d0_3/result.txt b/analysis/matlab/variations/matched_current_d0_3/result.txt
index f0fc9d2..1eefca1 100644
--- a/analysis/matlab/variations/matched_current_d0_3/result.txt
+++ b/analysis/matlab/variations/matched_current_d0_3/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        8.5397      6.1599    11.839
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(20)=  3.27  F(1)=  10.700  p=0.003822
-day (learning)             t(20)=  2.89  F(1)=   8.337  p=0.009105
-stim (main, window start)  t(20)= -1.42  F(1)=   2.025  p=0.1701
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(20)=  3.27 F(1)= 10.700  p=0.003822    p=0.004244 (df=18)
+day (learning)             t(20)=  2.89 F(1)=  8.337  p=0.009105    p=0.009807 (df=18)
+stim (main, window start)  t(20)= -1.42 F(1)=  2.025  p=0.1701    p=0.1703 (df=20)
 interaction 95% CI: [+3.70, +16.70]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,9.8)=9.76, p=0.01107
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.003822, slope diff=+10.20)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_10/analyze.m b/analysis/matlab/variations/naive_a2_d0_10/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d0_10/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_a2_d0_10/result.txt b/analysis/matlab/variations/naive_a2_d0_10/result.txt
index 2e8e291..3bef4cc 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_10/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        13.78       11.942    15.902
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(100)=  1.54  F(1)=   2.382  p=0.1259
-day (learning)             t(100)= 15.09  F(1)= 227.710  p=1.598e-27
-stim (main, window start)  t(100)=  1.48  F(1)=   2.192  p=0.1418
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(100)=  1.54 F(1)=  2.382  p=0.1259    p=0.1261 (df=95)
+day (learning)             t(100)= 15.09 F(1)=227.710  p=1.598e-27    p=4.059e-27 (df=96)
+stim (main, window start)  t(100)=  1.48 F(1)=  2.192  p=0.1418    p=0.1543 (df=20)
 interaction 95% CI: [-0.41, +3.28]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,9.0)=1.16, p=0.3093
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1259, slope diff=+1.43)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_13/analyze.m b/analysis/matlab/variations/naive_a2_d0_13/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d0_13/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_a2_d0_13/result.txt b/analysis/matlab/variations/naive_a2_d0_13/result.txt
index cf02d4c..91593bb 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_13/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        15.264      13.405    17.381
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(120)=  2.08  F(1)=   4.320  p=0.0398
-day (learning)             t(120)= 14.57  F(1)= 212.424  p=2.547e-28
-stim (main, window start)  t(120)=  1.44  F(1)=   2.062  p=0.1536
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(120)=  2.08 F(1)=  4.320  p=0.0398    p=0.03986 (df=117)
+day (learning)             t(120)= 14.57 F(1)=212.424  p=2.547e-28    p=3.246e-28 (df=119)
+stim (main, window start)  t(120)=  1.44 F(1)=  2.062  p=0.1536    p=0.1655 (df=21)
 interaction 95% CI: [+0.08, +3.15]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,41.7)=3.81, p=0.05781
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.0398, slope diff=+1.61)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_5/analyze.m b/analysis/matlab/variations/naive_a2_d0_5/analyze.m
index ac5dcd5..82da5ed 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d0_5/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -34,7 +50,7 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-y
+
 ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 if pI >= 0.05
     verdict = 'n.s. -- slopes parallel (no differential learning rate)';
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_a2_d0_5/result.txt b/analysis/matlab/variations/naive_a2_d0_5/result.txt
index bcec046..9c0f388 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_5/result.txt
@@ -28,27 +28,41 @@ Model fit statistics:
     489.28    501.74    -238.64          477.28  
 
 Fixed effects coefficients (95% CIs):
-    Name                   Estimate    SE        tStat       DF    pValue        Lower      Upper 
-    {'(Intercept)'}         9.0584     5.0267      1.8021    55      0.077018    -1.0153    19.132
-    {'day'        }         8.2568     1.1279      7.3206    55    1.1254e-09     5.9965    10.517
-    {'stim'       }        0.44958      9.048    0.049688    55       0.96055    -17.683    18.582
-    {'day:stim'   }         6.9622     2.0162      3.4532    55     0.0010733     2.9217    11.003
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}         9.0584     5.0267      1.8021    55      0.077018
+    {'day'        }         8.2568     1.1279      7.3206    55    1.1254e-09
+    {'stim'       }        0.44958      9.048    0.049688    55       0.96055
+    {'day:stim'   }         6.9622     2.0162      3.4532    55     0.0010733
+
+
+    Lower      Upper 
+    -1.0153    19.132
+     5.9965    10.517
+    -17.683    18.582
+     2.9217    11.003
 
 Random effects covariance parameters (95% CIs):
 Group: rat (10 Levels)
-    Name1                  Name2                  Type           Estimate    Lower     Upper 
-    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.6432      5.5002    16.907
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.6432  
+
+
+    Lower     Upper 
+    5.5002    16.907
 
 Group: Error
     Name               Estimate    Lower     Upper 
     {'Res Std'}        12.109      9.9317    14.763
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(55)=  3.45  F(1)=  11.924  p=0.001073
-day (learning)             t(55)=  7.32  F(1)=  53.591  p=1.125e-09
-stim (main, window start)  t(55)=  0.05  F(1)=   0.002  p=0.9606
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(55)=  3.45 F(1)= 11.924  p=0.001073    p=0.001152 (df=49)
+day (learning)             t(55)=  7.32 F(1)= 53.591  p=1.125e-09    p=2.092e-09 (df=49)
+stim (main, window start)  t(55)=  0.05 F(1)=  0.002  p=0.9606    p=0.9609 (df=20)
 interaction 95% CI: [+2.92, +11.00]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,9.7)=6.65, p=0.02815
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.001073, slope diff=+6.96)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d6_10/analyze.m b/analysis/matlab/variations/naive_a2_d6_10/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d6_10/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_a2_d6_10/result.txt b/analysis/matlab/variations/naive_a2_d6_10/result.txt
index f08be3a..8846366 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/result.txt
+++ b/analysis/matlab/variations/naive_a2_d6_10/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        10.552      8.376    13.294
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(41)= -0.76  F(1)=   0.582  p=0.4499
-day (learning)             t(41)=  4.38  F(1)=  19.183  p=8.027e-05
-stim (main, window start)  t(41)=  2.73  F(1)=   7.453  p=0.00929
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(41)= -0.76 F(1)=  0.582  p=0.4499    p=0.4505 (df=36)
+day (learning)             t(41)=  4.38 F(1)= 19.183  p=8.027e-05    p=9.813e-05 (df=36)
+stim (main, window start)  t(41)=  2.73 F(1)=  7.453  p=0.00929    p=0.01541 (df=15)
 interaction 95% CI: [-6.57, +2.97]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,9.0)=0.44, p=0.5223
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4499, slope diff=-1.80)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d6_13/analyze.m b/analysis/matlab/variations/naive_a2_d6_13/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d6_13/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_a2_d6_13/result.txt b/analysis/matlab/variations/naive_a2_d6_13/result.txt
index 6b0ff4c..2368021 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/result.txt
+++ b/analysis/matlab/variations/naive_a2_d6_13/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        12.033      10.004    14.473
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(61)=  0.78  F(1)=   0.605  p=0.4398
-day (learning)             t(61)=  3.39  F(1)=  11.485  p=0.001235
-stim (main, window start)  t(61)=  2.46  F(1)=   6.072  p=0.01657
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(61)=  0.78 F(1)=  0.605  p=0.4398    p=0.4399 (df=58)
+day (learning)             t(61)=  3.39 F(1)= 11.485  p=0.001235    p=0.001274 (df=57)
+stim (main, window start)  t(61)=  2.46 F(1)=  6.072  p=0.01657    p=0.02473 (df=17)
 interaction 95% CI: [-1.82, +4.13]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,7.7)=0.36, p=0.5635
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4398, slope diff=+1.16)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d0_10/analyze.m b/analysis/matlab/variations/naive_boxa_d0_10/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_d0_10/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_boxa_d0_10/result.txt b/analysis/matlab/variations/naive_boxa_d0_10/result.txt
index 206ef7d..a972e49 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_10/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        13.706      11.962    15.704
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(111)=  1.31  F(1)=   1.707  p=0.1941
-day (learning)             t(111)= 16.91  F(1)= 285.824  p=1.743e-32
-stim (main, window start)  t(111)=  1.55  F(1)=   2.392  p=0.1248
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(111)=  1.31 F(1)=  1.707  p=0.1941    p=0.1943 (df=104)
+day (learning)             t(111)= 16.91 F(1)=285.824  p=1.743e-32    p=6.758e-32 (df=106)
+stim (main, window start)  t(111)=  1.55 F(1)=  2.392  p=0.1248    p=0.1361 (df=22)
 interaction 95% CI: [-0.61, +2.96]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,10.1)=0.83, p=0.3836
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1941, slope diff=+1.18)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d0_13/analyze.m b/analysis/matlab/variations/naive_boxa_d0_13/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_d0_13/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_boxa_d0_13/result.txt b/analysis/matlab/variations/naive_boxa_d0_13/result.txt
index 65a3e77..506fa7e 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_13/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        15.18       13.424    17.167
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(134)=  1.74  F(1)=   3.015  p=0.08481
-day (learning)             t(134)= 16.74  F(1)= 280.201  p=1.213e-34
-stim (main, window start)  t(134)=  1.48  F(1)=   2.198  p=0.1405
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(134)=  1.74 F(1)=  3.015  p=0.08481    p=0.08489 (df=129)
+day (learning)             t(134)= 16.74 F(1)=280.201  p=1.213e-34    p=2.161e-34 (df=131)
+stim (main, window start)  t(134)=  1.48 F(1)=  2.198  p=0.1405    p=0.1519 (df=23)
 interaction 95% CI: [-0.18, +2.79]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,25.3)=2.32, p=0.1403
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.08481, slope diff=+1.31)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d0_5/analyze.m b/analysis/matlab/variations/naive_boxa_d0_5/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_d0_5/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_boxa_d0_5/result.txt b/analysis/matlab/variations/naive_boxa_d0_5/result.txt
index 687f673..f8f2c4c 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_5/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        12.477      10.33    15.071
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(61)=  3.15  F(1)=   9.909  p=0.002545
-day (learning)             t(61)=  8.14  F(1)=  66.293  p=2.506e-11
-stim (main, window start)  t(61)=  0.15  F(1)=   0.022  p=0.8814
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(61)=  3.15 F(1)=  9.909  p=0.002545    p=0.002679 (df=54)
+day (learning)             t(61)=  8.14 F(1)= 66.293  p=2.506e-11    p=5.765e-11 (df=54)
+stim (main, window start)  t(61)=  0.15 F(1)=  0.022  p=0.8814    p=0.8822 (df=24)
 interaction 95% CI: [+2.34, +10.47]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,10.5)=5.80, p=0.03561
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.002545, slope diff=+6.40)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d6_10/analyze.m b/analysis/matlab/variations/naive_boxa_d6_10/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_d6_10/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_boxa_d6_10/result.txt b/analysis/matlab/variations/naive_boxa_d6_10/result.txt
index 590e7d3..d33c85c 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d6_10/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        10.142      8.1466    12.627
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(46)= -0.73  F(1)=   0.535  p=0.4682
-day (learning)             t(46)=  4.77  F(1)=  22.779  p=1.877e-05
-stim (main, window start)  t(46)=  2.54  F(1)=   6.429  p=0.01469
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(46)= -0.73 F(1)=  0.535  p=0.4682    p=0.4687 (df=40)
+day (learning)             t(46)=  4.77 F(1)= 22.779  p=1.877e-05    p=2.436e-05 (df=40)
+stim (main, window start)  t(46)=  2.54 F(1)=  6.429  p=0.01469    p=0.02198 (df=16)
 interaction 95% CI: [-6.07, +2.84]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,10.0)=0.41, p=0.5344
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4682, slope diff=-1.62)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d6_13/analyze.m b/analysis/matlab/variations/naive_boxa_d6_13/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_d6_13/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/naive_boxa_d6_13/result.txt b/analysis/matlab/variations/naive_boxa_d6_13/result.txt
index ae22789..869fbd6 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d6_13/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        11.525      9.6827    13.718
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(69)=  0.63  F(1)=   0.397  p=0.5308
-day (learning)             t(69)=  4.26  F(1)=  18.109  p=6.446e-05
-stim (main, window start)  t(69)=  2.21  F(1)=   4.886  p=0.03039
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(69)=  0.63 F(1)=  0.397  p=0.5308    p=0.5309 (df=65)
+day (learning)             t(69)=  4.26 F(1)= 18.109  p=6.446e-05    p=6.931e-05 (df=64)
+stim (main, window start)  t(69)=  2.21 F(1)=  4.886  p=0.03039    p=0.0412 (df=17)
 interaction 95% CI: [-1.89, +3.64]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,8.5)=0.16, p=0.6966
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.5308, slope diff=+0.87)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/prev_f_full/analyze.m b/analysis/matlab/variations/prev_f_full/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/prev_f_full/analyze.m
+++ b/analysis/matlab/variations/prev_f_full/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/prev_f_full/result.txt b/analysis/matlab/variations/prev_f_full/result.txt
index 5a903a8..0383c95 100644
--- a/analysis/matlab/variations/prev_f_full/result.txt
+++ b/analysis/matlab/variations/prev_f_full/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        4.4892      4.0771    4.9429
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(227)=  2.66  F(1)=   7.088  p=0.008315
-day (learning)             t(227)=  8.94  F(1)=  79.838  p=1.43e-16
-stim (main, window start)  t(227)=  0.92  F(1)=   0.839  p=0.3606
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(227)=  2.66 F(1)=  7.088  p=0.008315    p=0.008365 (df=208)
+day (learning)             t(227)=  8.94 F(1)= 79.838  p=1.43e-16    p=2.16e-16 (df=209)
+stim (main, window start)  t(227)=  0.92 F(1)=  0.839  p=0.3606    p=0.3656 (df=37)
 interaction 95% CI: [+0.15, +0.97]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,24.0)=3.01, p=0.09541
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.008315, slope diff=+0.56)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/right_only_d0_10/analyze.m b/analysis/matlab/variations/right_only_d0_10/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/right_only_d0_10/analyze.m
+++ b/analysis/matlab/variations/right_only_d0_10/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/right_only_d0_10/result.txt b/analysis/matlab/variations/right_only_d0_10/result.txt
index 08784d3..a1b04e2 100644
--- a/analysis/matlab/variations/right_only_d0_10/result.txt
+++ b/analysis/matlab/variations/right_only_d0_10/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        11.956      10.063    14.205
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(68)=  1.83  F(1)=   3.336  p=0.07218
-day (learning)             t(68)= 10.55  F(1)= 111.222  p=5.943e-16
-stim (main, window start)  t(68)=  0.37  F(1)=   0.134  p=0.7159
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(68)=  1.83 F(1)=  3.336  p=0.07218    p=0.07218 (df=68)
+day (learning)             t(68)= 10.55 F(1)=111.222  p=5.943e-16    p=4.533e-16 (df=70)
+stim (main, window start)  t(68)=  0.37 F(1)=  0.134  p=0.7159    p=0.7187 (df=19)
 interaction 95% CI: [-0.16, +3.62]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,20.5)=2.56, p=0.1246
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.07218, slope diff=+1.73)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/right_only_d0_13/analyze.m b/analysis/matlab/variations/right_only_d0_13/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/right_only_d0_13/analyze.m
+++ b/analysis/matlab/variations/right_only_d0_13/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/right_only_d0_13/result.txt b/analysis/matlab/variations/right_only_d0_13/result.txt
index f689e31..752106e 100644
--- a/analysis/matlab/variations/right_only_d0_13/result.txt
+++ b/analysis/matlab/variations/right_only_d0_13/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        14.12       12.139    16.425
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(80)=  2.01  F(1)=   4.031  p=0.04804
-day (learning)             t(80)=  9.45  F(1)=  89.224  p=1.171e-14
-stim (main, window start)  t(80)=  0.63  F(1)=   0.401  p=0.5285
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(80)=  2.01 F(1)=  4.031  p=0.04804    p=0.04788 (df=84)
+day (learning)             t(80)=  9.45 F(1)= 89.224  p=1.171e-14    p=7.518e-15 (df=84)
+stim (main, window start)  t(80)=  0.63 F(1)=  0.401  p=0.5285    p=0.5284 (df=84)
 interaction 95% CI: [+0.02, +3.39]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,20.2)=2.92, p=0.1029
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.04804, slope diff=+1.70)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/right_only_d0_5/analyze.m b/analysis/matlab/variations/right_only_d0_5/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/right_only_d0_5/analyze.m
+++ b/analysis/matlab/variations/right_only_d0_5/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/right_only_d0_5/result.txt b/analysis/matlab/variations/right_only_d0_5/result.txt
index 44e70ce..a2f22a5 100644
--- a/analysis/matlab/variations/right_only_d0_5/result.txt
+++ b/analysis/matlab/variations/right_only_d0_5/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        9.9638      7.8829    12.594
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(38)=  2.92  F(1)=   8.511  p=0.005897
-day (learning)             t(38)=  7.17  F(1)=  51.382  p=1.458e-08
-stim (main, window start)  t(38)= -0.73  F(1)=   0.534  p=0.4696
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(38)=  2.92 F(1)=  8.511  p=0.005897    p=0.006128 (df=35)
+day (learning)             t(38)=  7.17 F(1)= 51.382  p=1.458e-08    p=2.321e-08 (df=35)
+stim (main, window start)  t(38)= -0.73 F(1)=  0.534  p=0.4696    p=0.4735 (df=20)
 interaction 95% CI: [+1.62, +8.99]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,7.0)=3.94, p=0.08763
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.005897, slope diff=+5.31)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/right_only_d6_10/analyze.m b/analysis/matlab/variations/right_only_d6_10/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/right_only_d6_10/analyze.m
+++ b/analysis/matlab/variations/right_only_d6_10/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/right_only_d6_10/result.txt b/analysis/matlab/variations/right_only_d6_10/result.txt
index a159886..e952e59 100644
--- a/analysis/matlab/variations/right_only_d6_10/result.txt
+++ b/analysis/matlab/variations/right_only_d6_10/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        8.1802      6.1646    10.855
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(26)=  1.08  F(1)=   1.172  p=0.289
-day (learning)             t(26)=  1.31  F(1)=   1.722  p=0.201
-stim (main, window start)  t(26)=  1.61  F(1)=   2.596  p=0.1192
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(26)=  1.08 F(1)=  1.172  p=0.289    p=0.2898 (df=24)
+day (learning)             t(26)=  1.31 F(1)=  1.722  p=0.201    p=0.2019 (df=24)
+stim (main, window start)  t(26)=  1.61 F(1)=  2.596  p=0.1192    p=0.1297 (df=14)
 interaction 95% CI: [-2.18, +7.03]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.90, p=0.3784
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.289, slope diff=+2.42)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/right_only_d6_13/analyze.m b/analysis/matlab/variations/right_only_d6_13/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/right_only_d6_13/analyze.m
+++ b/analysis/matlab/variations/right_only_d6_13/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/right_only_d6_13/result.txt b/analysis/matlab/variations/right_only_d6_13/result.txt
index ce70ce3..e0a7616 100644
--- a/analysis/matlab/variations/right_only_d6_13/result.txt
+++ b/analysis/matlab/variations/right_only_d6_13/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        7.3669      5.8525    9.2731
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(38)=  2.32  F(1)=   5.379  p=0.02585
-day (learning)             t(38)=  1.70  F(1)=   2.885  p=0.09761
-stim (main, window start)  t(38)=  1.64  F(1)=   2.685  p=0.1096
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(38)=  2.32 F(1)=  5.379  p=0.02585    p=0.02591 (df=38)
+day (learning)             t(38)=  1.70 F(1)=  2.885  p=0.09761    p=0.09766 (df=38)
+stim (main, window start)  t(38)=  1.64 F(1)=  2.685  p=0.1096    p=0.1304 (df=11)
 interaction 95% CI: [+0.35, +5.17]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,21.2)=4.96, p=0.03697
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.02585, slope diff=+2.76)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d0_10/analyze.m b/analysis/matlab/variations/unmerge_d0_10/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/unmerge_d0_10/analyze.m
+++ b/analysis/matlab/variations/unmerge_d0_10/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/unmerge_d0_10/result.txt b/analysis/matlab/variations/unmerge_d0_10/result.txt
index 408e850..d9b1736 100644
--- a/analysis/matlab/variations/unmerge_d0_10/result.txt
+++ b/analysis/matlab/variations/unmerge_d0_10/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        12.508      10.37    15.086
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(57)=  1.49  F(1)=   2.208  p=0.1428
-day (learning)             t(57)= 10.08  F(1)= 101.556  p=2.832e-14
-stim (main, window start)  t(57)=  0.68  F(1)=   0.466  p=0.4974
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(57)=  1.49 F(1)=  2.208  p=0.1428    p=0.1428 (df=57)
+day (learning)             t(57)= 10.08 F(1)=101.556  p=2.832e-14    p=1.931e-14 (df=59)
+stim (main, window start)  t(57)=  0.68 F(1)=  0.466  p=0.4974    p=0.5037 (df=17)
 interaction 95% CI: [-0.54, +3.66]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,13.0)=1.46, p=0.2479
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1428, slope diff=+1.56)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d0_13/analyze.m b/analysis/matlab/variations/unmerge_d0_13/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/unmerge_d0_13/analyze.m
+++ b/analysis/matlab/variations/unmerge_d0_13/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/unmerge_d0_13/result.txt b/analysis/matlab/variations/unmerge_d0_13/result.txt
index 18d3c1c..5de1bb3 100644
--- a/analysis/matlab/variations/unmerge_d0_13/result.txt
+++ b/analysis/matlab/variations/unmerge_d0_13/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        14.676      12.436    17.32
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(66)=  1.65  F(1)=   2.721  p=0.1038
-day (learning)             t(66)=  9.09  F(1)=  82.593  p=3.035e-13
-stim (main, window start)  t(66)=  0.95  F(1)=   0.910  p=0.3436
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(66)=  1.65 F(1)=  2.721  p=0.1038    p=0.1035 (df=70)
+day (learning)             t(66)=  9.09 F(1)= 82.593  p=3.035e-13    p=1.823e-13 (df=70)
+stim (main, window start)  t(66)=  0.95 F(1)=  0.910  p=0.3436    p=0.3434 (df=70)
 interaction 95% CI: [-0.33, +3.42]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,12.8)=1.64, p=0.2227
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1038, slope diff=+1.55)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d0_5/analyze.m b/analysis/matlab/variations/unmerge_d0_5/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/unmerge_d0_5/analyze.m
+++ b/analysis/matlab/variations/unmerge_d0_5/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/unmerge_d0_5/result.txt b/analysis/matlab/variations/unmerge_d0_5/result.txt
index c7586c8..8224622 100644
--- a/analysis/matlab/variations/unmerge_d0_5/result.txt
+++ b/analysis/matlab/variations/unmerge_d0_5/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        10.703      8.3104    13.785
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(32)=  2.57  F(1)=   6.588  p=0.01515
-day (learning)             t(32)=  6.67  F(1)=  44.528  p=1.571e-07
-stim (main, window start)  t(32)= -0.40  F(1)=   0.163  p=0.6888
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(32)=  2.57 F(1)=  6.588  p=0.01515    p=0.0155 (df=30)
+day (learning)             t(32)=  6.67 F(1)= 44.528  p=1.571e-07    p=2.162e-07 (df=30)
+stim (main, window start)  t(32)= -0.40 F(1)=  0.163  p=0.6888    p=0.6906 (df=19)
 interaction 95% CI: [+1.11, +9.62]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=3.02, p=0.1331
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.01515, slope diff=+5.36)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d6_10/analyze.m b/analysis/matlab/variations/unmerge_d6_10/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/unmerge_d6_10/analyze.m
+++ b/analysis/matlab/variations/unmerge_d6_10/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/unmerge_d6_10/result.txt b/analysis/matlab/variations/unmerge_d6_10/result.txt
index f019624..7064129 100644
--- a/analysis/matlab/variations/unmerge_d6_10/result.txt
+++ b/analysis/matlab/variations/unmerge_d6_10/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        8.6289      6.3295    11.764
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(21)=  0.71  F(1)=   0.503  p=0.486
-day (learning)             t(21)=  1.24  F(1)=   1.547  p=0.2273
-stim (main, window start)  t(21)=  1.73  F(1)=   3.008  p=0.09752
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(21)=  0.71 F(1)=  0.503  p=0.486    p=0.4864 (df=20)
+day (learning)             t(21)=  1.24 F(1)=  1.547  p=0.2273    p=0.2279 (df=20)
+stim (main, window start)  t(21)=  1.73 F(1)=  3.008  p=0.09752    p=0.1098 (df=11)
 interaction 95% CI: [-3.41, +6.95]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,5.0)=0.40, p=0.5549
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.486, slope diff=+1.77)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d6_13/analyze.m b/analysis/matlab/variations/unmerge_d6_13/analyze.m
index 5fcbc92..82da5ed 100644
--- a/analysis/matlab/variations/unmerge_d6_13/analyze.m
+++ b/analysis/matlab/variations/unmerge_d6_13/analyze.m
@@ -21,11 +21,27 @@ tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
+As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+
+% Honest test: refit with a per-animal random SLOPE so the interaction DF
+% collapses toward the animal count (guarded -- may not converge in short windows).
+rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
+wst = warning('off', 'all');
+try
+    mr = fitlme(tbl, 'behavior ~ stim + day + stim:day + (day|rat)');
+    Ar = anova(mr, 'DFMethod', 'satterthwaite');
+    ri = strcmp(Ar.Term, 'day:stim');
+    rsF = Ar.FStat(ri); rsDf = Ar.DF2(ri); rsP = Ar.pValue(ri); rsOk = true;
+catch
+end
+warning(wst);
 
 gi = @(t) find(strcmp(C.Name, t), 1);
 ga = @(t) find(strcmp(A.Term, t), 1);
-row = @(nm, t) sprintf('%-26s t(%d)=%6.2f  F(%d)=%8.3f  p=%.4g\n', nm, ...
-    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
+gs = @(t) find(strcmp(As.Term, t), 1);
+row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f)\n', nm, ...
+    C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
+    As.pValue(gs(t)), As.DF2(gs(t)));
 
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
@@ -54,11 +70,18 @@ s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim
 s = [s sprintf('N = %d rats, %d sessions   raw day coverage: treat %d..%d, control %d..%d\n', ...
     numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
 s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
-s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
+s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (resid)', 'Satterthwaite: p (df)', repmat('-', 1, 76))];
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
 s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+if rsOk
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
+else
+    s = [s sprintf('HONEST LME (per-animal random slope, day|rat): did not converge for this window.\n')];
+end
+s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
+    '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
 s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
 s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
@@ -70,5 +93,6 @@ fclose(fid);
 % Machine-readable handoff for the summary table (see make_variations.m).
 VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
     'nObs', height(D), 'interP', pI, 'interEst', eI, ...
+    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
     'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
     'covEqual', abs(maxT - maxC) <= 2);
diff --git a/analysis/matlab/variations/unmerge_d6_13/result.txt b/analysis/matlab/variations/unmerge_d6_13/result.txt
index 47ffce7..9948e0c 100644
--- a/analysis/matlab/variations/unmerge_d6_13/result.txt
+++ b/analysis/matlab/variations/unmerge_d6_13/result.txt
@@ -55,11 +55,14 @@ Group: Error
     {'Res Std'}        7.7328      5.9847    9.9914
 
 
-effect                     t (df)        F (df1)       p
-------------------------------------------------------------------
-stim x day (interaction)   t(30)=  1.71  F(1)=   2.933  p=0.0971
-day (learning)             t(30)=  1.61  F(1)=   2.598  p=0.1175
-stim (main, window start)  t(30)=  1.69  F(1)=   2.856  p=0.1014
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(30)=  1.71 F(1)=  2.933  p=0.0971    p=0.09701 (df=30)
+day (learning)             t(30)=  1.61 F(1)=  2.598  p=0.1175    p=0.1174 (df=30)
+stim (main, window start)  t(30)=  1.69 F(1)=  2.856  p=0.1014    p=0.1269 (df=9)
 interaction 95% CI: [-0.44, +4.97]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,13.9)=2.48, p=0.1382
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
 INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.0971, slope diff=+2.27)
 Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/n8n_create_workflows.py b/n8n_create_workflows.py
new file mode 100644
index 0000000..67a0a3a
--- /dev/null
+++ b/n8n_create_workflows.py
@@ -0,0 +1,345 @@
+"""
+Creates n8n workflow definitions for every entity in the experiments database.
+Each workflow has an Execute Workflow Trigger + Switch (routes by `action` field)
++ one Postgres node per operation. They act as reusable sub-workflows.
+"""
+
+import json, uuid, requests
+
+API   = "http://localhost:5678/api/v1"
+KEY   = "n8n_api_ca07bd874c628ed0f52d9a2e65073960f9f617c5"
+CRED  = {"id": "9iJuyA9iR5KUzmj5", "name": "Experiments DB (PostgreSQL)"}
+HEADS = {"X-N8N-API-KEY": KEY, "Content-Type": "application/json"}
+
+def uid(): return str(uuid.uuid4())
+
+# ── Node builders ──────────────────────────────────────────────────────────────
+
+def trigger_node(x, y):
+    return {
+        "id": uid(), "name": "Trigger",
+        "type": "n8n-nodes-base.executeWorkflowTrigger",
+        "typeVersion": 1.1,
+        "position": [x, y],
+        "parameters": {},
+    }
+
+def switch_node(name, actions, x, y):
+    """Route by $json.action — one output per action string."""
+    rules = []
+    for action in actions:
+        rules.append({
+            "conditions": {
+                "options": {"caseSensitive": False, "leftValue": "", "typeValidation": "loose"},
+                "combinator": "and",
+                "conditions": [{
+                    "leftValue": "={{ $json.action }}",
+                    "rightValue": action,
+                    "operator": {"type": "string", "operation": "equals"},
+                }],
+            },
+            "renameOutput": True,
+            "outputKey": action,
+        })
+    return {
+        "id": uid(), "name": name,
+        "type": "n8n-nodes-base.switch",
+        "typeVersion": 3.2,
+        "position": [x, y],
+        "parameters": {"rules": {"values": rules}, "options": {}},
+    }
+
+def pg_node(name, query, params, x, y):
+    """Postgres executeQuery node. params is a list of n8n expressions for $1,$2,..."""
+    node = {
+        "id": uid(), "name": name,
+        "type": "n8n-nodes-base.postgres",
+        "typeVersion": 2.5,
+        "position": [x, y],
+        "credentials": {"postgres": CRED},
+        "parameters": {
+            "operation": "executeQuery",
+            "query": query,
+            "options": {},
+        },
+    }
+    if params:
+        node["parameters"]["options"]["queryReplacement"] = "={{ [" + ", ".join(params) + "] }}"
+    return node
+
+# ── Connection helpers ──────────────────────────────────────────────────────────
+
+def conn(src, src_idx, dst):
+    """Return a connections dict fragment."""
+    return (src["name"], src_idx, dst["name"])
+
+def build_connections(pairs):
+    """
+    pairs: list of (src_node, output_index, dst_node)
+    Returns n8n connections object.
+    """
+    conns = {}
+    for src, idx, dst in pairs:
+        conns.setdefault(src, {"main": []})
+        while len(conns[src]["main"]) <= idx:
+            conns[src]["main"].append([])
+        conns[src]["main"][idx].append({"node": dst, "type": "main", "index": 0})
+    return conns
+
+# ── Workflow factory ───────────────────────────────────────────────────────────
+
+def make_workflow(name, trigger, switch, pg_nodes, connections):
+    return {
+        "name": name,
+        "nodes": [trigger, switch] + pg_nodes,
+        "connections": connections,
+        "settings": {"executionOrder": "v1"},
+    }
+
+def post_workflow(wf):
+    r = requests.post(f"{API}/workflows", headers=HEADS, json=wf)
+    if r.ok:
+        d = r.json()
+        print(f"  ✓  [{d['id']}]  {wf['name']}")
+    else:
+        print(f"  ✗  {wf['name']}  →  {r.status_code}  {r.text[:200]}")
+
+# ── Layouts ────────────────────────────────────────────────────────────────────
+# Nodes stacked vertically, 220px apart; Switch at col 300, pg nodes at col 560
+
+def pg_y(i): return i * 220 - 440   # center the stack
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 1. EXPERIMENTS
+# ══════════════════════════════════════════════════════════════════════════════
+
+def wf_experiments():
+    t   = trigger_node(60, 0)
+    sw  = switch_node("Route", ["list","get","create","update","delete"], 300, 0)
+    ops = [
+        pg_node("List Experiments",
+            "SELECT id, title, created_at FROM experiments ORDER BY created_at DESC",
+            [], 560, pg_y(0)),
+        pg_node("Get Experiment",
+            "SELECT * FROM experiments WHERE id = $1",
+            ["$json.id"], 560, pg_y(1)),
+        pg_node("Create Experiment",
+            "INSERT INTO experiments (id, title, template, subject_info_template) "
+            "VALUES (gen_random_uuid(), $1, '[]'::jsonb, '[]'::jsonb) RETURNING *",
+            ["$json.title"], 560, pg_y(2)),
+        pg_node("Update Experiment",
+            "UPDATE experiments SET title = $1 WHERE id = $2 RETURNING *",
+            ["$json.title", "$json.id"], 560, pg_y(3)),
+        pg_node("Delete Experiment",
+            "DELETE FROM experiments WHERE id = $1 RETURNING id",
+            ["$json.id"], 560, pg_y(4)),
+    ]
+    pairs = [(t["name"], 0, sw["name"])] + \
+            [(sw["name"], i, ops[i]["name"]) for i in range(len(ops))]
+    return make_workflow("ExpDB · Experiments", t, sw, ops, build_connections(pairs))
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 2. ANIMALS
+# ══════════════════════════════════════════════════════════════════════════════
+
+def wf_animals():
+    t   = trigger_node(60, 0)
+    sw  = switch_node("Route", ["list","get","create","update","delete"], 300, 0)
+    ops = [
+        pg_node("List Animals",
+            "SELECT a.*, COUNT(ds.id)::int AS daily_status_count "
+            "FROM animals a "
+            "LEFT JOIN daily_statuses ds ON ds.animal_id = a.id "
+            "WHERE a.experiment_id = $1 "
+            "GROUP BY a.id ORDER BY a.animal_name",
+            ["$json.experiment_id"], 560, pg_y(0)),
+        pg_node("Get Animal",
+            "SELECT a.*, e.title AS experiment_title "
+            "FROM animals a JOIN experiments e ON e.id = a.experiment_id "
+            "WHERE a.id = $1",
+            ["$json.id"], 560, pg_y(1)),
+        pg_node("Create Animal",
+            "INSERT INTO animals (id, experiment_id, animal_id_string, animal_name, subject_info) "
+            "VALUES (gen_random_uuid(), $1, $2, $3, $4::jsonb) RETURNING *",
+            ["$json.experiment_id", "$json.animal_id_string", "$json.animal_name",
+             "JSON.stringify($json.subject_info ?? {})"], 560, pg_y(2)),
+        pg_node("Update Animal",
+            "UPDATE animals SET animal_id_string=$1, animal_name=$2, subject_info=$3::jsonb "
+            "WHERE id=$4 RETURNING *",
+            ["$json.animal_id_string", "$json.animal_name",
+             "JSON.stringify($json.subject_info ?? {})", "$json.id"], 560, pg_y(3)),
+        pg_node("Delete Animal",
+            "DELETE FROM animals WHERE id = $1 RETURNING id",
+            ["$json.id"], 560, pg_y(4)),
+    ]
+    pairs = [(t["name"], 0, sw["name"])] + \
+            [(sw["name"], i, ops[i]["name"]) for i in range(len(ops))]
+    return make_workflow("ExpDB · Animals", t, sw, ops, build_connections(pairs))
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 3. DAILY STATUSES
+# ══════════════════════════════════════════════════════════════════════════════
+
+def wf_daily_statuses():
+    t   = trigger_node(60, 0)
+    sw  = switch_node("Route", ["list","get","create","update","delete"], 300, 0)
+    ops = [
+        pg_node("List Daily Statuses",
+            "SELECT ds.id, ds.date, ds.experiment_description, ds.vitals, "
+            "ds.treatment, ds.notes, ds.custom_fields, ds.analysis_summary, "
+            "a.animal_name, a.animal_id_string "
+            "FROM daily_statuses ds JOIN animals a ON a.id = ds.animal_id "
+            "WHERE ds.animal_id = $1 ORDER BY ds.date DESC",
+            ["$json.animal_id"], 560, pg_y(0)),
+        pg_node("Get Daily Status",
+            "SELECT ds.*, a.animal_name, a.animal_id_string, "
+            "a.experiment_id, e.title AS experiment_title "
+            "FROM daily_statuses ds "
+            "JOIN animals a ON a.id = ds.animal_id "
+            "JOIN experiments e ON e.id = a.experiment_id "
+            "WHERE ds.id = $1",
+            ["$json.id"], 560, pg_y(1)),
+        pg_node("Create Daily Status",
+            "INSERT INTO daily_statuses "
+            "(id, animal_id, date, experiment_description, vitals, treatment, notes, custom_fields) "
+            "VALUES (gen_random_uuid(), $1, $2::date, $3, $4, $5, $6, $7::jsonb) RETURNING *",
+            ["$json.animal_id", "$json.date", "$json.experiment_description ?? null",
+             "$json.vitals ?? null", "$json.treatment ?? null", "$json.notes ?? null",
+             "JSON.stringify($json.custom_fields ?? {})"], 560, pg_y(2)),
+        pg_node("Update Daily Status",
+            "UPDATE daily_statuses SET "
+            "experiment_description=$1, vitals=$2, treatment=$3, notes=$4, "
+            "custom_fields=$5::jsonb "
+            "WHERE id=$6 RETURNING *",
+            ["$json.experiment_description ?? null", "$json.vitals ?? null",
+             "$json.treatment ?? null", "$json.notes ?? null",
+             "JSON.stringify($json.custom_fields ?? {})", "$json.id"], 560, pg_y(3)),
+        pg_node("Delete Daily Status",
+            "DELETE FROM daily_statuses WHERE id = $1 RETURNING id",
+            ["$json.id"], 560, pg_y(4)),
+    ]
+    pairs = [(t["name"], 0, sw["name"])] + \
+            [(sw["name"], i, ops[i]["name"]) for i in range(len(ops))]
+    return make_workflow("ExpDB · Daily Statuses", t, sw, ops, build_connections(pairs))
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 4. DAILY ANALYSES  (read + summary-push)
+# ══════════════════════════════════════════════════════════════════════════════
+
+def wf_daily_analyses():
+    t   = trigger_node(60, 0)
+    sw  = switch_node("Route", ["list","get","push_summary"], 300, 0)
+    ops = [
+        pg_node("List Analyses",
+            "SELECT id, daily_status_id, created_at, file_name, "
+            "timestamp_col, note_col, total_note_rows, session_end_ts "
+            "FROM daily_analyses WHERE daily_status_id = $1 ORDER BY created_at DESC",
+            ["$json.daily_status_id"], 560, pg_y(0)),
+        pg_node("Get Analysis",
+            "SELECT * FROM daily_analyses WHERE id = $1",
+            ["$json.id"], 560, pg_y(1)),
+        pg_node("Push Analysis Summary",
+            "UPDATE daily_statuses SET analysis_summary = $1::jsonb WHERE id = $2 RETURNING id, analysis_summary",
+            ["JSON.stringify($json.analysis_summary)", "$json.daily_status_id"],
+            560, pg_y(2)),
+    ]
+    pairs = [(t["name"], 0, sw["name"])] + \
+            [(sw["name"], i, ops[i]["name"]) for i in range(len(ops))]
+    return make_workflow("ExpDB · Daily Analyses", t, sw, ops, build_connections(pairs))
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 5. SESSION FILES
+# ══════════════════════════════════════════════════════════════════════════════
+
+def wf_session_files():
+    t   = trigger_node(60, 0)
+    sw  = switch_node("Route", ["list","get"], 300, 0)
+    ops = [
+        pg_node("List Session Files",
+            "SELECT * FROM session_files WHERE daily_status_id = $1 ORDER BY created_at DESC",
+            ["$json.daily_status_id"], 560, pg_y(0)),
+        pg_node("Get Session File",
+            "SELECT * FROM session_files WHERE id = $1",
+            ["$json.id"], 560, pg_y(1)),
+    ]
+    pairs = [(t["name"], 0, sw["name"])] + \
+            [(sw["name"], i, ops[i]["name"]) for i in range(len(ops))]
+    return make_workflow("ExpDB · Session Files", t, sw, ops, build_connections(pairs))
+
+# ══════════════════════════════════════════════════════════════════════════════
+# 6. AUDIT LOGS
+# ══════════════════════════════════════════════════════════════════════════════
+
+def wf_audit_logs():
+    t   = trigger_node(60, 0)
+    sw  = switch_node("Route", ["list_by_record","list_by_table"], 300, 0)
+    ops = [
+        pg_node("List Logs by Record",
+            "SELECT * FROM audit_logs "
+            "WHERE table_name = $1 AND record_id = $2 "
+            "ORDER BY timestamp DESC LIMIT $3",
+            ["$json.table_name", "$json.record_id", "$json.limit ?? 50"],
+            560, pg_y(0)),
+        pg_node("List Logs by Table",
+            "SELECT * FROM audit_logs "
+            "WHERE table_name = $1 "
+            "ORDER BY timestamp DESC LIMIT $2",
+            ["$json.table_name", "$json.limit ?? 100"],
+            560, pg_y(1)),
+    ]
+    pairs = [(t["name"], 0, sw["name"])] + \
+            [(sw["name"], i, ops[i]["name"]) for i in range(len(ops))]
+    return make_workflow("ExpDB · Audit Logs", t, sw, ops, build_connections(pairs))
+
+# ══════════════════════════════════════════════════════════════════════════════
+# BONUS: Cross-subject report — one query to summarise all subjects in an experiment
+# ══════════════════════════════════════════════════════════════════════════════
+
+def wf_reports():
+    t   = trigger_node(60, 0)
+    sw  = switch_node("Route", ["subjects_summary","daily_summary","recent_activity"], 300, 0)
+    ops = [
+        pg_node("Subjects Summary",
+            "SELECT a.id, a.animal_name, a.animal_id_string, a.subject_info, "
+            "COUNT(ds.id)::int AS total_days, "
+            "MAX(ds.date) AS last_entry, "
+            "COUNT(ds.analysis_summary)::int AS days_with_metrics "
+            "FROM animals a "
+            "LEFT JOIN daily_statuses ds ON ds.animal_id = a.id "
+            "WHERE a.experiment_id = $1 "
+            "GROUP BY a.id ORDER BY a.animal_name",
+            ["$json.experiment_id"], 560, pg_y(0)),
+        pg_node("Daily Summary",
+            "SELECT ds.date, a.animal_name, "
+            "ds.analysis_summary->>'total' AS total_attempts, "
+            "ds.analysis_summary->>'success_rate' AS success_rate "
+            "FROM daily_statuses ds JOIN animals a ON a.id = ds.animal_id "
+            "WHERE a.experiment_id = $1 AND ds.analysis_summary IS NOT NULL "
+            "ORDER BY ds.date, a.animal_name",
+            ["$json.experiment_id"], 560, pg_y(1)),
+        pg_node("Recent Activity",
+            "SELECT al.timestamp, al.table_name, al.action, al.record_id, al.changes "
+            "FROM audit_logs al "
+            "ORDER BY al.timestamp DESC LIMIT $1",
+            ["$json.limit ?? 50"], 560, pg_y(2)),
+    ]
+    pairs = [(t["name"], 0, sw["name"])] + \
+            [(sw["name"], i, ops[i]["name"]) for i in range(len(ops))]
+    return make_workflow("ExpDB · Reports", t, sw, ops, build_connections(pairs))
+
+# ── Main ───────────────────────────────────────────────────────────────────────
+
+if __name__ == "__main__":
+    workflows = [
+        wf_experiments(),
+        wf_animals(),
+        wf_daily_statuses(),
+        wf_daily_analyses(),
+        wf_session_files(),
+        wf_audit_logs(),
+        wf_reports(),
+    ]
+    print(f"Creating {len(workflows)} workflows…\n")
+    for wf in workflows:
+        post_workflow(wf)
+    print("\nDone.")
diff --git a/scripts/backup.sh b/scripts/backup.sh
new file mode 100755
index 0000000..a1886f3
--- /dev/null
+++ b/scripts/backup.sh
@@ -0,0 +1,130 @@
+#!/usr/bin/env bash
+# =============================================================================
+# Experiments DB — incremental backup script
+#
+# Usage:
+#   backup.sh daily    – backup only if DB changed since last run; keep 5
+#   backup.sh weekly   – unconditional backup; keep 2
+#
+# Cron (added by setup_cron.sh):
+#   0 2 * * *   /home/sam/docker-images/experiments-database/scripts/backup.sh daily
+#   0 3 * * 0   /home/sam/docker-images/experiments-database/scripts/backup.sh weekly
+# =============================================================================
+
+set -euo pipefail
+
+# ── Config ────────────────────────────────────────────────────────────────────
+MODE="${1:-daily}"
+BACKUP_ROOT="/home/sam/synology/Backups/Experiments-DB-Backup"
+DAILY_DIR="$BACKUP_ROOT/daily"
+WEEKLY_DIR="$BACKUP_ROOT/weekly"
+STATE_FILE="$BACKUP_ROOT/.last_backup_state"
+LOG_FILE="$BACKUP_ROOT/backup.log"
+KEEP_DAILY=5
+KEEP_WEEKLY=2
+
+PG_CONTAINER="experiments_postgres"
+PG_USER="expuser"
+PG_DB="experiments_db"
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a "$LOG_FILE"; }
+
+die() { log "ERROR: $*"; exit 1; }
+
+# Ensure directories exist
+mkdir -p "$DAILY_DIR" "$WEEKLY_DIR"
+
+# Check postgres container is running
+if ! docker inspect --format '{{.State.Running}}' "$PG_CONTAINER" 2>/dev/null | grep -q true; then
+    die "Container $PG_CONTAINER is not running."
+fi
+
+# ── DB state fingerprint ──────────────────────────────────────────────────────
+# Combines row counts from all key tables + latest audit_log timestamp.
+# Changes in any table (insert, update, delete) alter this string.
+get_db_state() {
+    docker exec "$PG_CONTAINER" psql -U "$PG_USER" -d "$PG_DB" -t -A -c "
+        SELECT
+            (SELECT COUNT(*) FROM experiments)      || ',' ||
+            (SELECT COUNT(*) FROM animals)          || ',' ||
+            (SELECT COUNT(*) FROM daily_statuses)   || ',' ||
+            (SELECT COUNT(*) FROM daily_analyses)   || ',' ||
+            (SELECT COUNT(*) FROM session_files)    || ',' ||
+            COALESCE(
+                (SELECT MAX(timestamp)::text FROM audit_logs),
+                'no-audit'
+            )
+        AS fingerprint;
+    " 2>/dev/null | tr -d '[:space:]'
+}
+
+# ── Dump function ─────────────────────────────────────────────────────────────
+run_dump() {
+    local dest="$1"
+    local tmp="${dest}.tmp"
+    log "Dumping → $dest"
+    docker exec "$PG_CONTAINER" pg_dump \
+        -U "$PG_USER" \
+        --format=custom \
+        --compress=9 \
+        "$PG_DB" > "$tmp" \
+        && mv "$tmp" "$dest" \
+        || { rm -f "$tmp"; die "pg_dump failed."; }
+    local size
+    size=$(du -sh "$dest" | cut -f1)
+    log "Backup written ($size): $(basename "$dest")"
+}
+
+# ── Prune old backups ─────────────────────────────────────────────────────────
+prune() {
+    local dir="$1"
+    local keep="$2"
+    local count
+    count=$(ls -1 "$dir"/*.pgdump 2>/dev/null | wc -l)
+    if (( count > keep )); then
+        ls -1t "$dir"/*.pgdump | tail -n "+$((keep + 1))" | while read -r f; do
+            log "Pruning old backup: $(basename "$f")"
+            rm -f "$f"
+        done
+    fi
+}
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+TIMESTAMP=$(date '+%Y%m%d_%H%M%S')
+
+if [[ "$MODE" == "daily" ]]; then
+    log "--- Daily backup check ---"
+    CURRENT_STATE=$(get_db_state)
+
+    if [[ -z "$CURRENT_STATE" ]]; then
+        die "Could not read DB state."
+    fi
+
+    LAST_STATE=$(cat "$STATE_FILE" 2>/dev/null || echo "")
+
+    if [[ "$CURRENT_STATE" == "$LAST_STATE" ]]; then
+        log "No changes detected since last backup. Skipping."
+        exit 0
+    fi
+
+    DEST="$DAILY_DIR/experiments_db_daily_${TIMESTAMP}.pgdump"
+    run_dump "$DEST"
+
+    # Save new state only after successful dump
+    echo "$CURRENT_STATE" > "$STATE_FILE"
+    log "State fingerprint updated."
+
+    prune "$DAILY_DIR" "$KEEP_DAILY"
+    log "Daily backup complete."
+
+elif [[ "$MODE" == "weekly" ]]; then
+    log "--- Weekly backup (unconditional) ---"
+    DEST="$WEEKLY_DIR/experiments_db_weekly_${TIMESTAMP}.pgdump"
+    run_dump "$DEST"
+    prune "$WEEKLY_DIR" "$KEEP_WEEKLY"
+    log "Weekly backup complete."
+
+else
+    die "Unknown mode '$MODE'. Use 'daily' or 'weekly'."
+fi
diff --git a/scripts/setup_cron.sh b/scripts/setup_cron.sh
new file mode 100755
index 0000000..b1048b4
--- /dev/null
+++ b/scripts/setup_cron.sh
@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+# =============================================================================
+# Installs (or updates) cron entries for the experiments DB backup.
+# Safe to run multiple times — replaces existing ExpDB entries.
+# =============================================================================
+
+SCRIPT="$(cd "$(dirname "$0")" && pwd)/backup.sh"
+
+if [[ ! -x "$SCRIPT" ]]; then
+    chmod +x "$SCRIPT"
+fi
+
+# Remove any previous ExpDB backup lines, then append fresh ones
+TMPFILE=$(mktemp)
+crontab -l 2>/dev/null | grep -v "# ExpDB-backup" > "$TMPFILE" || true
+
+cat >> "$TMPFILE" <<EOF
+
+# ExpDB-backup — daily change-detect backup at 02:00
+0 2 * * * $SCRIPT daily >> /home/sam/synology/Backups/Experiments-DB-Backup/backup.log 2>&1 # ExpDB-backup
+# ExpDB-backup — weekly unconditional backup every Sunday at 03:00
+0 3 * * 0 $SCRIPT weekly >> /home/sam/synology/Backups/Experiments-DB-Backup/backup.log 2>&1 # ExpDB-backup
+EOF
+
+crontab "$TMPFILE"
+rm -f "$TMPFILE"
+
+echo "Cron entries installed:"
+crontab -l | grep "ExpDB-backup"