Add make_variations.m + variation_analyze.m generating 20 self-contained
subfolders under analysis/matlab/variations/, one per grouping x day-window,
each with exactly three files: data.csv (curated subset), analyze.m (a simple
standalone script fitting behavior ~ stim + day + stim:day + (1|rat) on the
success COUNT), and result.txt (its output). Plus variations/SUMMARY.csv.
Groupings (stim=1 / stim=0, other groups dropped):
unmerge B2 vs A2
right_only B2+Right vs A2 (Box-A dropped)
naive_a2 B2 vs A2+Naive (Right, Box-A dropped)
naive_boxa B2 vs A2+Naive+Box-A (Right dropped)
Windows: 0-10, 0-13, 0-5, 6-10, 6-13. All 20 have equal day coverage.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Make tdcs_paper_lme window-aware (windowKey 'full'|'d0_10'|'d0_13',
default 'full' -> unchanged filename). Windowed runs give the two anchor
arms equal day coverage, so the stim:day interaction is not confounded by
the full-range coverage imbalance; the report now states whether coverage
is equal and adds an INTERPRETATION block. Add switch cases
paper_mergeNaive_d0_10 / _d0_13 and include them in run_all.
Finding: unlike mergeA2 (fair-window interaction n.s.), the pooled-naive
control keeps the interaction significant in the fair d0_13 window
(F(1)=7.07, p=0.0088, +1.80 [+0.46,+3.14]; equal on Day 1 p=0.20),
reproducing the paper without the coverage confound; d0_10 is borderline
(p=0.051).
Tests: tLme asserts equal coverage / no warning / one full summary for
both windows and a significant positive d0_13 interaction; bad windowKey
errors. Suite 41/41.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add tdcs_model_summary as the single place that renders a fitted model
verbatim (disp(model) with the Command-Window <strong> markup stripped),
and route every report through it so all scenarios emit the complete
model-fitting output:
- tdcs_report: adds the learning-rate GLMM's full summary (count/rate
already had theirs); localCleanDisp now delegates to the shared helper.
- tdcs_lme_report / tdcs_paper_lme: embed the full fitlme summary before
the curated effect table.
- tdcs_phase_lme: appends each phase's full fitlme summary.
Tests: tReport asserts 3 native summaries in a GLMM report; tLme asserts
the lme_*/paper_*/phase_* reports embed 1/1/3 summaries with markup
stripped. Suite 39/39.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Document Box-A2 as an a-priori sham/sensory control and the two-tier
analysis it implies: B2-vs-A2 as the primary sensation-controlled contrast,
and the pooled-control mergeNaive grouping as a confirmatory power-boost
licensed by A2/Naive exchangeability (with the TOST caveat that the
exchangeability is supported but not proven at n=3-4).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds cfg.mergeNaive and scenario/paper/phase/power support. This pooled grouping
is the closest replication of the paper's full pattern: paper LME interaction
F(1)=7.30, p=0.008 (paper 7.12/0.008), stim main n.s. (p=0.21 = equal on Day 1),
strong day effect; the overall tDCS advantage is significant at the cluster-honest
per-animal level (rate p=0.003) and well powered. Adds a grouping test. Suite 38/38.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds tdcs_paper_lme replicating the paper's exact formula/variable names (paper_*
switch cases): mergeA2 reproduces the interaction F(1)=7.09 vs 7.12, p=0.009 vs
0.008, with a coverage-confound warning. Rewrites tdcs_power_sim to fit that same
formula (interaction term canonicalized to day:stim). Adds a replication test.
Suite 37/37.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds make_figure (2-panel learning curves + per-phase slopes, colorblind-safe
Box-B2/Box-A2 palette) -> results/figure_learning.png; analysis/writeup.md
(figure + methods + results summary); and tdcs_export_all/run_export exporting
every data variation into an organized export/ tree (curated_all, scenarios/9,
anchors/3 with tDCS factor, phases/9 + MANIFEST). Adds tExport tests. Suite 36/36.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds tdcs_phase_lme (same LME refit within learning phases 0-5/6-10/6-13, with
per-phase slopes + interaction 95% CI) as phase_* switch cases, and
tdcs_power_sim (Monte-Carlo power for the early-phase interaction, scored by the
cluster-honest per-animal test and the LME test) + run_power. Findings: the
Box-B2 faster-acquisition signal is in the early phase; at n=3-5/group honest
power is 0.3-0.7 even at the observed effect (need ~8/group if effect is as
observed, ~20/group if half). Adds tPhasePower tests. Suite 35/35.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Box-A2 has no data past ~day 13, so the full-range days x tDCS interaction
extrapolates A2's slope and is confounded (spuriously significant, wrong sign).
Add the _d0_13 window (equal anchor coverage) and a report warning when the two
groups' day coverage is unequal. In the fair 0-13 window the interaction is n.s.
with a POSITIVE slope diff and the Day-1 tDCS effect is n.s. -- matching the
paper's 'equal on Day 1' direction (underpowered at n=8). Suite 32/32.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The model already evaluates the tDCS main effect at day 0, which is the paper's
'Day 1'; relabel the report/docstring to state the equivalence explicitly so the
'equal on Day 1' comparison is unambiguous. No model change (re-indexing day
would move the main-effect evaluation point off Day 1). Suite 31/31.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds 6 switch cases replicating the paper's linear mixed model
success ~ day * tDCS + (1|subject) on the Box-B2(tDCS=1) vs Box-A2(tDCS=0)
arm, reporting the days x tDCS interaction, days, and tDCS effects as t/F/p.
mergeA2_full reproduces the paper's interaction (F=7.09 vs 7.12, p=0.009 vs
0.008); interpretation is sign-aware and honestly notes our data diverges from
the paper (Box-B2 ahead on day 1, gap narrows -- not 'equal on day 1,
accumulates'). Adds tLme tests. Suite 31/31.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
fitglme coefTest only offers observation-level DF (df2~=178), which overstates
the group x day interaction significance for this few-subject design (p~=0 in
every scenario). Replace the reported learning-rate result with a cluster-honest
per-animal slope test (per-subject OLS slope of success vs day, Welch + MWU,
B2 vs A2) -- now correctly parallel in all 6 scenarios, matching the Python GEE.
The GLMM F-test is kept as a flagged reference only. Adds a regression test.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Poisson GEE / binomial rate / learning-rate models, subject random-intercept
sensitivity, unknown-group classification and merge scenarios, plus METHODS.md
and learning-curve plots. Data reconstructed from the experiment DB and
verified against the exported matrix.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Final code review of the configurable-data-export feature: ExperimentDetail
was forwarding its on-page groupField state (defaults to '__name__') into
ExportDataModal, leaking a clustering default into the default (untouched)
export. Now passes the raw saved localStorage value, defaulting to
'__none__'. The modal also now validates the incoming groupField against
'__none__'/'__name__'/'__id__'/active subjectTemplate fields and coerces to
'__none__' otherwise, guarding against stale/deleted subject_info fields.
Plus a stale module comment fix and optional-chaining consistency fix in
listSubjectMembers.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Client-side CSV export on the experiment page: pick a daily parameter
(session metrics or any daily-record field) and download a date×subject matrix.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
samsam2610
Experiments DB Dev