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feat(matlab): windowed paper-LME replication for mergeNaive (d0_10, d0_13)

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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>
This commit is contained in:
Experiments DB Dev
2026-07-20 17:49:09 -04:00
parent 0e9263b958
commit 963fd889b2
9 changed files with 532 additions and 16 deletions
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analysis/matlab/results/paper_mergeNaive_d0_13.txt
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==============================================================================
PAPER LME REPLICATION -- mergeNaive (days 0-13)
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
behavior = successful reaches (COUNT per session)
N = 11 rats, 138 sessions (day raw; day 0 = paper "Day 1")
day coverage: stim(B2) 0..13, control(A2) 0..13
(Equal day coverage -- the stim:day interaction over this window is NOT
confounded by the full-range coverage imbalance.)
==============================================================================
FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 138
Fixed effects coefficients 4
Random effects coefficients 11
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
1164.9 1182.4 -576.44 1152.9
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 16.254 4.2865 3.792 134 0.00022519
{'day' } 6.4041 0.42677 15.006 134 1.7662e-30
{'stim' } 8.9845 7.0391 1.2764 134 0.20403
{'day:stim' } 1.7992 0.67672 2.6587 134 0.008801
Lower Upper
7.7764 24.732
5.5601 7.2482
-4.9377 22.907
0.46073 3.1376
Random effects covariance parameters (95% CIs):
Group: rat (11 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 8.1481
Lower Upper
4.7633 13.938
Group: Error
Name Estimate Lower Upper
{'Res Std'} 14.825 13.109 16.766
effect t (df) F (df1) p
------------------------------------------------------------------
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
interaction 95% CI: [+0.46, +3.14]
INTERPRETATION
- stim x day interaction: SIGNIFICANT (p=0.0088, slope diff=+1.80) -> tDCS (Box-B2) improves FASTER -- benefit accumulates over training.
- stim main effect on Day 1 (our day 0): n.s. (p=0.2040) -> groups are comparable on Day 1.
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.