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experiments-database/analysis/matlab/variations/boxa_b2_d6_10/result.txt
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Experiments DB Dev e1af1af7f4 analysis(matlab): Box-A pooling variations (b2 vs a2+a, b2+a vs a2)
Add make_boxa_variations.m producing 6 variation folders (data.csv + analyze.m
+ result.txt) for the paper LME over windows 0-5, 6-10, 0-10:
  boxa_a2  B2         vs A2 + Box-A   (b2 vs a2+a)
  boxa_b2  B2 + Box-A vs A2           (b2+a vs a2)
plus variations/boxa_summary.csv (residual / Satterthwaite / random-slope
interaction p). Early window (0-5) is obs-level significant (res p~0.03-0.04)
but n.s. under the honest random-slope test (rs p~0.13); later windows n.s.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-23 13:39:01 -04:00

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==============================================================================
VARIATION: boxa_b2_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-A, Electrode-Box-B2
control (stim=0): Electrode-Box-A2
N = 6 rats, 30 sessions raw day coverage: treat 6..10, control 6..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 30
Fixed effects coefficients 4
Random effects coefficients 6
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
231.05 239.46 -109.52 219.05
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 74.2 6.2618 11.85 26 5.5401e-12
{'day' } 2.4 1.8164 1.3213 26 0.1979
{'stim' } 12 7.6691 1.5647 26 0.12974
{'day:stim' } 1.9 2.2246 0.85409 26 0.40085
Lower Upper
61.329 87.071
-1.3336 6.1336
-3.764 27.764
-2.6727 6.4727
Random effects covariance parameters (95% CIs):
Group: rat (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 6.2314
Lower Upper
2.9021 13.38
Group: Error
Name Estimate Lower Upper
{'Res Std'} 8.123 6.1215 10.779
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(26)= 0.85 F(1)= 0.729 p=0.4009 p=0.4015 (df=24)
day (learning) t(26)= 1.32 F(1)= 1.746 p=0.1979 p=0.1989 (df=24)
stim (main, window start) t(26)= 1.56 F(1)= 2.448 p=0.1297 p=0.142 (df=13)
interaction 95% CI: [-2.67, +6.47]
HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.61, p=0.4631
(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=0.4009, slope diff=+1.90)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.