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experiments-database/analysis/matlab/variations/boxa_b2_d0_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_d0_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 = 7 rats, 72 sessions raw day coverage: treat 0..10, control 0..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 72
Fixed effects coefficients 4
Random effects coefficients 7
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
587.74 601.4 -287.87 575.74
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 17.308 5.4187 3.1941 68 0.0021263
{'day' } 8.0028 0.78816 10.154 68 2.9164e-15
{'stim' } 1.8623 7.1264 0.26132 68 0.79463
{'day:stim' } 1.604 0.98592 1.6269 68 0.10838
Lower Upper
6.4953 28.121
6.4301 9.5756
-12.358 16.083
-0.36337 3.5714
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 6.0474
Lower Upper
2.8281 12.931
Group: Error
Name Estimate Lower Upper
{'Res Std'} 12.425 10.46 14.759
effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction) t(68)= 1.63 F(1)= 2.647 p=0.1084 p=0.1084 (df=68)
day (learning) t(68)= 10.15 F(1)=103.100 p=2.916e-15 p=2.368e-15 (df=69)
stim (main, window start) t(68)= 0.26 F(1)= 0.068 p=0.7946 p=0.7968 (df=18)
interaction 95% CI: [-0.36, +3.57]
HONEST LME (per-animal random slope, day|rat): interaction F(1,22.2)=2.05, p=0.1666
(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=0.1084, slope diff=+1.60)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.