Experiments DB Dev
969c0205e8
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>
66 lines
2.5 KiB
Plaintext
66 lines
2.5 KiB
Plaintext
==============================================================================
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VARIATION: naive_a2_d6_10
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==============================================================================
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model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
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day = training day within window (0 = first analyzed day)
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treatment (stim=1): Electrode-Box-B2
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control (stim=0): Electrode-Box-A2, Naive
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N = 9 rats, 45 sessions raw day coverage: treat 6..10, control 6..10
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(equal day coverage over this window)
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==============================================================================
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FULL MODEL SUMMARY -- fitlme
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==============================================================================
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Linear mixed-effects model fit by ML
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Model information:
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Number of observations 45
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Fixed effects coefficients 4
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Random effects coefficients 9
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Covariance parameters 2
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Formula:
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behavior ~ 1 + day*stim + (1 | rat)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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368.51 379.35 -178.25 356.51
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 62.1 5.5902 11.109 41 6.1328e-14
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{'day' } 5.9667 1.3623 4.3798 41 8.0273e-05
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{'stim' } 26.433 9.6824 2.73 41 0.0092904
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{'day:stim' } -1.8 2.3596 -0.76285 41 0.44992
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Lower Upper
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50.81 73.39
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3.2154 8.7179
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6.8793 45.987
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-6.5653 2.9653
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Random effects covariance parameters (95% CIs):
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Group: rat (9 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 10.986
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Lower Upper
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6.3454 19.02
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 10.552 8.376 13.294
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(41)= -0.76 F(1)= 0.582 p=0.4499
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day (learning) t(41)= 4.38 F(1)= 19.183 p=8.027e-05
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stim (main, window start) t(41)= 2.73 F(1)= 7.453 p=0.00929
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interaction 95% CI: [-6.57, +2.97]
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INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4499, slope diff=-1.80)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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