analysis(matlab): rate + count outputs, variation batch 3
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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@@ -1,5 +1,5 @@
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==============================================================================
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VARIATION: naive_boxa_d6_13
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VARIATION: naive_boxa_d6_13 [metric: success COUNT]
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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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@@ -60,9 +60,9 @@ effect t(df) / F(df1) p (resid) Satterthwaite: p (df)
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stim x day (interaction) t(69)= 0.63 F(1)= 0.397 p=0.5308 p=0.5309 (df=65)
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day (learning) t(69)= 4.26 F(1)= 18.109 p=6.446e-05 p=6.931e-05 (df=64)
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stim (main, window start) t(69)= 2.21 F(1)= 4.886 p=0.03039 p=0.0412 (df=17)
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interaction 95% CI: [-1.89, +3.64]
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interaction 95% CI: [-1.893, +3.641]
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HONEST LME (per-animal random slope, day|rat): interaction F(1,8.5)=0.16, p=0.6966
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(Satterthwaite DF ~= residual on this random-intercept model; the random-slope
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model above is the honest learning-rate test -- DF collapses toward the animal count.)
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INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.5308, slope diff=+0.87)
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.5308, slope diff=+0.8737)
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Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
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