analysis(matlab): matched-effort comparison (prev _f full vs current 0-3)

Add make_matched_effort.m: pick the current-study day cutoff whose per-animal cumulative attempts best match the previous (_f) study's full-span total (~405 attempts/animal -> current days 0-3), then fit the paper LME on both. Two variation folders (data.csv + analyze.m + result.txt): variations/prev_f_full/ b2_f vs a2_f, days 0-9 (24 rats) variations/matched_current_d0_3/ Box-B2 vs Box-A2, 0-3 (6 rats) At matched cumulative effort both show a significant positive stim x day interaction (prev p=0.008 +0.56/day; current-0-3 p=0.004 +10.2/day) -- the tDCS acceleration replicates at equal practice, though the count slopes are not directly comparable across datasets given differing attempts/session. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-22 14:39:15 -04:00
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+==============================================================================
+VARIATION: prev_f_full
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): b2_f
+control   (stim=0): a2_f
+N = 24 rats, 231 sessions   raw day coverage: treat 0..9, control 0..9
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             231
+    Fixed effects coefficients           4
+    Random effects coefficients         24
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    1416.2    1436.9    -702.11          1404.2  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue    
+    {'(Intercept)'}         7.7376      1.4647    5.2828    227    2.9782e-07
+    {'day'        }         1.3487     0.15094    8.9352    227    1.4298e-16
+    {'stim'       }         1.8961      2.0698    0.9161    227       0.36059
+    {'day:stim'   }        0.56023     0.21043    2.6623    227     0.0083151
+
+
+    Lower      Upper  
+     4.8515     10.624
+     1.0513     1.6461
+    -2.1823     5.9745
+    0.14559    0.97488
+
+Random effects covariance parameters (95% CIs):
+Group: rat (24 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        4.3164  
+
+
+    Lower     Upper 
+    3.1535    5.9081
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        4.4892      4.0771    4.9429
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(227)=  2.66  F(1)=   7.088  p=0.008315
+day (learning)             t(227)=  8.94  F(1)=  79.838  p=1.43e-16
+stim (main, window start)  t(227)=  0.92  F(1)=   0.839  p=0.3606
+interaction 95% CI: [+0.15, +0.97]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.008315, slope diff=+0.56)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.