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: matched_current_d0_3
+==============================================================================
+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-B2
+control   (stim=0): Electrode-Box-A2
+N = 6 rats, 24 sessions   raw day coverage: treat 0..3, control 0..3
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              24
+    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
+    185.35    192.42    -86.676          173.35  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF    pValue   
+    {'(Intercept)'}         16.867     4.4554     3.7856    20    0.0011608
+    {'day'        }         6.3667     2.2049     2.8875    20    0.0091051
+    {'stim'       }        -8.9667     6.3009    -1.4231    20      0.17013
+    {'day:stim'   }           10.2     3.1182     3.2711    20    0.0038217
+
+
+    Lower     Upper 
+    7.5728    26.161
+    1.7673    10.966
+    -22.11    4.1769
+    3.6955    16.705
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.9163  
+
+
+    Lower      Upper 
+    0.43116    19.725
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        8.5397      6.1599    11.839
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(20)=  3.27  F(1)=  10.700  p=0.003822
+day (learning)             t(20)=  2.89  F(1)=   8.337  p=0.009105
+stim (main, window start)  t(20)= -1.42  F(1)=   2.025  p=0.1701
+interaction 95% CI: [+3.70, +16.70]
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.003822, slope diff=+10.20)
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.