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VARIATION: prev_f_full
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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)

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FULL MODEL SUMMARY -- fitlme
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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 (resid)    Satterthwaite: p (df)
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stim x day (interaction)   t(227)=  2.66 F(1)=  7.088  p=0.008315    p=0.008365 (df=208)
day (learning)             t(227)=  8.94 F(1)= 79.838  p=1.43e-16    p=2.16e-16 (df=209)
stim (main, window start)  t(227)=  0.92 F(1)=  0.839  p=0.3606    p=0.3656 (df=37)
interaction 95% CI: [+0.15, +0.97]
HONEST LME (per-animal random slope, day|rat): interaction F(1,24.0)=3.01, p=0.09541
  (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 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.