==============================================================================
VARIATION: matched_current_d0_3   [metric: success COUNT]
==============================================================================
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 (resid)    Satterthwaite: p (df)
----------------------------------------------------------------------------
stim x day (interaction)   t(20)=  3.27 F(1)= 10.700  p=0.003822    p=0.004244 (df=18)
day (learning)             t(20)=  2.89 F(1)=  8.337  p=0.009105    p=0.009807 (df=18)
stim (main, window start)  t(20)= -1.42 F(1)=  2.025  p=0.1701    p=0.1703 (df=20)
interaction 95% CI: [+3.695, +16.7]
HONEST LME (per-animal random slope, day|rat): interaction F(1,9.8)=9.76, p=0.01107
  (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=0.003822, slope diff=+10.2)
Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
