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LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_5   [metric: # successes (count)]
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model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = # successes (count))
log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
observed groups: stim n=3, control n=8   nrep=120, alpha=0.05

--- fitted on real data ---
stim x log(day) interaction: F(1,61)=8.016  p(resid)=0.006273  p(Satt)=0.006505 (df=54)
  honest per-animal random slope (log-day): F(1,15.5)=6.72  p=0.01995
Cohen's f (interaction, partial eta^2=0.048) = 0.223  (small-medium; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=+18.3, ratSD=8.823, resSD=13.99) ---

 true stim:log(day) = +18.3 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.33            |  0.66  <- observed
  5        |  0.73            |  0.82
  8        |  0.95            |  0.98  <- observed
  12       |  1.00            |  1.00
  16       |  1.00            |  1.00
  24       |  1.00            |  1.00

 true stim:log(day) = +9.151 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.11            |  0.24  <- observed
  5        |  0.23            |  0.31
  8        |  0.37            |  0.43  <- observed
  12       |  0.56            |  0.63
  16       |  0.74            |  0.78
  24       |  0.93            |  0.94

Read per-animal as the honest power; LME matches the paper's power code (optimistic).