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LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_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=7   nrep=120, alpha=0.05

--- fitted on real data ---
stim x log(day) interaction: F(1,55)=9.364  p(resid)=0.003417  p(Satt)=0.003585 (df=49)
  honest per-animal random slope (log-day): F(1,13.5)=7.67  p=0.01548
Cohen's f (interaction, partial eta^2=0.057) = 0.245  (small-medium; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=+19.33, ratSD=9.472, resSD=13.37) ---

 true stim:log(day) = +19.33 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.38            |  0.73  <- observed
  5        |  0.76            |  0.92
  8        |  0.98            |  1.00
  12       |  1.00            |  1.00
  16       |  1.00            |  1.00
  24       |  1.00            |  1.00

 true stim:log(day) = +9.665 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.12            |  0.28  <- observed
  5        |  0.31            |  0.40
  8        |  0.46            |  0.53
  12       |  0.67            |  0.68
  16       |  0.83            |  0.84
  24       |  0.95            |  0.96

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