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LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_10   [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(1,100)=4.633  p(resid)=0.03378  p(Satt)=0.03391 (df=95)
  honest per-animal random slope (log-day): F(1,7.2)=2.24  p=0.177
Cohen's f (interaction, partial eta^2=0.010) = 0.101  (small-medium; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=+9.586, ratSD=8.17, resSD=14.84) ---

 true stim:log(day) = +9.586 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.23            |  0.44  <- observed
  5        |  0.57            |  0.68
  8        |  0.83            |  0.86
  12       |  0.97            |  0.98
  16       |  0.99            |  1.00
  24       |  1.00            |  1.00

 true stim:log(day) = +4.793 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.07            |  0.10  <- observed
  5        |  0.11            |  0.17
  8        |  0.30            |  0.37
  12       |  0.34            |  0.38
  16       |  0.62            |  0.64
  24       |  0.75            |  0.78

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