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LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_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(1,46)=0.613  p(resid)=0.4376  p(Satt)=0.4382 (df=40)
  honest per-animal random slope (log-day): F(1,10.0)=0.43  p=0.5247
Cohen's f (interaction, partial eta^2=0.005) = 0.069  (small; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=-15.28, ratSD=11.24, resSD=10.1) ---

 true stim:log(day) = -15.28 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.06            |  0.08  <- observed
  5        |  0.07            |  0.07
  8        |  0.18            |  0.21
  12       |  0.28            |  0.30
  16       |  0.30            |  0.35
  24       |  0.45            |  0.49

 true stim:log(day) = -7.64 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.03            |  0.09  <- observed
  5        |  0.07            |  0.07
  8        |  0.07            |  0.11
  12       |  0.07            |  0.08
  16       |  0.10            |  0.11
  24       |  0.13            |  0.14

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