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LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_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=6   nrep=120, alpha=0.05

--- fitted on real data ---
stim x log(day) interaction: F(1,41)=0.662  p(resid)=0.4205  p(Satt)=0.4211 (df=36)
  honest per-animal random slope (log-day): F(1,9.0)=0.46  p=0.5139
Cohen's f (interaction, partial eta^2=0.006) = 0.076  (small; f: .10 small, .25 medium, .40 large)

--- power simulation (log-day ground truth: stim:logday=-16.93, ratSD=10.99, resSD=10.51) ---

 true stim:log(day) = -16.93 (100% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.07            |  0.12  <- observed
  5        |  0.07            |  0.07
  8        |  0.20            |  0.27
  12       |  0.30            |  0.34
  16       |  0.34            |  0.36
  24       |  0.51            |  0.53

 true stim:log(day) = -8.464 (50% of observed)
  N/group  | per-animal power | LME power
  ------------------------------------------
  3        |  0.03            |  0.07  <- observed
  5        |  0.07            |  0.07
  8        |  0.08            |  0.11
  12       |  0.07            |  0.08
  16       |  0.11            |  0.12
  24       |  0.13            |  0.14

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