diff --git a/analysis/matlab/variations/matched_current_d0_3/analyze.m b/analysis/matlab/variations/matched_current_d0_3/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/matched_current_d0_3/analyze.m
+++ b/analysis/matlab/variations/matched_current_d0_3/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/matched_current_d0_3/learning_curve.png b/analysis/matlab/variations/matched_current_d0_3/learning_curve.png
index ff1a27d..04c2af3 100644
Binary files a/analysis/matlab/variations/matched_current_d0_3/learning_curve.png and b/analysis/matlab/variations/matched_current_d0_3/learning_curve.png differ
diff --git a/analysis/matlab/variations/matched_current_d0_3/learning_curve_rate.png b/analysis/matlab/variations/matched_current_d0_3/learning_curve_rate.png
new file mode 100644
index 0000000..81a1872
Binary files /dev/null and b/analysis/matlab/variations/matched_current_d0_3/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/matched_current_d0_3/logpower_result.txt b/analysis/matlab/variations/matched_current_d0_3/logpower_result.txt
index 00090ba..9ede5ad 100644
--- a/analysis/matlab/variations/matched_current_d0_3/logpower_result.txt
+++ b/analysis/matlab/variations/matched_current_d0_3/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- matched_current_d0_3
+LOG-DAY MODEL + COHEN'S f + POWER -- matched_current_d0_3   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+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=3   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,20)=7.043  p(resid)=0.01524  p(Satt)=0.0139 (df
   honest per-animal random slope (log-day): F(1,18.5)=7.24  p=0.01475
 Cohen's f (interaction, partial eta^2=0.102) = 0.336  (medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+20.79, ratSD=0.00, resSD=9.99) ---
+--- power simulation (log-day ground truth: stim:logday=+20.79, ratSD=0, resSD=9.991) ---
 
  true stim:log(day) = +20.79 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.102) = 0.336  (medium; f: .10 small, .25
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +10.40 (50% of observed)
+ true stim:log(day) = +10.4 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.11            |  0.23  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.102) = 0.336  (medium; f: .10 small, .25
   16       |  0.79            |  0.84
   24       |  0.95            |  0.97
 
-Read the per-animal column as the honest power; the LME column matches the
-paper's power code (anova interaction p, observation-level DF) and is optimistic.
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/matched_current_d0_3/logpower_result_rate.txt b/analysis/matlab/variations/matched_current_d0_3/logpower_result_rate.txt
new file mode 100644
index 0000000..4e180af
--- /dev/null
+++ b/analysis/matlab/variations/matched_current_d0_3/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- matched_current_d0_3   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
+observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,20)=7.974  p(resid)=0.01049  p(Satt)=0.01125 (df=18)
+  honest per-animal random slope (log-day): F(1,18.3)=7.99  p=0.01106
+Cohen's f (interaction, partial eta^2=0.157) = 0.431  (large; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1785, ratSD=0.01747, resSD=0.08062) ---
+
+ true stim:log(day) = +0.1785 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.42            |  0.82  <- observed
+  5        |  0.88            |  0.95
+  8        |  1.00            |  0.99
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.08926 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.26  <- observed
+  5        |  0.39            |  0.50
+  8        |  0.53            |  0.63
+  12       |  0.82            |  0.82
+  16       |  0.87            |  0.90
+  24       |  0.97            |  0.98
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/matched_current_d0_3/logpowersim.m b/analysis/matlab/variations/matched_current_d0_3/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/matched_current_d0_3/logpowersim.m
+++ b/analysis/matlab/variations/matched_current_d0_3/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/matched_current_d0_3/plotcurve.m b/analysis/matlab/variations/matched_current_d0_3/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/matched_current_d0_3/plotcurve.m
+++ b/analysis/matlab/variations/matched_current_d0_3/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/matched_current_d0_3/power_result.txt b/analysis/matlab/variations/matched_current_d0_3/power_result.txt
index 1ec16ec..86ec573 100644
--- a/analysis/matlab/variations/matched_current_d0_3/power_result.txt
+++ b/analysis/matlab/variations/matched_current_d0_3/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- matched_current_d0_3
+POWER SIMULATION -- matched_current_d0_3   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
-ground truth: stim:day=+10.20/day, rat SD=2.92, residual SD=8.54, days=4
+ground truth: stim:day=+10.2/day, rat SD=2.916, residual SD=8.54, days=4
 
- true stim:day interaction = +10.20 (100% of observed)
+ true stim:day interaction = +10.2 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.55            |  0.91  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+10.20/day, rat SD=2.92, residual SD=8.54, days=4
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:day interaction = +5.10 (50% of observed)
+ true stim:day interaction = +5.1 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.11            |  0.33  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+10.20/day, rat SD=2.92, residual SD=8.54, days=4
   16       |  0.97            |  0.97
   24       |  1.00            |  1.00
 
-Read the per-animal column as the honest power. At the observed N this study
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/matched_current_d0_3/power_result_rate.txt b/analysis/matlab/variations/matched_current_d0_3/power_result_rate.txt
new file mode 100644
index 0000000..f0dee6a
--- /dev/null
+++ b/analysis/matlab/variations/matched_current_d0_3/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- matched_current_d0_3   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.07723/day, rat SD=0.01983, residual SD=0.07841, days=4
+
+ true stim:day interaction = +0.07723 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.39            |  0.78  <- observed
+  5        |  0.86            |  0.94
+  8        |  0.98            |  0.99
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.03861 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.07            |  0.21  <- observed
+  5        |  0.35            |  0.47
+  8        |  0.53            |  0.62
+  12       |  0.78            |  0.79
+  16       |  0.82            |  0.87
+  24       |  0.97            |  0.97
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/matched_current_d0_3/powersim.m b/analysis/matlab/variations/matched_current_d0_3/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/matched_current_d0_3/powersim.m
+++ b/analysis/matlab/variations/matched_current_d0_3/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/matched_current_d0_3/result.txt b/analysis/matlab/variations/matched_current_d0_3/result.txt
index 1eefca1..6a20a91 100644
--- a/analysis/matlab/variations/matched_current_d0_3/result.txt
+++ b/analysis/matlab/variations/matched_current_d0_3/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: matched_current_d0_3
+VARIATION: matched_current_d0_3   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(20)=  3.27 F(1)= 10.700  p=0.003822    p=0.004244 (df=18)
 day (learning)             t(20)=  2.89 F(1)=  8.337  p=0.009105    p=0.009807 (df=18)
 stim (main, window start)  t(20)= -1.42 F(1)=  2.025  p=0.1701    p=0.1703 (df=20)
-interaction 95% CI: [+3.70, +16.70]
+interaction 95% CI: [+3.695, +16.7]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,9.8)=9.76, p=0.01107
   (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
    model above is the honest learning-rate test -- DF collapses toward the animal count.)
-INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.003822, slope diff=+10.20)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.003822, slope diff=+10.2)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/matched_current_d0_3/result_rate.txt b/analysis/matlab/variations/matched_current_d0_3/result_rate.txt
new file mode 100644
index 0000000..9666498
--- /dev/null
+++ b/analysis/matlab/variations/matched_current_d0_3/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: matched_current_d0_3   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2
+N = 6 rats, 24 sessions   raw day coverage: treat 0..3, control 0..3
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              24
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -40.724    -33.656    26.362           -52.724 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat      DF    pValue   
+    {'(Intercept)'}          0.23952    0.039567     6.0535    20    6.443e-06
+    {'day'        }         0.027914    0.020245     1.3788    20      0.18317
+    {'stim'       }        -0.089781    0.055956    -1.6045    20      0.12428
+    {'day:stim'   }         0.077228     0.02863     2.6974    20     0.013857
+
+
+    Lower        Upper   
+      0.15698     0.32205
+    -0.014316    0.070144
+      -0.2065    0.026941
+     0.017506     0.13695
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.019829
+
+
+    Lower         Upper  
+    0.00093276    0.42155
+
+Group: Error
+    Name               Estimate    Lower       Upper 
+    {'Res Std'}        0.078408    0.056558    0.1087
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(20)=  2.70 F(1)=  7.276  p=0.01386    p=0.01473 (df=18)
+day (learning)             t(20)=  1.38 F(1)=  1.901  p=0.1832    p=0.1848 (df=18)
+stim (main, window start)  t(20)= -1.60 F(1)=  2.574  p=0.1243    p=0.1235 (df=21)
+interaction 95% CI: [+0.01751, +0.1369]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,18.0)=7.28, p=0.01473
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.01386, slope diff=+0.07723)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_10/analyze.m b/analysis/matlab/variations/naive_a2_d0_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d0_10/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/naive_a2_d0_10/learning_curve.png b/analysis/matlab/variations/naive_a2_d0_10/learning_curve.png
index 427a949..456dcfa 100644
Binary files a/analysis/matlab/variations/naive_a2_d0_10/learning_curve.png and b/analysis/matlab/variations/naive_a2_d0_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_a2_d0_10/learning_curve_rate.png b/analysis/matlab/variations/naive_a2_d0_10/learning_curve_rate.png
new file mode 100644
index 0000000..a094a92
Binary files /dev/null and b/analysis/matlab/variations/naive_a2_d0_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_a2_d0_10/logpower_result.txt b/analysis/matlab/variations/naive_a2_d0_10/logpower_result.txt
index 5d48bf7..f6617ad 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/logpower_result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_10
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_10   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+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
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,100)=4.633  p(resid)=0.03378  p(Satt)=0.03391 (
   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=+9.59, ratSD=8.17, resSD=14.84) ---
+--- power simulation (log-day ground truth: stim:logday=+9.586, ratSD=8.17, resSD=14.84) ---
 
- true stim:log(day) = +9.59 (100% of observed)
+ true stim:log(day) = +9.586 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.23            |  0.44  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.010) = 0.101  (small-medium; f: .10 smal
   16       |  0.99            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +4.79 (50% of observed)
+ true stim:log(day) = +4.793 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.10  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.010) = 0.101  (small-medium; f: .10 smal
   16       |  0.62            |  0.64
   24       |  0.75            |  0.78
 
-Read the per-animal column as the honest power; the LME column matches the
-paper's power code (anova interaction p, observation-level DF) and is optimistic.
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d0_10/logpower_result_rate.txt b/analysis/matlab/variations/naive_a2_d0_10/logpower_result_rate.txt
new file mode 100644
index 0000000..a1547d8
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+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(1,98)=6.255  p(resid)=0.01404  p(Satt)=0.01413 (df=93)
+  honest per-animal random slope (log-day): F(1,6.5)=3.64  p=0.1012
+Cohen's f (interaction, partial eta^2=0.019) = 0.138  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.07796, ratSD=0.04949, resSD=0.1018) ---
+
+ true stim:log(day) = +0.07796 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.25            |  0.57  <- observed
+  5        |  0.71            |  0.84
+  8        |  0.92            |  0.95
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.03898 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.09            |  0.15  <- observed
+  5        |  0.14            |  0.26
+  8        |  0.41            |  0.46
+  12       |  0.52            |  0.56
+  16       |  0.78            |  0.78
+  24       |  0.88            |  0.89
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d0_10/logpowersim.m b/analysis/matlab/variations/naive_a2_d0_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/logpowersim.m
+++ b/analysis/matlab/variations/naive_a2_d0_10/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d0_10/plotcurve.m b/analysis/matlab/variations/naive_a2_d0_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/plotcurve.m
+++ b/analysis/matlab/variations/naive_a2_d0_10/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/naive_a2_d0_10/power_result.txt b/analysis/matlab/variations/naive_a2_d0_10/power_result.txt
index 853c454..4bed4d9 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/power_result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_a2_d0_10
+POWER SIMULATION -- naive_a2_d0_10   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=7   nrep=120, alpha=0.05
-ground truth: stim:day=+1.43/day, rat SD=8.75, residual SD=13.78, days=11
+ground truth: stim:day=+1.434/day, rat SD=8.753, residual SD=13.78, days=11
 
- true stim:day interaction = +1.43 (100% of observed)
+ true stim:day interaction = +1.434 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.09            |  0.23  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.43/day, rat SD=8.75, residual SD=13.78, days=11
   16       |  0.84            |  0.84
   24       |  0.95            |  0.94
 
- true stim:day interaction = +0.72 (50% of observed)
+ true stim:day interaction = +0.7169 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.09  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.43/day, rat SD=8.75, residual SD=13.78, days=11
   16       |  0.38            |  0.36
   24       |  0.47            |  0.47
 
-Read the per-animal column as the honest power. At the observed N this study
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d0_10/power_result_rate.txt b/analysis/matlab/variations/naive_a2_d0_10/power_result_rate.txt
new file mode 100644
index 0000000..2088156
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_a2_d0_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=7   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01123/day, rat SD=0.05096, residual SD=0.098, days=11
+
+ true stim:day interaction = +0.01123 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.28  <- observed
+  5        |  0.39            |  0.47
+  8        |  0.54            |  0.59
+  12       |  0.84            |  0.84
+  16       |  0.89            |  0.93
+  24       |  0.97            |  0.98
+
+ true stim:day interaction = +0.005617 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.08            |  0.10  <- observed
+  5        |  0.07            |  0.12
+  8        |  0.18            |  0.19
+  12       |  0.28            |  0.28
+  16       |  0.43            |  0.40
+  24       |  0.56            |  0.56
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d0_10/powersim.m b/analysis/matlab/variations/naive_a2_d0_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/powersim.m
+++ b/analysis/matlab/variations/naive_a2_d0_10/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d0_10/result.txt b/analysis/matlab/variations/naive_a2_d0_10/result.txt
index 3bef4cc..9a87fe2 100644
--- a/analysis/matlab/variations/naive_a2_d0_10/result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_a2_d0_10
+VARIATION: naive_a2_d0_10   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(100)=  1.54 F(1)=  2.382  p=0.1259    p=0.1261 (df=95)
 day (learning)             t(100)= 15.09 F(1)=227.710  p=1.598e-27    p=4.059e-27 (df=96)
 stim (main, window start)  t(100)=  1.48 F(1)=  2.192  p=0.1418    p=0.1543 (df=20)
-interaction 95% CI: [-0.41, +3.28]
+interaction 95% CI: [-0.4093, +3.277]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,9.0)=1.16, p=0.3093
   (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
    model above is the honest learning-rate test -- DF collapses toward the animal count.)
-INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1259, slope diff=+1.43)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1259, slope diff=+1.434)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_10/result_rate.txt b/analysis/matlab/variations/naive_a2_d0_10/result_rate.txt
new file mode 100644
index 0000000..8ef1f31
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_a2_d0_10   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2, Naive
+N = 10 rats, 102 sessions   raw day coverage: treat 0..10, control 0..10
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             102
+    Fixed effects coefficients           4
+    Random effects coefficients         10
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -159.21    -143.46    85.607           -171.21 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE           tStat     DF    pValue    
+    {'(Intercept)'}         0.19588      0.02981     6.571    98    2.4247e-09
+    {'day'        }         0.04169    0.0039441     10.57    98    7.0294e-18
+    {'stim'       }         0.05903      0.05266     1.121    98       0.26504
+    {'day:stim'   }        0.011235    0.0066828    1.6812    98      0.095917
+
+
+    Lower        Upper   
+      0.13672     0.25504
+     0.033863    0.049517
+    -0.045471     0.16353
+    -0.002027    0.024497
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.050962
+
+
+    Lower       Upper   
+    0.027158    0.095633
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.098003    0.084761    0.11331
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(98)=  1.68 F(1)=  2.826  p=0.09592    p=0.09609 (df=93)
+day (learning)             t(98)= 10.57 F(1)=111.730  p=7.029e-18    p=9.281e-18 (df=96)
+stim (main, window start)  t(98)=  1.12 F(1)=  1.257  p=0.265    p=0.2735 (df=24)
+interaction 95% CI: [-0.002027, +0.0245]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,8.4)=2.15, p=0.1786
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.09592, slope diff=+0.01123)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_13/analyze.m b/analysis/matlab/variations/naive_a2_d0_13/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d0_13/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/naive_a2_d0_13/learning_curve.png b/analysis/matlab/variations/naive_a2_d0_13/learning_curve.png
index 6122b31..7b244a3 100644
Binary files a/analysis/matlab/variations/naive_a2_d0_13/learning_curve.png and b/analysis/matlab/variations/naive_a2_d0_13/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_a2_d0_13/learning_curve_rate.png b/analysis/matlab/variations/naive_a2_d0_13/learning_curve_rate.png
new file mode 100644
index 0000000..89dd914
Binary files /dev/null and b/analysis/matlab/variations/naive_a2_d0_13/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_a2_d0_13/logpower_result.txt b/analysis/matlab/variations/naive_a2_d0_13/logpower_result.txt
index be8d54f..07def22 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/logpower_result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_13/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_13
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_13   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+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
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,120)=6.786  p(resid)=0.01035  p(Satt)=0.01039 (
   honest per-animal random slope (log-day): F(1,5.5)=5.06  p=0.06966
 Cohen's f (interaction, partial eta^2=0.011) = 0.104  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+9.94, ratSD=8.36, resSD=14.40) ---
+--- power simulation (log-day ground truth: stim:logday=+9.939, ratSD=8.359, resSD=14.4) ---
 
- true stim:log(day) = +9.94 (100% of observed)
+ true stim:log(day) = +9.939 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.27            |  0.71  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.011) = 0.104  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +4.97 (50% of observed)
+ true stim:log(day) = +4.969 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.12            |  0.25  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.011) = 0.104  (small-medium; f: .10 smal
   16       |  0.80            |  0.85
   24       |  0.92            |  0.93
 
-Read the per-animal column as the honest power; the LME column matches the
-paper's power code (anova interaction p, observation-level DF) and is optimistic.
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d0_13/logpower_result_rate.txt b/analysis/matlab/variations/naive_a2_d0_13/logpower_result_rate.txt
new file mode 100644
index 0000000..ec94263
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_13/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+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(1,118)=8.542  p(resid)=0.00416  p(Satt)=0.00418 (df=115)
+  honest per-animal random slope (log-day): F(1,5.1)=7.00  p=0.04487
+Cohen's f (interaction, partial eta^2=0.019) = 0.141  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.07789, ratSD=0.04775, resSD=0.09872) ---
+
+ true stim:log(day) = +0.07789 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.38            |  0.80  <- observed
+  5        |  0.84            |  0.93
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.03895 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.13            |  0.31  <- observed
+  5        |  0.31            |  0.38
+  8        |  0.59            |  0.63
+  12       |  0.75            |  0.78
+  16       |  0.92            |  0.92
+  24       |  0.95            |  0.95
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d0_13/logpowersim.m b/analysis/matlab/variations/naive_a2_d0_13/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/logpowersim.m
+++ b/analysis/matlab/variations/naive_a2_d0_13/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d0_13/plotcurve.m b/analysis/matlab/variations/naive_a2_d0_13/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/plotcurve.m
+++ b/analysis/matlab/variations/naive_a2_d0_13/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/naive_a2_d0_13/power_result.txt b/analysis/matlab/variations/naive_a2_d0_13/power_result.txt
index 4de41e2..0a410b9 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/power_result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_13/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_a2_d0_13
+POWER SIMULATION -- naive_a2_d0_13   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=7   nrep=120, alpha=0.05
-ground truth: stim:day=+1.61/day, rat SD=8.46, residual SD=15.26, days=14
+ground truth: stim:day=+1.614/day, rat SD=8.46, residual SD=15.26, days=14
 
- true stim:day interaction = +1.61 (100% of observed)
+ true stim:day interaction = +1.614 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.22            |  0.53  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.61/day, rat SD=8.46, residual SD=15.26, days=14
   16       |  0.98            |  0.98
   24       |  1.00            |  1.00
 
- true stim:day interaction = +0.81 (50% of observed)
+ true stim:day interaction = +0.8068 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.12            |  0.21  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.61/day, rat SD=8.46, residual SD=15.26, days=14
   16       |  0.64            |  0.72
   24       |  0.78            |  0.75
 
-Read the per-animal column as the honest power. At the observed N this study
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d0_13/power_result_rate.txt b/analysis/matlab/variations/naive_a2_d0_13/power_result_rate.txt
new file mode 100644
index 0000000..99903c5
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_13/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_a2_d0_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=7   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01209/day, rat SD=0.0442, residual SD=0.1045, days=14
+
+ true stim:day interaction = +0.01209 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.27            |  0.64  <- observed
+  5        |  0.62            |  0.75
+  8        |  0.88            |  0.93
+  12       |  0.98            |  1.00
+  16       |  0.99            |  0.99
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.006044 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.13            |  0.24  <- observed
+  5        |  0.21            |  0.24
+  8        |  0.46            |  0.47
+  12       |  0.53            |  0.55
+  16       |  0.70            |  0.77
+  24       |  0.82            |  0.81
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d0_13/powersim.m b/analysis/matlab/variations/naive_a2_d0_13/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/powersim.m
+++ b/analysis/matlab/variations/naive_a2_d0_13/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d0_13/result.txt b/analysis/matlab/variations/naive_a2_d0_13/result.txt
index 91593bb..6ae17f7 100644
--- a/analysis/matlab/variations/naive_a2_d0_13/result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_13/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_a2_d0_13
+VARIATION: naive_a2_d0_13   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(120)=  2.08 F(1)=  4.320  p=0.0398    p=0.03986 (df=117)
 day (learning)             t(120)= 14.57 F(1)=212.424  p=2.547e-28    p=3.246e-28 (df=119)
 stim (main, window start)  t(120)=  1.44 F(1)=  2.062  p=0.1536    p=0.1655 (df=21)
-interaction 95% CI: [+0.08, +3.15]
+interaction 95% CI: [+0.07651, +3.151]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,41.7)=3.81, p=0.05781
   (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
    model above is the honest learning-rate test -- DF collapses toward the animal count.)
-INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.0398, slope diff=+1.61)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.0398, slope diff=+1.614)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_13/result_rate.txt b/analysis/matlab/variations/naive_a2_d0_13/result_rate.txt
new file mode 100644
index 0000000..e154fc1
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_13/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_a2_d0_13   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2, Naive
+N = 10 rats, 122 sessions   raw day coverage: treat 0..13, control 0..13
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             122
+    Fixed effects coefficients           4
+    Random effects coefficients         10
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC       LogLikelihood    Deviance
+    -181.33    -164.5    96.663           -193.33 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE           tStat     DF     pValue    
+    {'(Intercept)'}         0.23325     0.027564    8.4623    118    8.2776e-14
+    {'day'        }        0.031488    0.0030917    10.184    118    7.3141e-18
+    {'stim'       }        0.055421     0.048873     1.134    118        0.2591
+    {'day:stim'   }        0.012089    0.0053578    2.2563    118      0.025894
+
+
+    Lower        Upper   
+      0.17867     0.28783
+     0.025365     0.03761
+    -0.041361      0.1522
+    0.0014788    0.022699
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.044199
+
+
+    Lower       Upper   
+    0.022679    0.086139
+
+Group: Error
+    Name               Estimate    Lower       Upper 
+    {'Res Std'}        0.10453     0.091674    0.1192
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(118)=  2.26 F(1)=  5.091  p=0.02589    p=0.02593 (df=116)
+day (learning)             t(118)= 10.18 F(1)=103.724  p=7.314e-18    p=6.719e-18 (df=119)
+stim (main, window start)  t(118)=  1.13 F(1)=  1.286  p=0.2591    p=0.2666 (df=27)
+interaction 95% CI: [+0.001479, +0.0227]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,97.3)=5.06, p=0.02667
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.02589, slope diff=+0.01209)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_5/analyze.m b/analysis/matlab/variations/naive_a2_d0_5/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d0_5/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/naive_a2_d0_5/learning_curve.png b/analysis/matlab/variations/naive_a2_d0_5/learning_curve.png
index 78345a8..0e8cc67 100644
Binary files a/analysis/matlab/variations/naive_a2_d0_5/learning_curve.png and b/analysis/matlab/variations/naive_a2_d0_5/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_a2_d0_5/learning_curve_rate.png b/analysis/matlab/variations/naive_a2_d0_5/learning_curve_rate.png
new file mode 100644
index 0000000..9327263
Binary files /dev/null and b/analysis/matlab/variations/naive_a2_d0_5/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_a2_d0_5/logpower_result.txt b/analysis/matlab/variations/naive_a2_d0_5/logpower_result.txt
index 4f3706b..13d6a00 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/logpower_result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_5/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_5
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_5   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+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
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,55)=9.364  p(resid)=0.003417  p(Satt)=0.003585
   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=9.47, resSD=13.37) ---
+--- 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
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.057) = 0.245  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +9.67 (50% of observed)
+ true stim:log(day) = +9.665 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.12            |  0.28  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.057) = 0.245  (small-medium; f: .10 smal
   16       |  0.83            |  0.84
   24       |  0.95            |  0.96
 
-Read the per-animal column as the honest power; the LME column matches the
-paper's power code (anova interaction p, observation-level DF) and is optimistic.
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d0_5/logpower_result_rate.txt b/analysis/matlab/variations/naive_a2_d0_5/logpower_result_rate.txt
new file mode 100644
index 0000000..c4cbf3e
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_5/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d0_5   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+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(1,53)=14.105  p(resid)=0.0004314  p(Satt)=0.0004753 (df=47)
+  honest per-animal random slope (log-day): F(1,11.9)=12.05  p=0.004674
+Cohen's f (interaction, partial eta^2=0.124) = 0.376  (medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1804, ratSD=0.05407, resSD=0.09942) ---
+
+ true stim:log(day) = +0.1804 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.53            |  0.89  <- observed
+  5        |  0.92            |  0.97
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.09019 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.13            |  0.38  <- observed
+  5        |  0.44            |  0.54
+  8        |  0.72            |  0.73
+  12       |  0.78            |  0.88
+  16       |  0.94            |  0.97
+  24       |  1.00            |  1.00
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d0_5/logpowersim.m b/analysis/matlab/variations/naive_a2_d0_5/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/logpowersim.m
+++ b/analysis/matlab/variations/naive_a2_d0_5/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d0_5/plotcurve.m b/analysis/matlab/variations/naive_a2_d0_5/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/plotcurve.m
+++ b/analysis/matlab/variations/naive_a2_d0_5/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/naive_a2_d0_5/power_result.txt b/analysis/matlab/variations/naive_a2_d0_5/power_result.txt
index c57b265..949cbad 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/power_result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_5/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_a2_d0_5
+POWER SIMULATION -- naive_a2_d0_5   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=7   nrep=120, alpha=0.05
-ground truth: stim:day=+6.96/day, rat SD=9.64, residual SD=12.11, days=6
+ground truth: stim:day=+6.962/day, rat SD=9.643, residual SD=12.11, days=6
 
- true stim:day interaction = +6.96 (100% of observed)
+ true stim:day interaction = +6.962 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.43            |  0.85  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+6.96/day, rat SD=9.64, residual SD=12.11, days=6
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:day interaction = +3.48 (50% of observed)
+ true stim:day interaction = +3.481 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.12            |  0.31  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+6.96/day, rat SD=9.64, residual SD=12.11, days=6
   16       |  0.90            |  0.89
   24       |  0.97            |  0.99
 
-Read the per-animal column as the honest power. At the observed N this study
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d0_5/power_result_rate.txt b/analysis/matlab/variations/naive_a2_d0_5/power_result_rate.txt
new file mode 100644
index 0000000..11627a1
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_5/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_a2_d0_5   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=7   nrep=120, alpha=0.05
+ground truth: stim:day=+0.06206/day, rat SD=0.05458, residual SD=0.09694, days=6
+
+ true stim:day interaction = +0.06206 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.53            |  0.90  <- observed
+  5        |  0.93            |  0.98
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.03103 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.35  <- observed
+  5        |  0.44            |  0.52
+  8        |  0.66            |  0.72
+  12       |  0.83            |  0.88
+  16       |  0.94            |  0.95
+  24       |  0.99            |  0.99
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d0_5/powersim.m b/analysis/matlab/variations/naive_a2_d0_5/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/powersim.m
+++ b/analysis/matlab/variations/naive_a2_d0_5/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d0_5/result.txt b/analysis/matlab/variations/naive_a2_d0_5/result.txt
index 9c0f388..0d48098 100644
--- a/analysis/matlab/variations/naive_a2_d0_5/result.txt
+++ b/analysis/matlab/variations/naive_a2_d0_5/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_a2_d0_5
+VARIATION: naive_a2_d0_5   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(55)=  3.45 F(1)= 11.924  p=0.001073    p=0.001152 (df=49)
 day (learning)             t(55)=  7.32 F(1)= 53.591  p=1.125e-09    p=2.092e-09 (df=49)
 stim (main, window start)  t(55)=  0.05 F(1)=  0.002  p=0.9606    p=0.9609 (df=20)
-interaction 95% CI: [+2.92, +11.00]
+interaction 95% CI: [+2.922, +11]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,9.7)=6.65, p=0.02815
   (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
    model above is the honest learning-rate test -- DF collapses toward the animal count.)
-INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.001073, slope diff=+6.96)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.001073, slope diff=+6.962)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d0_5/result_rate.txt b/analysis/matlab/variations/naive_a2_d0_5/result_rate.txt
new file mode 100644
index 0000000..ba01608
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d0_5/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_a2_d0_5   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2, Naive
+N = 10 rats, 57 sessions   raw day coverage: treat 0..5, control 0..5
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              57
+    Fixed effects coefficients           4
+    Random effects coefficients         10
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -81.991    -69.732    46.995           -93.991 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat       DF
+    {'(Intercept)'}          0.21873      0.03639      6.0107    53
+    {'day'        }         0.030326    0.0094955      3.1937    53
+    {'stim'       }        -0.053886     0.062912    -0.85653    53
+    {'day:stim'   }         0.062061     0.016406      3.7829    53
+
+
+    pValue        Lower       Upper   
+    1.7409e-07     0.14574     0.29172
+      0.002365     0.01128    0.049372
+       0.39556    -0.18007    0.072299
+    0.00039586    0.029156    0.094967
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.054581
+
+
+    Lower       Upper  
+    0.026757    0.11134
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.096936    0.079103    0.11879
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(53)=  3.78 F(1)= 14.310  p=0.0003959    p=0.0004381 (df=47)
+day (learning)             t(53)=  3.19 F(1)= 10.200  p=0.002365    p=0.002483 (df=48)
+stim (main, window start)  t(53)= -0.86 F(1)=  0.734  p=0.3956    p=0.3992 (df=27)
+interaction 95% CI: [+0.02916, +0.09497]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,11.5)=11.53, p=0.005637
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
+INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.0003959, slope diff=+0.06206)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d6_10/analyze.m b/analysis/matlab/variations/naive_a2_d6_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d6_10/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/naive_a2_d6_10/learning_curve.png b/analysis/matlab/variations/naive_a2_d6_10/learning_curve.png
index 029ba85..a6de151 100644
Binary files a/analysis/matlab/variations/naive_a2_d6_10/learning_curve.png and b/analysis/matlab/variations/naive_a2_d6_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_a2_d6_10/learning_curve_rate.png b/analysis/matlab/variations/naive_a2_d6_10/learning_curve_rate.png
new file mode 100644
index 0000000..6e47570
Binary files /dev/null and b/analysis/matlab/variations/naive_a2_d6_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_a2_d6_10/logpower_result.txt b/analysis/matlab/variations/naive_a2_d6_10/logpower_result.txt
index e2d0afe..f8495b1 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/logpower_result.txt
+++ b/analysis/matlab/variations/naive_a2_d6_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d6_10
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d6_10   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+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
 
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.006) = 0.076  (small; f: .10 small, .25
   16       |  0.34            |  0.36
   24       |  0.51            |  0.53
 
- true stim:log(day) = -8.46 (50% of observed)
+ true stim:log(day) = -8.464 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.03            |  0.07  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.006) = 0.076  (small; f: .10 small, .25
   16       |  0.11            |  0.12
   24       |  0.13            |  0.14
 
-Read the per-animal column as the honest power; the LME column matches the
-paper's power code (anova interaction p, observation-level DF) and is optimistic.
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d6_10/logpower_result_rate.txt b/analysis/matlab/variations/naive_a2_d6_10/logpower_result_rate.txt
new file mode 100644
index 0000000..13d169b
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d6_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d6_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+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)=2.375  p(resid)=0.131  p(Satt)=0.1321 (df=36)
+  honest per-animal random slope (log-day): F(1,9.0)=1.58  p=0.2398
+Cohen's f (interaction, partial eta^2=0.023) = 0.153  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=-0.2002, ratSD=0.06161, resSD=0.06563) ---
+
+ true stim:log(day) = -0.2002 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.26  <- observed
+  5        |  0.27            |  0.39
+  8        |  0.50            |  0.57
+  12       |  0.78            |  0.82
+  16       |  0.88            |  0.91
+  24       |  0.97            |  0.97
+
+ true stim:log(day) = -0.1001 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.10  <- observed
+  5        |  0.07            |  0.12
+  8        |  0.17            |  0.22
+  12       |  0.17            |  0.19
+  16       |  0.33            |  0.39
+  24       |  0.36            |  0.39
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d6_10/logpowersim.m b/analysis/matlab/variations/naive_a2_d6_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/logpowersim.m
+++ b/analysis/matlab/variations/naive_a2_d6_10/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d6_10/plotcurve.m b/analysis/matlab/variations/naive_a2_d6_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/plotcurve.m
+++ b/analysis/matlab/variations/naive_a2_d6_10/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/naive_a2_d6_10/power_result.txt b/analysis/matlab/variations/naive_a2_d6_10/power_result.txt
index ae734b8..cea797f 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/power_result.txt
+++ b/analysis/matlab/variations/naive_a2_d6_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_a2_d6_10
+POWER SIMULATION -- naive_a2_d6_10   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=6   nrep=120, alpha=0.05
-ground truth: stim:day=-1.80/day, rat SD=10.99, residual SD=10.55, days=5
+ground truth: stim:day=-1.8/day, rat SD=10.99, residual SD=10.55, days=5
 
- true stim:day interaction = -1.80 (100% of observed)
+ true stim:day interaction = -1.8 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.06            |  0.09  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=-1.80/day, rat SD=10.99, residual SD=10.55, days=5
   16       |  0.33            |  0.34
   24       |  0.45            |  0.49
 
- true stim:day interaction = -0.90 (50% of observed)
+ true stim:day interaction = -0.9 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.03            |  0.07  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=-1.80/day, rat SD=10.99, residual SD=10.55, days=5
   16       |  0.11            |  0.11
   24       |  0.13            |  0.14
 
-Read the per-animal column as the honest power. At the observed N this study
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d6_10/power_result_rate.txt b/analysis/matlab/variations/naive_a2_d6_10/power_result_rate.txt
new file mode 100644
index 0000000..2125f2e
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d6_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_a2_d6_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=6   nrep=120, alpha=0.05
+ground truth: stim:day=-0.02201/day, rat SD=0.06153, residual SD=0.06598, days=5
+
+ true stim:day interaction = -0.02201 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.10            |  0.23  <- observed
+  5        |  0.23            |  0.37
+  8        |  0.47            |  0.55
+  12       |  0.75            |  0.78
+  16       |  0.88            |  0.88
+  24       |  0.95            |  0.96
+
+ true stim:day interaction = -0.01101 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.10  <- observed
+  5        |  0.07            |  0.11
+  8        |  0.17            |  0.21
+  12       |  0.15            |  0.18
+  16       |  0.33            |  0.38
+  24       |  0.34            |  0.37
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d6_10/powersim.m b/analysis/matlab/variations/naive_a2_d6_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/powersim.m
+++ b/analysis/matlab/variations/naive_a2_d6_10/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d6_10/result.txt b/analysis/matlab/variations/naive_a2_d6_10/result.txt
index 8846366..2bdf8a6 100644
--- a/analysis/matlab/variations/naive_a2_d6_10/result.txt
+++ b/analysis/matlab/variations/naive_a2_d6_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_a2_d6_10
+VARIATION: naive_a2_d6_10   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(41)= -0.76 F(1)=  0.582  p=0.4499    p=0.4505 (df=36)
 day (learning)             t(41)=  4.38 F(1)= 19.183  p=8.027e-05    p=9.813e-05 (df=36)
 stim (main, window start)  t(41)=  2.73 F(1)=  7.453  p=0.00929    p=0.01541 (df=15)
-interaction 95% CI: [-6.57, +2.97]
+interaction 95% CI: [-6.565, +2.965]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,9.0)=0.44, p=0.5223
   (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
    model above is the honest learning-rate test -- DF collapses toward the animal count.)
-INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4499, slope diff=-1.80)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4499, slope diff=-1.8)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d6_10/result_rate.txt b/analysis/matlab/variations/naive_a2_d6_10/result_rate.txt
new file mode 100644
index 0000000..69f1ea9
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d6_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_a2_d6_10   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2, Naive
+N = 9 rats, 45 sessions   raw day coverage: treat 6..10, control 6..10
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              45
+    Fixed effects coefficients           4
+    Random effects coefficients          9
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    -89.86    -79.02    50.93            -101.86 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat      DF    pValue    
+    {'(Intercept)'}          0.46152    0.032656     14.133    41     2.355e-17
+    {'day'        }         0.034211    0.008518     4.0163    41    0.00024603
+    {'stim'       }          0.18209    0.056562     3.2193    41     0.0025147
+    {'day:stim'   }        -0.022011    0.014754    -1.4919    41       0.14338
+
+
+    Lower        Upper    
+      0.39557      0.52747
+     0.017008     0.051413
+     0.067859      0.29632
+    -0.051806    0.0077844
+
+Random effects covariance parameters (95% CIs):
+Group: rat (9 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.061534
+
+
+    Lower       Upper  
+    0.034776    0.10888
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.06598     0.052372    0.083124
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(41)= -1.49 F(1)=  2.226  p=0.1434    p=0.1444 (df=36)
+day (learning)             t(41)=  4.02 F(1)= 16.131  p=0.000246    p=0.0002875 (df=36)
+stim (main, window start)  t(41)=  3.22 F(1)= 10.364  p=0.002515    p=0.005217 (df=16)
+interaction 95% CI: [-0.05181, +0.007784]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,9.0)=1.61, p=0.2369
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1434, slope diff=-0.02201)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d6_13/analyze.m b/analysis/matlab/variations/naive_a2_d6_13/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/analyze.m
+++ b/analysis/matlab/variations/naive_a2_d6_13/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/naive_a2_d6_13/learning_curve.png b/analysis/matlab/variations/naive_a2_d6_13/learning_curve.png
index eb6784c..8aa97fb 100644
Binary files a/analysis/matlab/variations/naive_a2_d6_13/learning_curve.png and b/analysis/matlab/variations/naive_a2_d6_13/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_a2_d6_13/learning_curve_rate.png b/analysis/matlab/variations/naive_a2_d6_13/learning_curve_rate.png
new file mode 100644
index 0000000..43a8c0f
Binary files /dev/null and b/analysis/matlab/variations/naive_a2_d6_13/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_a2_d6_13/logpower_result.txt b/analysis/matlab/variations/naive_a2_d6_13/logpower_result.txt
index fee1b05..ab30786 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/logpower_result.txt
+++ b/analysis/matlab/variations/naive_a2_d6_13/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d6_13
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d6_13   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+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
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,61)=0.405  p(resid)=0.5267  p(Satt)=0.5268 (df=
   honest per-animal random slope (log-day): F(1,8.2)=0.20  p=0.663
 Cohen's f (interaction, partial eta^2=0.002) = 0.047  (small; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+9.35, ratSD=9.78, resSD=11.90) ---
+--- power simulation (log-day ground truth: stim:logday=+9.352, ratSD=9.782, resSD=11.9) ---
 
- true stim:log(day) = +9.35 (100% of observed)
+ true stim:log(day) = +9.352 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.10  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.002) = 0.047  (small; f: .10 small, .25
   16       |  0.30            |  0.32
   24       |  0.42            |  0.42
 
- true stim:log(day) = +4.68 (50% of observed)
+ true stim:log(day) = +4.676 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.05            |  0.09  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.002) = 0.047  (small; f: .10 small, .25
   16       |  0.07            |  0.07
   24       |  0.17            |  0.17
 
-Read the per-animal column as the honest power; the LME column matches the
-paper's power code (anova interaction p, observation-level DF) and is optimistic.
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d6_13/logpower_result_rate.txt b/analysis/matlab/variations/naive_a2_d6_13/logpower_result_rate.txt
new file mode 100644
index 0000000..1560ba6
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d6_13/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_a2_d6_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+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(1,61)=0.040  p(resid)=0.843  p(Satt)=0.843 (df=58)
+  honest per-animal random slope (log-day): F(1,7.8)=0.00  p=0.9656
+Cohen's f (interaction, partial eta^2=0.000) = 0.010  (small; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.01851, ratSD=0.05356, resSD=0.07547) ---
+
+ true stim:log(day) = +0.01851 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.05            |  0.05  <- observed
+  5        |  0.09            |  0.08
+  8        |  0.05            |  0.03
+  12       |  0.04            |  0.06
+  16       |  0.07            |  0.08
+  24       |  0.03            |  0.07
+
+ true stim:log(day) = +0.009253 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.05            |  0.07  <- observed
+  5        |  0.05            |  0.06
+  8        |  0.07            |  0.04
+  12       |  0.04            |  0.05
+  16       |  0.04            |  0.05
+  24       |  0.07            |  0.07
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_a2_d6_13/logpowersim.m b/analysis/matlab/variations/naive_a2_d6_13/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/logpowersim.m
+++ b/analysis/matlab/variations/naive_a2_d6_13/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d6_13/plotcurve.m b/analysis/matlab/variations/naive_a2_d6_13/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/plotcurve.m
+++ b/analysis/matlab/variations/naive_a2_d6_13/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/naive_a2_d6_13/power_result.txt b/analysis/matlab/variations/naive_a2_d6_13/power_result.txt
index 7b2e583..b02b282 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/power_result.txt
+++ b/analysis/matlab/variations/naive_a2_d6_13/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_a2_d6_13
+POWER SIMULATION -- naive_a2_d6_13   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=6   nrep=120, alpha=0.05
-ground truth: stim:day=+1.16/day, rat SD=9.76, residual SD=12.03, days=8
+ground truth: stim:day=+1.155/day, rat SD=9.759, residual SD=12.03, days=8
 
- true stim:day interaction = +1.16 (100% of observed)
+ true stim:day interaction = +1.155 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.08            |  0.15  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.16/day, rat SD=9.76, residual SD=12.03, days=8
   16       |  0.42            |  0.47
   24       |  0.58            |  0.59
 
- true stim:day interaction = +0.58 (50% of observed)
+ true stim:day interaction = +0.5776 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.06            |  0.09  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.16/day, rat SD=9.76, residual SD=12.03, days=8
   16       |  0.12            |  0.12
   24       |  0.21            |  0.23
 
-Read the per-animal column as the honest power. At the observed N this study
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d6_13/power_result_rate.txt b/analysis/matlab/variations/naive_a2_d6_13/power_result_rate.txt
new file mode 100644
index 0000000..d96945e
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d6_13/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_a2_d6_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=6   nrep=120, alpha=0.05
+ground truth: stim:day=+0.003468/day, rat SD=0.05337, residual SD=0.07609, days=8
+
+ true stim:day interaction = +0.003468 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.05            |  0.05  <- observed
+  5        |  0.12            |  0.13
+  8        |  0.09            |  0.11
+  12       |  0.09            |  0.08
+  16       |  0.15            |  0.17
+  24       |  0.14            |  0.15
+
+ true stim:day interaction = +0.001734 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.05            |  0.07  <- observed
+  5        |  0.04            |  0.07
+  8        |  0.07            |  0.07
+  12       |  0.05            |  0.05
+  16       |  0.06            |  0.07
+  24       |  0.08            |  0.08
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_a2_d6_13/powersim.m b/analysis/matlab/variations/naive_a2_d6_13/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/powersim.m
+++ b/analysis/matlab/variations/naive_a2_d6_13/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/naive_a2_d6_13/result.txt b/analysis/matlab/variations/naive_a2_d6_13/result.txt
index 2368021..81643c9 100644
--- a/analysis/matlab/variations/naive_a2_d6_13/result.txt
+++ b/analysis/matlab/variations/naive_a2_d6_13/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_a2_d6_13
+VARIATION: naive_a2_d6_13   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(61)=  0.78 F(1)=  0.605  p=0.4398    p=0.4399 (df=58)
 day (learning)             t(61)=  3.39 F(1)= 11.485  p=0.001235    p=0.001274 (df=57)
 stim (main, window start)  t(61)=  2.46 F(1)=  6.072  p=0.01657    p=0.02473 (df=17)
-interaction 95% CI: [-1.82, +4.13]
+interaction 95% CI: [-1.815, +4.126]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,7.7)=0.36, p=0.5635
   (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
    model above is the honest learning-rate test -- DF collapses toward the animal count.)
-INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4398, slope diff=+1.16)
-Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4398, slope diff=+1.155)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_a2_d6_13/result_rate.txt b/analysis/matlab/variations/naive_a2_d6_13/result_rate.txt
new file mode 100644
index 0000000..1c1d330
--- /dev/null
+++ b/analysis/matlab/variations/naive_a2_d6_13/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_a2_d6_13   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2, Naive
+N = 9 rats, 65 sessions   raw day coverage: treat 6..13, control 6..13
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              65
+    Fixed effects coefficients           4
+    Random effects coefficients          9
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC       LogLikelihood    Deviance
+    -124.85    -111.8    68.424           -136.85 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat      DF    pValue    
+    {'(Intercept)'}          0.49381     0.029854     16.541    61    3.1921e-24
+    {'day'        }         0.012224    0.0052666      2.321    61      0.023647
+    {'stim'       }          0.14414     0.051782     2.7835    61      0.007149
+    {'day:stim'   }        0.0034682    0.0093748    0.36995    61        0.7127
+
+
+    Lower        Upper   
+      0.43411      0.5535
+    0.0016924    0.022755
+     0.040593     0.24768
+    -0.015278    0.022214
+
+Random effects covariance parameters (95% CIs):
+Group: rat (9 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.053368
+
+
+    Lower       Upper   
+    0.029753    0.095724
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.076088    0.063265    0.091511
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(61)=  0.37 F(1)=  0.137  p=0.7127    p=0.7128 (df=58)
+day (learning)             t(61)=  2.32 F(1)=  5.387  p=0.02365    p=0.02384 (df=58)
+stim (main, window start)  t(61)=  2.78 F(1)=  7.748  p=0.007149    p=0.01177 (df=19)
+interaction 95% CI: [-0.01528, +0.02221]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,7.4)=0.04, p=0.8393
+  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope
+   model above is the honest learning-rate test -- DF collapses toward the animal count.)
+INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.7127, slope diff=+0.003468)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.