diff --git a/analysis/matlab/variations/naive_boxa_d0_10/analyze.m b/analysis/matlab/variations/naive_boxa_d0_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_10/learning_curve.png b/analysis/matlab/variations/naive_boxa_d0_10/learning_curve.png
index 58d5ff5..dd07c2b 100644
Binary files a/analysis/matlab/variations/naive_boxa_d0_10/learning_curve.png and b/analysis/matlab/variations/naive_boxa_d0_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d0_10/learning_curve_rate.png b/analysis/matlab/variations/naive_boxa_d0_10/learning_curve_rate.png
new file mode 100644
index 0000000..bb7956e
Binary files /dev/null and b/analysis/matlab/variations/naive_boxa_d0_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d0_10/logpower_result.txt b/analysis/matlab/variations/naive_boxa_d0_10/logpower_result.txt
index 60b6e42..3af794c 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/logpower_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_10
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=8   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,111)=3.705  p(resid)=0.05681  p(Satt)=0.05696 (
   honest per-animal random slope (log-day): F(1,8.2)=2.03  p=0.1913
 Cohen's f (interaction, partial eta^2=0.007) = 0.086  (small; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+8.43, ratSD=8.13, resSD=14.95) ---
+--- power simulation (log-day ground truth: stim:logday=+8.428, ratSD=8.126, resSD=14.95) ---
 
- true stim:log(day) = +8.43 (100% of observed)
+ true stim:log(day) = +8.428 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.17            |  0.35  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.086  (small; f: .10 small, .25
   16       |  0.95            |  0.96
   24       |  0.99            |  1.00
 
- true stim:log(day) = +4.21 (50% of observed)
+ true stim:log(day) = +4.214 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.07  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.086  (small; f: .10 small, .25
   16       |  0.55            |  0.53
   24       |  0.63            |  0.62
 
-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_boxa_d0_10/logpower_result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_10/logpower_result_rate.txt
new file mode 100644
index 0000000..f3a0479
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=8   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,109)=4.842  p(resid)=0.02988  p(Satt)=0.03001 (df=103)
+  honest per-animal random slope (log-day): F(1,7.9)=3.23  p=0.1106
+Cohen's f (interaction, partial eta^2=0.013) = 0.116  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.06771, ratSD=0.04956, resSD=0.1034) ---
+
+ true stim:log(day) = +0.06771 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.23            |  0.47  <- observed
+  5        |  0.57            |  0.71
+  8        |  0.85            |  0.87  <- observed
+  12       |  0.98            |  0.99
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.03385 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.07            |  0.12  <- observed
+  5        |  0.12            |  0.17
+  8        |  0.32            |  0.39  <- observed
+  12       |  0.36            |  0.39
+  16       |  0.63            |  0.66
+  24       |  0.75            |  0.78
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_boxa_d0_10/logpowersim.m b/analysis/matlab/variations/naive_boxa_d0_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/logpowersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_10/plotcurve.m b/analysis/matlab/variations/naive_boxa_d0_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/plotcurve.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_10/power_result.txt b/analysis/matlab/variations/naive_boxa_d0_10/power_result.txt
index 64b5503..eb8153d 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/power_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_boxa_d0_10
+POWER SIMULATION -- naive_boxa_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=8   nrep=120, alpha=0.05
-ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
+ground truth: stim:day=+1.178/day, rat SD=8.671, residual SD=13.71, days=11
 
- true stim:day interaction = +1.18 (100% of observed)
+ true stim:day interaction = +1.178 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.17  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
   16       |  0.68            |  0.70
   24       |  0.88            |  0.88
 
- true stim:day interaction = +0.59 (50% of observed)
+ true stim:day interaction = +0.589 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.05            |  0.07  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.18/day, rat SD=8.67, residual SD=13.71, days=11
   16       |  0.28            |  0.27
   24       |  0.28            |  0.29
 
-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_boxa_d0_10/power_result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_10/power_result_rate.txt
new file mode 100644
index 0000000..017911b
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_boxa_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=8   nrep=120, alpha=0.05
+ground truth: stim:day=+0.008893/day, rat SD=0.05104, residual SD=0.09808, days=11
+
+ true stim:day interaction = +0.008893 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.08            |  0.16  <- observed
+  5        |  0.21            |  0.25
+  8        |  0.36            |  0.43  <- observed
+  12       |  0.62            |  0.68
+  16       |  0.72            |  0.73
+  24       |  0.88            |  0.88
+
+ true stim:day interaction = +0.004446 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.07            |  0.08  <- observed
+  5        |  0.05            |  0.08
+  8        |  0.11            |  0.11  <- observed
+  12       |  0.17            |  0.19
+  16       |  0.30            |  0.29
+  24       |  0.33            |  0.35
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_boxa_d0_10/powersim.m b/analysis/matlab/variations/naive_boxa_d0_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/powersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_10/result.txt b/analysis/matlab/variations/naive_boxa_d0_10/result.txt
index a972e49..67d4c5a 100644
--- a/analysis/matlab/variations/naive_boxa_d0_10/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_boxa_d0_10
+VARIATION: naive_boxa_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(111)=  1.31 F(1)=  1.707  p=0.1941    p=0.1943 (df=104)
 day (learning)             t(111)= 16.91 F(1)=285.824  p=1.743e-32    p=6.758e-32 (df=106)
 stim (main, window start)  t(111)=  1.55 F(1)=  2.392  p=0.1248    p=0.1361 (df=22)
-interaction 95% CI: [-0.61, +2.96]
+interaction 95% CI: [-0.6088, +2.965]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,10.1)=0.83, p=0.3836
   (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=0.1941, slope diff=+1.18)
-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=0.1941, slope diff=+1.178)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d0_10/result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_10/result_rate.txt
new file mode 100644
index 0000000..5f1e72e
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_boxa_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-A, Electrode-Box-A2, Naive
+N = 11 rats, 113 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             113
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -177.53    -161.16    94.764           -189.53 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat     DF     pValue    
+    {'(Intercept)'}          0.19077     0.027729    6.8796    109    3.9606e-10
+    {'day'        }         0.044032    0.0036365    12.108    109     6.709e-22
+    {'stim'       }         0.064146     0.051551    1.2443    109       0.21605
+    {'day:stim'   }        0.0088928    0.0065096    1.3661    109       0.17471
+
+
+    Lower         Upper   
+       0.13581     0.24572
+      0.036824    0.051239
+     -0.038027     0.16632
+    -0.0040089    0.021795
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.05104 
+
+
+    Lower       Upper   
+    0.028096    0.092721
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.09808     0.085453    0.11257
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(109)=  1.37 F(1)=  1.866  p=0.1747    p=0.1749 (df=103)
+day (learning)             t(109)= 12.11 F(1)=146.612  p=6.709e-22    p=1.131e-21 (df=105)
+stim (main, window start)  t(109)=  1.24 F(1)=  1.548  p=0.2161    p=0.2244 (df=26)
+interaction 95% CI: [-0.004009, +0.02179]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,9.6)=1.38, p=0.2681
+  (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=0.1747, slope diff=+0.008893)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d0_13/analyze.m b/analysis/matlab/variations/naive_boxa_d0_13/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_13/learning_curve.png b/analysis/matlab/variations/naive_boxa_d0_13/learning_curve.png
index 026ae49..fa988ed 100644
Binary files a/analysis/matlab/variations/naive_boxa_d0_13/learning_curve.png and b/analysis/matlab/variations/naive_boxa_d0_13/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d0_13/learning_curve_rate.png b/analysis/matlab/variations/naive_boxa_d0_13/learning_curve_rate.png
new file mode 100644
index 0000000..1962484
Binary files /dev/null and b/analysis/matlab/variations/naive_boxa_d0_13/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d0_13/logpower_result.txt b/analysis/matlab/variations/naive_boxa_d0_13/logpower_result.txt
index 264e541..c0b8d44 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/logpower_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_13/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_13
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=8   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,134)=4.865  p(resid)=0.02911  p(Satt)=0.02918 (
   honest per-animal random slope (log-day): F(1,7.9)=3.22  p=0.1109
 Cohen's f (interaction, partial eta^2=0.007) = 0.083  (small; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+8.27, ratSD=8.74, resSD=14.51) ---
+--- power simulation (log-day ground truth: stim:logday=+8.265, ratSD=8.74, resSD=14.51) ---
 
- true stim:log(day) = +8.27 (100% of observed)
+ true stim:log(day) = +8.265 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.21            |  0.55  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.083  (small; f: .10 small, .25
   16       |  0.98            |  0.98
   24       |  1.00            |  1.00
 
- true stim:log(day) = +4.13 (50% of observed)
+ true stim:log(day) = +4.133 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.08            |  0.17  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.007) = 0.083  (small; f: .10 small, .25
   16       |  0.64            |  0.68
   24       |  0.76            |  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_boxa_d0_13/logpower_result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_13/logpower_result_rate.txt
new file mode 100644
index 0000000..21868f6
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_13/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=8   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,132)=5.963  p(resid)=0.01593  p(Satt)=0.01598 (df=127)
+  honest per-animal random slope (log-day): F(1,8.1)=4.16  p=0.07502
+Cohen's f (interaction, partial eta^2=0.012) = 0.111  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.06399, ratSD=0.05139, resSD=0.1) ---
+
+ true stim:log(day) = +0.06399 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.26            |  0.66  <- observed
+  5        |  0.57            |  0.78
+  8        |  0.93            |  0.96  <- observed
+  12       |  1.00            |  1.00
+  16       |  0.99            |  0.99
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.032 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.09            |  0.23  <- observed
+  5        |  0.23            |  0.28
+  8        |  0.48            |  0.53  <- observed
+  12       |  0.55            |  0.61
+  16       |  0.76            |  0.78
+  24       |  0.85            |  0.82
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_boxa_d0_13/logpowersim.m b/analysis/matlab/variations/naive_boxa_d0_13/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/logpowersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_13/plotcurve.m b/analysis/matlab/variations/naive_boxa_d0_13/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/plotcurve.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_13/power_result.txt b/analysis/matlab/variations/naive_boxa_d0_13/power_result.txt
index 37e92dd..8282c2b 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/power_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_13/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_boxa_d0_13
+POWER SIMULATION -- naive_boxa_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=8   nrep=120, alpha=0.05
-ground truth: stim:day=+1.31/day, rat SD=8.76, residual SD=15.18, days=14
+ground truth: stim:day=+1.306/day, rat SD=8.76, residual SD=15.18, days=14
 
- true stim:day interaction = +1.31 (100% of observed)
+ true stim:day interaction = +1.306 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.12            |  0.32  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.31/day, rat SD=8.76, residual SD=15.18, days=14
   16       |  0.95            |  0.96
   24       |  0.97            |  0.98
 
- true stim:day interaction = +0.65 (50% of observed)
+ true stim:day interaction = +0.6532 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.17  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.31/day, rat SD=8.76, residual SD=15.18, days=14
   16       |  0.42            |  0.51
   24       |  0.62            |  0.60
 
-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_boxa_d0_13/power_result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_13/power_result_rate.txt
new file mode 100644
index 0000000..9ebdf75
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_13/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_boxa_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=8   nrep=120, alpha=0.05
+ground truth: stim:day=+0.009461/day, rat SD=0.04773, residual SD=0.1046, days=14
+
+ true stim:day interaction = +0.009461 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.13            |  0.37  <- observed
+  5        |  0.40            |  0.52
+  8        |  0.70            |  0.72  <- observed
+  12       |  0.96            |  0.96
+  16       |  0.97            |  0.97
+  24       |  0.99            |  0.99
+
+ true stim:day interaction = +0.00473 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.07            |  0.18  <- observed
+  5        |  0.18            |  0.20
+  8        |  0.34            |  0.32  <- observed
+  12       |  0.38            |  0.39
+  16       |  0.48            |  0.53
+  24       |  0.64            |  0.63
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_boxa_d0_13/powersim.m b/analysis/matlab/variations/naive_boxa_d0_13/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/powersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_13/result.txt b/analysis/matlab/variations/naive_boxa_d0_13/result.txt
index 506fa7e..0f3226c 100644
--- a/analysis/matlab/variations/naive_boxa_d0_13/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_13/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_boxa_d0_13
+VARIATION: naive_boxa_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(134)=  1.74 F(1)=  3.015  p=0.08481    p=0.08489 (df=129)
 day (learning)             t(134)= 16.74 F(1)=280.201  p=1.213e-34    p=2.161e-34 (df=131)
 stim (main, window start)  t(134)=  1.48 F(1)=  2.198  p=0.1405    p=0.1519 (df=23)
-interaction 95% CI: [-0.18, +2.79]
+interaction 95% CI: [-0.1817, +2.794]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,25.3)=2.32, p=0.1403
   (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=0.08481, slope diff=+1.31)
-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=0.08481, slope diff=+1.306)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d0_13/result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_13/result_rate.txt
new file mode 100644
index 0000000..24c125d
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_13/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_boxa_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-A, Electrode-Box-A2, Naive
+N = 11 rats, 136 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             136
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -202.35    -184.87    107.17           -214.35 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE           tStat     DF     pValue    
+    {'(Intercept)'}         0.22695     0.026364    8.6084    132    1.9298e-14
+    {'day'        }        0.034099    0.0028279    12.058    132    4.9136e-23
+    {'stim'       }        0.061777     0.049323    1.2525    132        0.2126
+    {'day:stim'   }        0.009461    0.0052125    1.8151    132      0.071786
+
+
+    Lower          Upper   
+         0.1748      0.2791
+       0.028506    0.039693
+      -0.035788     0.15934
+    -0.00084987    0.019772
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.047732
+
+
+    Lower       Upper   
+    0.026198    0.086966
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.10455     0.092342    0.11837
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(132)=  1.82 F(1)=  3.294  p=0.07179    p=0.07185 (df=128)
+day (learning)             t(132)= 12.06 F(1)=145.397  p=4.914e-23    p=5.237e-23 (df=131)
+stim (main, window start)  t(132)=  1.25 F(1)=  1.569  p=0.2126    p=0.2207 (df=28)
+interaction 95% CI: [-0.0008499, +0.01977]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,10.0)=2.86, p=0.1216
+  (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=0.07179, slope diff=+0.009461)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d0_5/analyze.m b/analysis/matlab/variations/naive_boxa_d0_5/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_5/learning_curve.png b/analysis/matlab/variations/naive_boxa_d0_5/learning_curve.png
index c59672a..ec2dd12 100644
Binary files a/analysis/matlab/variations/naive_boxa_d0_5/learning_curve.png and b/analysis/matlab/variations/naive_boxa_d0_5/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d0_5/learning_curve_rate.png b/analysis/matlab/variations/naive_boxa_d0_5/learning_curve_rate.png
new file mode 100644
index 0000000..a7b3c3b
Binary files /dev/null and b/analysis/matlab/variations/naive_boxa_d0_5/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d0_5/logpower_result.txt b/analysis/matlab/variations/naive_boxa_d0_5/logpower_result.txt
index 02048c0..51e2c1f 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/logpower_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_5/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d0_5
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=8   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,61)=8.016  p(resid)=0.006273  p(Satt)=0.006505
   honest per-animal random slope (log-day): F(1,15.5)=6.72  p=0.01995
 Cohen's f (interaction, partial eta^2=0.048) = 0.223  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+18.30, ratSD=8.82, resSD=13.99) ---
+--- power simulation (log-day ground truth: stim:logday=+18.3, ratSD=8.823, resSD=13.99) ---
 
- true stim:log(day) = +18.30 (100% of observed)
+ true stim:log(day) = +18.3 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.33            |  0.66  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.048) = 0.223  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +9.15 (50% of observed)
+ true stim:log(day) = +9.151 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.11            |  0.24  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.048) = 0.223  (small-medium; f: .10 smal
   16       |  0.74            |  0.78
   24       |  0.93            |  0.94
 
-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_boxa_d0_5/logpower_result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_5/logpower_result_rate.txt
new file mode 100644
index 0000000..705f41e
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_5/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=8   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,59)=12.241  p(resid)=0.0008959  p(Satt)=0.000969 (df=52)
+  honest per-animal random slope (log-day): F(1,13.9)=10.72  p=0.005603
+Cohen's f (interaction, partial eta^2=0.104) = 0.340  (medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1706, ratSD=0.04897, resSD=0.1038) ---
+
+ true stim:log(day) = +0.1706 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.45            |  0.84  <- observed
+  5        |  0.87            |  0.96
+  8        |  1.00            |  1.00  <- observed
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.08531 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.31  <- observed
+  5        |  0.36            |  0.48
+  8        |  0.58            |  0.67  <- observed
+  12       |  0.76            |  0.78
+  16       |  0.89            |  0.91
+  24       |  0.99            |  0.99
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_boxa_d0_5/logpowersim.m b/analysis/matlab/variations/naive_boxa_d0_5/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/logpowersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_5/plotcurve.m b/analysis/matlab/variations/naive_boxa_d0_5/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/plotcurve.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_5/power_result.txt b/analysis/matlab/variations/naive_boxa_d0_5/power_result.txt
index 6223e39..e584fe8 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/power_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_5/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_boxa_d0_5
+POWER SIMULATION -- naive_boxa_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=8   nrep=120, alpha=0.05
-ground truth: stim:day=+6.40/day, rat SD=9.10, residual SD=12.48, days=6
+ground truth: stim:day=+6.403/day, rat SD=9.103, residual SD=12.48, days=6
 
- true stim:day interaction = +6.40 (100% of observed)
+ true stim:day interaction = +6.403 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.36            |  0.77  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+6.40/day, rat SD=9.10, residual SD=12.48, days=6
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:day interaction = +3.20 (50% of observed)
+ true stim:day interaction = +3.202 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.11            |  0.24  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+6.40/day, rat SD=9.10, residual SD=12.48, days=6
   16       |  0.82            |  0.84
   24       |  0.95            |  0.94
 
-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_boxa_d0_5/power_result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_5/power_result_rate.txt
new file mode 100644
index 0000000..72c4d9a
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_5/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_boxa_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=8   nrep=120, alpha=0.05
+ground truth: stim:day=+0.05713/day, rat SD=0.05041, residual SD=0.09994, days=6
+
+ true stim:day interaction = +0.05713 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.42            |  0.85  <- observed
+  5        |  0.86            |  0.96
+  8        |  1.00            |  1.00  <- observed
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.02856 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.32  <- observed
+  5        |  0.32            |  0.44
+  8        |  0.57            |  0.66  <- observed
+  12       |  0.74            |  0.79
+  16       |  0.89            |  0.88
+  24       |  0.97            |  0.99
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_boxa_d0_5/powersim.m b/analysis/matlab/variations/naive_boxa_d0_5/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/powersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d0_5/result.txt b/analysis/matlab/variations/naive_boxa_d0_5/result.txt
index f8f2c4c..23ba3d9 100644
--- a/analysis/matlab/variations/naive_boxa_d0_5/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d0_5/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_boxa_d0_5
+VARIATION: naive_boxa_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(61)=  3.15 F(1)=  9.909  p=0.002545    p=0.002679 (df=54)
 day (learning)             t(61)=  8.14 F(1)= 66.293  p=2.506e-11    p=5.765e-11 (df=54)
 stim (main, window start)  t(61)=  0.15 F(1)=  0.022  p=0.8814    p=0.8822 (df=24)
-interaction 95% CI: [+2.34, +10.47]
+interaction 95% CI: [+2.336, +10.47]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,10.5)=5.80, p=0.03561
   (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=0.002545, slope diff=+6.40)
-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=0.002545, slope diff=+6.403)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d0_5/result_rate.txt b/analysis/matlab/variations/naive_boxa_d0_5/result_rate.txt
new file mode 100644
index 0000000..f869ce0
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d0_5/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_boxa_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-A, Electrode-Box-A2, Naive
+N = 11 rats, 63 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              63
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -89.552    -76.693    50.776           -101.55 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat       DF
+    {'(Intercept)'}          0.20761      0.03353      6.1919    59
+    {'day'        }         0.035262    0.0090474      3.8975    59
+    {'stim'       }        -0.042773     0.060953    -0.70173    59
+    {'day:stim'   }         0.057126     0.016495      3.4631    59
+
+
+    pValue        Lower       Upper   
+    6.1978e-08     0.14052     0.27471
+      0.000251    0.017158    0.053365
+        0.4856    -0.16474    0.079194
+     0.0010002    0.024119    0.090133
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.050413
+
+
+    Lower      Upper  
+    0.02388    0.10643
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.099938    0.082348    0.12129
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(59)=  3.46 F(1)= 11.993  p=0.001    p=0.00108 (df=52)
+day (learning)             t(59)=  3.90 F(1)= 15.190  p=0.000251    p=0.0002756 (df=53)
+stim (main, window start)  t(59)= -0.70 F(1)=  0.492  p=0.4856    p=0.4879 (df=32)
+interaction 95% CI: [+0.02412, +0.09013]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,12.0)=9.63, p=0.009127
+  (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=0.001, slope diff=+0.05713)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d6_10/analyze.m b/analysis/matlab/variations/naive_boxa_d6_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d6_10/learning_curve.png b/analysis/matlab/variations/naive_boxa_d6_10/learning_curve.png
index 4e66928..604d88b 100644
Binary files a/analysis/matlab/variations/naive_boxa_d6_10/learning_curve.png and b/analysis/matlab/variations/naive_boxa_d6_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d6_10/learning_curve_rate.png b/analysis/matlab/variations/naive_boxa_d6_10/learning_curve_rate.png
new file mode 100644
index 0000000..a572fc9
Binary files /dev/null and b/analysis/matlab/variations/naive_boxa_d6_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d6_10/logpower_result.txt b/analysis/matlab/variations/naive_boxa_d6_10/logpower_result.txt
index 08f1714..e09ca2d 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/logpower_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d6_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_10
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=7   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,46)=0.613  p(resid)=0.4376  p(Satt)=0.4382 (df=
   honest per-animal random slope (log-day): F(1,10.0)=0.43  p=0.5247
 Cohen's f (interaction, partial eta^2=0.005) = 0.069  (small; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=-15.28, ratSD=11.24, resSD=10.10) ---
+--- power simulation (log-day ground truth: stim:logday=-15.28, ratSD=11.24, resSD=10.1) ---
 
  true stim:log(day) = -15.28 (100% of observed)
   N/group  | per-animal power | LME power
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.005) = 0.069  (small; f: .10 small, .25
   16       |  0.10            |  0.11
   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_boxa_d6_10/logpower_result_rate.txt b/analysis/matlab/variations/naive_boxa_d6_10/logpower_result_rate.txt
new file mode 100644
index 0000000..a2be137
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d6_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=7   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,46)=2.393  p(resid)=0.1287  p(Satt)=0.1297 (df=40)
+  honest per-animal random slope (log-day): F(1,10.0)=1.69  p=0.2224
+Cohen's f (interaction, partial eta^2=0.021) = 0.147  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=-0.1921, ratSD=0.0649, resSD=0.06429) ---
+
+ true stim:log(day) = -0.1921 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.25  <- observed
+  5        |  0.24            |  0.36
+  8        |  0.49            |  0.56
+  12       |  0.78            |  0.80
+  16       |  0.88            |  0.89
+  24       |  0.97            |  0.95
+
+ true stim:log(day) = -0.09607 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.09  <- observed
+  5        |  0.07            |  0.12
+  8        |  0.17            |  0.21
+  12       |  0.17            |  0.17
+  16       |  0.33            |  0.37
+  24       |  0.36            |  0.38
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_boxa_d6_10/logpowersim.m b/analysis/matlab/variations/naive_boxa_d6_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/logpowersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d6_10/plotcurve.m b/analysis/matlab/variations/naive_boxa_d6_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/plotcurve.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d6_10/power_result.txt b/analysis/matlab/variations/naive_boxa_d6_10/power_result.txt
index 0da438b..5bc7b7a 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/power_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d6_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_boxa_d6_10
+POWER SIMULATION -- naive_boxa_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=7   nrep=120, alpha=0.05
-ground truth: stim:day=-1.62/day, rat SD=11.24, residual SD=10.14, days=5
+ground truth: stim:day=-1.619/day, rat SD=11.24, residual SD=10.14, days=5
 
- true stim:day interaction = -1.62 (100% of observed)
+ true stim:day interaction = -1.619 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.05            |  0.08  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=-1.62/day, rat SD=11.24, residual SD=10.14, days=5
   16       |  0.28            |  0.30
   24       |  0.41            |  0.45
 
- true stim:day interaction = -0.81 (50% of observed)
+ true stim:day interaction = -0.8095 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.03            |  0.08  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=-1.62/day, rat SD=11.24, residual SD=10.14, days=5
   16       |  0.11            |  0.09
   24       |  0.12            |  0.13
 
-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_boxa_d6_10/power_result_rate.txt b/analysis/matlab/variations/naive_boxa_d6_10/power_result_rate.txt
new file mode 100644
index 0000000..b58db33
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d6_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_boxa_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=7   nrep=120, alpha=0.05
+ground truth: stim:day=-0.02112/day, rat SD=0.06482, residual SD=0.06465, days=5
+
+ true stim:day interaction = -0.02112 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.10            |  0.23  <- observed
+  5        |  0.21            |  0.33
+  8        |  0.47            |  0.53
+  12       |  0.73            |  0.78
+  16       |  0.87            |  0.87
+  24       |  0.93            |  0.93
+
+ true stim:day interaction = -0.01056 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.10  <- observed
+  5        |  0.07            |  0.11
+  8        |  0.16            |  0.21
+  12       |  0.15            |  0.17
+  16       |  0.32            |  0.38
+  24       |  0.33            |  0.35
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_boxa_d6_10/powersim.m b/analysis/matlab/variations/naive_boxa_d6_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/powersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d6_10/result.txt b/analysis/matlab/variations/naive_boxa_d6_10/result.txt
index d33c85c..f918c3a 100644
--- a/analysis/matlab/variations/naive_boxa_d6_10/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d6_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_boxa_d6_10
+VARIATION: naive_boxa_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(46)= -0.73 F(1)=  0.535  p=0.4682    p=0.4687 (df=40)
 day (learning)             t(46)=  4.77 F(1)= 22.779  p=1.877e-05    p=2.436e-05 (df=40)
 stim (main, window start)  t(46)=  2.54 F(1)=  6.429  p=0.01469    p=0.02198 (df=16)
-interaction 95% CI: [-6.07, +2.84]
+interaction 95% CI: [-6.074, +2.836]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,10.0)=0.41, p=0.5344
   (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=0.4682, slope diff=-1.62)
-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=0.4682, slope diff=-1.619)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d6_10/result_rate.txt b/analysis/matlab/variations/naive_boxa_d6_10/result_rate.txt
new file mode 100644
index 0000000..cd9fa18
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d6_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_boxa_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-A, Electrode-Box-A2, Naive
+N = 10 rats, 50 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              50
+    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
+    -102.02    -90.545    57.008           -114.02 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat      DF    pValue    
+    {'(Intercept)'}           0.4775     0.030961     15.422    46    8.4559e-20
+    {'day'        }         0.033319    0.0077275     4.3117    46    8.4717e-05
+    {'stim'       }           0.1661     0.056527     2.9385    46     0.0051416
+    {'day:stim'   }        -0.021119     0.014108    -1.4969    46       0.14124
+
+
+    Lower        Upper    
+      0.41518      0.53982
+     0.017764     0.048873
+     0.052321      0.27989
+    -0.049518    0.0072795
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.064825
+
+
+    Lower       Upper  
+    0.038261    0.10983
+
+Group: Error
+    Name               Estimate    Lower      Upper   
+    {'Res Std'}        0.064653    0.05193    0.080492
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(46)= -1.50 F(1)=  2.241  p=0.1412    p=0.1423 (df=40)
+day (learning)             t(46)=  4.31 F(1)= 18.591  p=8.472e-05    p=0.0001028 (df=40)
+stim (main, window start)  t(46)=  2.94 F(1)=  8.635  p=0.005142    p=0.009071 (df=17)
+interaction 95% CI: [-0.04952, +0.00728]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,10.0)=1.71, p=0.2198
+  (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=0.1412, slope diff=-0.02112)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d6_13/analyze.m b/analysis/matlab/variations/naive_boxa_d6_13/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/analyze.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d6_13/learning_curve.png b/analysis/matlab/variations/naive_boxa_d6_13/learning_curve.png
index 01ba17f..480a1ee 100644
Binary files a/analysis/matlab/variations/naive_boxa_d6_13/learning_curve.png and b/analysis/matlab/variations/naive_boxa_d6_13/learning_curve.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d6_13/learning_curve_rate.png b/analysis/matlab/variations/naive_boxa_d6_13/learning_curve_rate.png
new file mode 100644
index 0000000..7258b0b
Binary files /dev/null and b/analysis/matlab/variations/naive_boxa_d6_13/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/naive_boxa_d6_13/logpower_result.txt b/analysis/matlab/variations/naive_boxa_d6_13/logpower_result.txt
index 25c42f8..71ba4a8 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/logpower_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d6_13/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_d6_13
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=7   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,69)=0.239  p(resid)=0.6263  p(Satt)=0.6264 (df=
   honest per-animal random slope (log-day): F(1,9.1)=0.07  p=0.8013
 Cohen's f (interaction, partial eta^2=0.001) = 0.034  (small; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+6.70, ratSD=10.78, resSD=11.38) ---
+--- power simulation (log-day ground truth: stim:logday=+6.701, ratSD=10.78, resSD=11.38) ---
 
- true stim:log(day) = +6.70 (100% of observed)
+ true stim:log(day) = +6.701 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.05            |  0.07  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.001) = 0.034  (small; f: .10 small, .25
   16       |  0.06            |  0.07
   24       |  0.12            |  0.11
 
-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_boxa_d6_13/logpower_result_rate.txt b/analysis/matlab/variations/naive_boxa_d6_13/logpower_result_rate.txt
new file mode 100644
index 0000000..98940c6
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d6_13/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- naive_boxa_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=7   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,69)=0.000  p(resid)=0.9874  p(Satt)=0.9874 (df=65)
+  honest per-animal random slope (log-day): F(1,8.6)=0.03  p=0.8653
+Cohen's f (interaction, partial eta^2=0.000) = 0.000  (small; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.001382, ratSD=0.06213, resSD=0.07275) ---
+
+ true stim:log(day) = +0.001382 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.06  <- observed
+  5        |  0.08            |  0.07
+  8        |  0.01            |  0.02
+  12       |  0.03            |  0.05
+  16       |  0.05            |  0.07
+  24       |  0.02            |  0.03
+
+ true stim:log(day) = +0.0006912 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.04            |  0.06  <- observed
+  5        |  0.04            |  0.04
+  8        |  0.08            |  0.04
+  12       |  0.05            |  0.04
+  16       |  0.05            |  0.04
+  24       |  0.05            |  0.04
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/naive_boxa_d6_13/logpowersim.m b/analysis/matlab/variations/naive_boxa_d6_13/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/logpowersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d6_13/plotcurve.m b/analysis/matlab/variations/naive_boxa_d6_13/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/plotcurve.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d6_13/power_result.txt b/analysis/matlab/variations/naive_boxa_d6_13/power_result.txt
index f391e92..40d1ec7 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/power_result.txt
+++ b/analysis/matlab/variations/naive_boxa_d6_13/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- naive_boxa_d6_13
+POWER SIMULATION -- naive_boxa_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=7   nrep=120, alpha=0.05
-ground truth: stim:day=+0.87/day, rat SD=10.75, residual SD=11.53, days=8
+ground truth: stim:day=+0.8737/day, rat SD=10.75, residual SD=11.53, days=8
 
- true stim:day interaction = +0.87 (100% of observed)
+ true stim:day interaction = +0.8737 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.11  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+0.87/day, rat SD=10.75, residual SD=11.53, days=8
   16       |  0.28            |  0.33
   24       |  0.38            |  0.41
 
- true stim:day interaction = +0.44 (50% of observed)
+ true stim:day interaction = +0.4368 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.06            |  0.10  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+0.87/day, rat SD=10.75, residual SD=11.53, days=8
   16       |  0.07            |  0.07
   24       |  0.16            |  0.15
 
-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_boxa_d6_13/power_result_rate.txt b/analysis/matlab/variations/naive_boxa_d6_13/power_result_rate.txt
new file mode 100644
index 0000000..d8f2f16
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d6_13/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- naive_boxa_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=7   nrep=120, alpha=0.05
+ground truth: stim:day=+0.001651/day, rat SD=0.06197, residual SD=0.07346, days=8
+
+ true stim:day interaction = +0.001651 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.04  <- observed
+  5        |  0.10            |  0.08
+  8        |  0.05            |  0.03
+  12       |  0.04            |  0.05
+  16       |  0.06            |  0.07
+  24       |  0.03            |  0.06
+
+ true stim:day interaction = +0.0008253 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.05            |  0.07  <- observed
+  5        |  0.04            |  0.06
+  8        |  0.07            |  0.05
+  12       |  0.03            |  0.05
+  16       |  0.03            |  0.05
+  24       |  0.07            |  0.07
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/naive_boxa_d6_13/powersim.m b/analysis/matlab/variations/naive_boxa_d6_13/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/powersim.m
+++ b/analysis/matlab/variations/naive_boxa_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_boxa_d6_13/result.txt b/analysis/matlab/variations/naive_boxa_d6_13/result.txt
index 869fbd6..ce8141f 100644
--- a/analysis/matlab/variations/naive_boxa_d6_13/result.txt
+++ b/analysis/matlab/variations/naive_boxa_d6_13/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: naive_boxa_d6_13
+VARIATION: naive_boxa_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(69)=  0.63 F(1)=  0.397  p=0.5308    p=0.5309 (df=65)
 day (learning)             t(69)=  4.26 F(1)= 18.109  p=6.446e-05    p=6.931e-05 (df=64)
 stim (main, window start)  t(69)=  2.21 F(1)=  4.886  p=0.03039    p=0.0412 (df=17)
-interaction 95% CI: [-1.89, +3.64]
+interaction 95% CI: [-1.893, +3.641]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,8.5)=0.16, p=0.6966
   (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=0.5308, slope diff=+0.87)
-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=0.5308, slope diff=+0.8737)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/naive_boxa_d6_13/result_rate.txt b/analysis/matlab/variations/naive_boxa_d6_13/result_rate.txt
new file mode 100644
index 0000000..db3fcde
--- /dev/null
+++ b/analysis/matlab/variations/naive_boxa_d6_13/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: naive_boxa_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-A, Electrode-Box-A2, Naive
+N = 10 rats, 73 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              73
+    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
+    -143.92    -130.17    77.958           -155.92 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat      DF    pValue    
+    {'(Intercept)'}          0.50639     0.029658     17.074    69    1.7857e-26
+    {'day'        }         0.013881    0.0046444     2.9887    69     0.0038784
+    {'stim'       }          0.13179      0.05426     2.4289    69      0.017756
+    {'day:stim'   }        0.0016506    0.0088296    0.18693    69       0.85226
+
+
+    Lower        Upper   
+      0.44722     0.56555
+    0.0046157    0.023146
+     0.023544     0.24004
+    -0.015964    0.019265
+
+Random effects covariance parameters (95% CIs):
+Group: rat (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.061972
+
+
+    Lower       Upper  
+    0.036939    0.10397
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.073458    0.061716    0.087434
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(69)=  0.19 F(1)=  0.035  p=0.8523    p=0.8523 (df=65)
+day (learning)             t(69)=  2.99 F(1)=  8.933  p=0.003878    p=0.003964 (df=64)
+stim (main, window start)  t(69)=  2.43 F(1)=  5.899  p=0.01776    p=0.02572 (df=18)
+interaction 95% CI: [-0.01596, +0.01927]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,8.1)=0.00, p=0.9836
+  (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=0.8523, slope diff=+0.001651)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/prev_f_full/analyze.m b/analysis/matlab/variations/prev_f_full/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/prev_f_full/analyze.m
+++ b/analysis/matlab/variations/prev_f_full/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/prev_f_full/learning_curve.png b/analysis/matlab/variations/prev_f_full/learning_curve.png
index 5b7419f..7971adb 100644
Binary files a/analysis/matlab/variations/prev_f_full/learning_curve.png and b/analysis/matlab/variations/prev_f_full/learning_curve.png differ
diff --git a/analysis/matlab/variations/prev_f_full/learning_curve_rate.png b/analysis/matlab/variations/prev_f_full/learning_curve_rate.png
new file mode 100644
index 0000000..8a6ddf1
Binary files /dev/null and b/analysis/matlab/variations/prev_f_full/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/prev_f_full/logpower_result.txt b/analysis/matlab/variations/prev_f_full/logpower_result.txt
index 2cac7ea..cc0652b 100644
--- a/analysis/matlab/variations/prev_f_full/logpower_result.txt
+++ b/analysis/matlab/variations/prev_f_full/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- prev_f_full
+LOG-DAY MODEL + COHEN'S f + POWER -- prev_f_full   [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=12, control n=12   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,227)=8.356  p(resid)=0.004218  p(Satt)=0.004253
   honest per-animal random slope (log-day): F(1,24.3)=3.32  p=0.08079
 Cohen's f (interaction, partial eta^2=0.012) = 0.112  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+2.39, ratSD=4.33, resSD=4.34) ---
+--- power simulation (log-day ground truth: stim:logday=+2.389, ratSD=4.328, resSD=4.338) ---
 
- true stim:log(day) = +2.39 (100% of observed)
+ true stim:log(day) = +2.389 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.15            |  0.28
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.012) = 0.112  (small-medium; f: .10 smal
   16       |  0.92            |  0.94
   24       |  0.99            |  0.98
 
- true stim:log(day) = +1.19 (50% of observed)
+ true stim:log(day) = +1.195 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.05            |  0.09
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.012) = 0.112  (small-medium; f: .10 smal
   16       |  0.46            |  0.49
   24       |  0.46            |  0.49
 
-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/prev_f_full/logpower_result_rate.txt b/analysis/matlab/variations/prev_f_full/logpower_result_rate.txt
new file mode 100644
index 0000000..9156a86
--- /dev/null
+++ b/analysis/matlab/variations/prev_f_full/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- prev_f_full   [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=12, control n=12   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,227)=2.888  p(resid)=0.0906  p(Satt)=0.09072 (df=208)
+  honest per-animal random slope (log-day): F(1,23.7)=2.05  p=0.1655
+Cohen's f (interaction, partial eta^2=0.006) = 0.079  (small; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.02595, ratSD=0.06838, resSD=0.08017) ---
+
+ true stim:log(day) = +0.02595 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.06            |  0.07
+  5        |  0.17            |  0.18
+  8        |  0.33            |  0.38
+  12       |  0.38            |  0.47  <- observed
+  16       |  0.55            |  0.62
+  24       |  0.64            |  0.60
+
+ true stim:log(day) = +0.01298 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.04            |  0.04
+  5        |  0.07            |  0.11
+  8        |  0.10            |  0.12
+  12       |  0.17            |  0.17  <- observed
+  16       |  0.10            |  0.15
+  24       |  0.15            |  0.17
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/prev_f_full/logpowersim.m b/analysis/matlab/variations/prev_f_full/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/prev_f_full/logpowersim.m
+++ b/analysis/matlab/variations/prev_f_full/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/prev_f_full/plotcurve.m b/analysis/matlab/variations/prev_f_full/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/prev_f_full/plotcurve.m
+++ b/analysis/matlab/variations/prev_f_full/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/prev_f_full/power_result.txt b/analysis/matlab/variations/prev_f_full/power_result.txt
index 9368d88..91f2c44 100644
--- a/analysis/matlab/variations/prev_f_full/power_result.txt
+++ b/analysis/matlab/variations/prev_f_full/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- prev_f_full
+POWER SIMULATION -- prev_f_full   [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=12, control n=12   nrep=120, alpha=0.05
-ground truth: stim:day=+0.56/day, rat SD=4.32, residual SD=4.49, days=10
+ground truth: stim:day=+0.5602/day, rat SD=4.316, residual SD=4.489, days=10
 
- true stim:day interaction = +0.56 (100% of observed)
+ true stim:day interaction = +0.5602 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.13            |  0.27
@@ -15,7 +15,7 @@ ground truth: stim:day=+0.56/day, rat SD=4.32, residual SD=4.49, days=10
   16       |  0.91            |  0.93
   24       |  0.98            |  0.98
 
- true stim:day interaction = +0.28 (50% of observed)
+ true stim:day interaction = +0.2801 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.04            |  0.07
@@ -25,5 +25,4 @@ ground truth: stim:day=+0.56/day, rat SD=4.32, residual SD=4.49, days=10
   16       |  0.33            |  0.37
   24       |  0.45            |  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/prev_f_full/power_result_rate.txt b/analysis/matlab/variations/prev_f_full/power_result_rate.txt
new file mode 100644
index 0000000..8970c48
--- /dev/null
+++ b/analysis/matlab/variations/prev_f_full/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- prev_f_full   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=12, control n=12   nrep=120, alpha=0.05
+ground truth: stim:day=+0.006292/day, rat SD=0.06824, residual SD=0.08152, days=10
+
+ true stim:day interaction = +0.006292 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.06            |  0.09
+  5        |  0.17            |  0.16
+  8        |  0.28            |  0.35
+  12       |  0.37            |  0.41  <- observed
+  16       |  0.55            |  0.62
+  24       |  0.62            |  0.62
+
+ true stim:day interaction = +0.003146 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.04
+  5        |  0.07            |  0.05
+  8        |  0.12            |  0.15
+  12       |  0.17            |  0.18  <- observed
+  16       |  0.12            |  0.17
+  24       |  0.15            |  0.13
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/prev_f_full/powersim.m b/analysis/matlab/variations/prev_f_full/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/prev_f_full/powersim.m
+++ b/analysis/matlab/variations/prev_f_full/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/prev_f_full/result.txt b/analysis/matlab/variations/prev_f_full/result.txt
index 0383c95..3b0d61b 100644
--- a/analysis/matlab/variations/prev_f_full/result.txt
+++ b/analysis/matlab/variations/prev_f_full/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: prev_f_full
+VARIATION: prev_f_full   [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(227)=  2.66 F(1)=  7.088  p=0.008315    p=0.008365 (df=208)
 day (learning)             t(227)=  8.94 F(1)= 79.838  p=1.43e-16    p=2.16e-16 (df=209)
 stim (main, window start)  t(227)=  0.92 F(1)=  0.839  p=0.3606    p=0.3656 (df=37)
-interaction 95% CI: [+0.15, +0.97]
+interaction 95% CI: [+0.1456, +0.9749]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,24.0)=3.01, p=0.09541
   (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=0.008315, slope diff=+0.56)
-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=0.008315, slope diff=+0.5602)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/prev_f_full/result_rate.txt b/analysis/matlab/variations/prev_f_full/result_rate.txt
new file mode 100644
index 0000000..1ef97a5
--- /dev/null
+++ b/analysis/matlab/variations/prev_f_full/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: prev_f_full   [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): b2_f
+control   (stim=0): a2_f
+N = 24 rats, 231 sessions   raw day coverage: treat 0..9, control 0..9
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             231
+    Fixed effects coefficients           4
+    Random effects coefficients         24
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -441.62    -420.96    226.81           -453.62 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE           tStat     DF     pValue    
+    {'(Intercept)'}         0.25306     0.024155    10.477    227    3.3213e-21
+    {'day'        }        0.017111    0.0027396     6.246    227    2.0536e-09
+    {'stim'       }        0.040889     0.034129    1.1981    227       0.23214
+    {'day:stim'   }        0.006292    0.0038198    1.6472    227        0.1009
+
+
+    Lower         Upper   
+       0.20547     0.30066
+      0.011713     0.02251
+     -0.026361     0.10814
+    -0.0012348    0.013819
+
+Random effects covariance parameters (95% CIs):
+Group: rat (24 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.06824 
+
+
+    Lower       Upper   
+    0.049364    0.094332
+
+Group: Error
+    Name               Estimate    Lower      Upper  
+    {'Res Std'}        0.081522    0.07404    0.08976
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(227)=  1.65 F(1)=  2.713  p=0.1009    p=0.101 (df=209)
+day (learning)             t(227)=  6.25 F(1)= 39.012  p=2.054e-09    p=2.316e-09 (df=209)
+stim (main, window start)  t(227)=  1.20 F(1)=  1.435  p=0.2321    p=0.2378 (df=41)
+interaction 95% CI: [-0.001235, +0.01382]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,22.9)=1.78, p=0.1956
+  (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=0.1009, slope diff=+0.006292)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.