diff --git a/analysis/matlab/variations/boxa_a2_d0_10/analyze.m b/analysis/matlab/variations/boxa_a2_d0_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/boxa_a2_d0_10/analyze.m
+++ b/analysis/matlab/variations/boxa_a2_d0_10/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/learning_curve.png b/analysis/matlab/variations/boxa_a2_d0_10/learning_curve.png
index 3a10526..3cf72c5 100644
Binary files a/analysis/matlab/variations/boxa_a2_d0_10/learning_curve.png and b/analysis/matlab/variations/boxa_a2_d0_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/learning_curve_rate.png b/analysis/matlab/variations/boxa_a2_d0_10/learning_curve_rate.png
new file mode 100644
index 0000000..f3e0092
Binary files /dev/null and b/analysis/matlab/variations/boxa_a2_d0_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/logpower_result.txt b/analysis/matlab/variations/boxa_a2_d0_10/logpower_result.txt
index bc3cd4f..bacc259 100644
--- a/analysis/matlab/variations/boxa_a2_d0_10/logpower_result.txt
+++ b/analysis/matlab/variations/boxa_a2_d0_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d0_10
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d0_10   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = # successes (count))
 log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
 observed groups: stim n=3, control n=4   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,68)=3.821  p(resid)=0.05474  p(Satt)=0.05451 (d
   honest per-animal random slope (log-day): F(1,14.6)=3.22  p=0.09348
 Cohen's f (interaction, partial eta^2=0.009) = 0.097  (small; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+8.69, ratSD=0.00, resSD=13.42) ---
+--- power simulation (log-day ground truth: stim:logday=+8.69, ratSD=2.979e-15, resSD=13.42) ---
 
  true stim:log(day) = +8.69 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.009) = 0.097  (small; f: .10 small, .25
   16       |  0.99            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +4.34 (50% of observed)
+ true stim:log(day) = +4.345 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.12  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.009) = 0.097  (small; f: .10 small, .25
   16       |  0.62            |  0.64
   24       |  0.75            |  0.78
 
-Read the per-animal column as the honest power; the LME column matches the
-paper's power code (anova interaction p, observation-level DF) and is optimistic.
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/logpower_result_rate.txt b/analysis/matlab/variations/boxa_a2_d0_10/logpower_result_rate.txt
new file mode 100644
index 0000000..b9524d1
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d0_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d0_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
+observed groups: stim n=3, control n=4   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,68)=5.247  p(resid)=0.02509  p(Satt)=0.02516 (df=66)
+  honest per-animal random slope (log-day): F(1,25.2)=4.88  p=0.0364
+Cohen's f (interaction, partial eta^2=0.017) = 0.131  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.06695, ratSD=0.03723, resSD=0.0875) ---
+
+ true stim:log(day) = +0.06695 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.25            |  0.57  <- observed
+  5        |  0.71            |  0.84
+  8        |  0.92            |  0.93
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.03347 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.09            |  0.16  <- observed
+  5        |  0.14            |  0.27
+  8        |  0.41            |  0.46
+  12       |  0.52            |  0.55
+  16       |  0.78            |  0.77
+  24       |  0.88            |  0.87
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/logpowersim.m b/analysis/matlab/variations/boxa_a2_d0_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/boxa_a2_d0_10/logpowersim.m
+++ b/analysis/matlab/variations/boxa_a2_d0_10/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/plotcurve.m b/analysis/matlab/variations/boxa_a2_d0_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/boxa_a2_d0_10/plotcurve.m
+++ b/analysis/matlab/variations/boxa_a2_d0_10/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/power_result.txt b/analysis/matlab/variations/boxa_a2_d0_10/power_result.txt
index 9543b93..2d1643a 100644
--- a/analysis/matlab/variations/boxa_a2_d0_10/power_result.txt
+++ b/analysis/matlab/variations/boxa_a2_d0_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- boxa_a2_d0_10
+POWER SIMULATION -- boxa_a2_d0_10   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=4   nrep=120, alpha=0.05
-ground truth: stim:day=+1.20/day, rat SD=0.00, residual SD=13.47, days=11
+ground truth: stim:day=+1.198/day, rat SD=0, residual SD=13.47, days=11
 
- true stim:day interaction = +1.20 (100% of observed)
+ true stim:day interaction = +1.198 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.17  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.20/day, rat SD=0.00, residual SD=13.47, days=11
   16       |  0.72            |  0.72
   24       |  0.88            |  0.88
 
- true stim:day interaction = +0.60 (50% of observed)
+ true stim:day interaction = +0.5992 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.08  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.20/day, rat SD=0.00, residual SD=13.47, days=11
   16       |  0.29            |  0.30
   24       |  0.31            |  0.33
 
-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/boxa_a2_d0_10/power_result_rate.txt b/analysis/matlab/variations/boxa_a2_d0_10/power_result_rate.txt
new file mode 100644
index 0000000..aa77d1b
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d0_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- boxa_a2_d0_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=4   nrep=120, alpha=0.05
+ground truth: stim:day=+0.007205/day, rat SD=0.04049, residual SD=0.08883, days=11
+
+ true stim:day interaction = +0.007205 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.07            |  0.16  <- observed
+  5        |  0.18            |  0.19
+  8        |  0.28            |  0.33
+  12       |  0.53            |  0.51
+  16       |  0.62            |  0.62
+  24       |  0.84            |  0.85
+
+ true stim:day interaction = +0.003602 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.05            |  0.07  <- observed
+  5        |  0.04            |  0.06
+  8        |  0.09            |  0.10
+  12       |  0.15            |  0.16
+  16       |  0.25            |  0.26
+  24       |  0.23            |  0.26
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/powersim.m b/analysis/matlab/variations/boxa_a2_d0_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/boxa_a2_d0_10/powersim.m
+++ b/analysis/matlab/variations/boxa_a2_d0_10/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/result.txt b/analysis/matlab/variations/boxa_a2_d0_10/result.txt
index 9d79605..55f74d6 100644
--- a/analysis/matlab/variations/boxa_a2_d0_10/result.txt
+++ b/analysis/matlab/variations/boxa_a2_d0_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: boxa_a2_d0_10
+VARIATION: boxa_a2_d0_10   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(68)=  1.18 F(1)=  1.397  p=0.2414    p=0.2412 (df=72)
 day (learning)             t(68)= 12.04 F(1)=144.862  p=1.64e-18    p=6.575e-19 (df=72)
 stim (main, window start)  t(68)=  1.16 F(1)=  1.336  p=0.2517    p=0.2515 (df=72)
-interaction 95% CI: [-0.83, +3.22]
+interaction 95% CI: [-0.8251, +3.222]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,14.0)=0.68, p=0.4245
   (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=0.2414, slope diff=+1.20)
-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=0.2414, slope diff=+1.198)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_a2_d0_10/result_rate.txt b/analysis/matlab/variations/boxa_a2_d0_10/result_rate.txt
new file mode 100644
index 0000000..6da1ffc
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d0_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: boxa_a2_d0_10   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A, Electrode-Box-A2
+N = 7 rats, 72 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              72
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC       LogLikelihood    Deviance
+    -124.36    -110.7    68.18            -136.36 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat      DF    pValue    
+    {'(Intercept)'}          0.21277     0.032537     6.5394    68    9.4885e-09
+    {'day'        }          0.04572    0.0046771     9.7753    68    1.3713e-14
+    {'stim'       }         0.042139     0.049417    0.85273    68        0.3968
+    {'day:stim'   }        0.0072048    0.0067664     1.0648    68       0.29074
+
+
+    Lower         Upper   
+       0.14785      0.2777
+      0.036387    0.055053
+     -0.056471     0.14075
+    -0.0062973    0.020707
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.040493
+
+
+    Lower       Upper   
+    0.018248    0.089857
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.088826    0.074767    0.10553
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(68)=  1.06 F(1)=  1.134  p=0.2907    p=0.2908 (df=67)
+day (learning)             t(68)=  9.78 F(1)= 95.556  p=1.371e-14    p=1.275e-14 (df=68)
+stim (main, window start)  t(68)=  0.85 F(1)=  0.727  p=0.3968    p=0.4044 (df=19)
+interaction 95% CI: [-0.006297, +0.02071]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,29.1)=0.97, p=0.3332
+  (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=0.2907, slope diff=+0.007205)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/analyze.m b/analysis/matlab/variations/boxa_a2_d0_5/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/boxa_a2_d0_5/analyze.m
+++ b/analysis/matlab/variations/boxa_a2_d0_5/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/learning_curve.png b/analysis/matlab/variations/boxa_a2_d0_5/learning_curve.png
index 5ca4e38..5cb589f 100644
Binary files a/analysis/matlab/variations/boxa_a2_d0_5/learning_curve.png and b/analysis/matlab/variations/boxa_a2_d0_5/learning_curve.png differ
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/learning_curve_rate.png b/analysis/matlab/variations/boxa_a2_d0_5/learning_curve_rate.png
new file mode 100644
index 0000000..4aa6fca
Binary files /dev/null and b/analysis/matlab/variations/boxa_a2_d0_5/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/logpower_result.txt b/analysis/matlab/variations/boxa_a2_d0_5/logpower_result.txt
index b43d1f5..9c1d27e 100644
--- a/analysis/matlab/variations/boxa_a2_d0_5/logpower_result.txt
+++ b/analysis/matlab/variations/boxa_a2_d0_5/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d0_5
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d0_5   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = # successes (count))
 log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
 observed groups: stim n=3, control n=4   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,38)=4.320  p(resid)=0.04447  p(Satt)=0.04382 (d
   honest per-animal random slope (log-day): F(1,10.2)=3.63  p=0.0853
 Cohen's f (interaction, partial eta^2=0.032) = 0.181  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+15.16, ratSD=0.00, resSD=14.15) ---
+--- power simulation (log-day ground truth: stim:logday=+15.16, ratSD=0, resSD=14.15) ---
 
  true stim:log(day) = +15.16 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.032) = 0.181  (small-medium; f: .10 smal
   16       |  1.00            |  0.99
   24       |  1.00            |  1.00
 
- true stim:log(day) = +7.58 (50% of observed)
+ true stim:log(day) = +7.581 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.08            |  0.23  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.032) = 0.181  (small-medium; f: .10 smal
   16       |  0.55            |  0.58
   24       |  0.85            |  0.85
 
-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/boxa_a2_d0_5/logpower_result_rate.txt b/analysis/matlab/variations/boxa_a2_d0_5/logpower_result_rate.txt
new file mode 100644
index 0000000..0acb5f5
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d0_5/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d0_5   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
+observed groups: stim n=3, control n=4   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,38)=8.750  p(resid)=0.005301  p(Satt)=0.005517 (df=35)
+  honest per-animal random slope (log-day): F(1,12.5)=7.35  p=0.01836
+Cohen's f (interaction, partial eta^2=0.099) = 0.331  (medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.143, ratSD=0.0361, resSD=0.09377) ---
+
+ true stim:log(day) = +0.143 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.41            |  0.80  <- observed
+  5        |  0.80            |  0.93
+  8        |  0.99            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.07149 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.29  <- observed
+  5        |  0.33            |  0.42
+  8        |  0.50            |  0.58
+  12       |  0.72            |  0.72
+  16       |  0.86            |  0.87
+  24       |  0.95            |  0.97
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/logpowersim.m b/analysis/matlab/variations/boxa_a2_d0_5/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/boxa_a2_d0_5/logpowersim.m
+++ b/analysis/matlab/variations/boxa_a2_d0_5/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/plotcurve.m b/analysis/matlab/variations/boxa_a2_d0_5/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/boxa_a2_d0_5/plotcurve.m
+++ b/analysis/matlab/variations/boxa_a2_d0_5/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/power_result.txt b/analysis/matlab/variations/boxa_a2_d0_5/power_result.txt
index 2b2b2ae..9524dd0 100644
--- a/analysis/matlab/variations/boxa_a2_d0_5/power_result.txt
+++ b/analysis/matlab/variations/boxa_a2_d0_5/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- boxa_a2_d0_5
+POWER SIMULATION -- boxa_a2_d0_5   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=4   nrep=120, alpha=0.05
-ground truth: stim:day=+4.68/day, rat SD=0.00, residual SD=12.01, days=6
+ground truth: stim:day=+4.676/day, rat SD=0, residual SD=12.01, days=6
 
- true stim:day interaction = +4.68 (100% of observed)
+ true stim:day interaction = +4.676 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.23            |  0.54  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+4.68/day, rat SD=0.00, residual SD=12.01, days=6
   16       |  0.99            |  0.99
   24       |  1.00            |  1.00
 
- true stim:day interaction = +2.34 (50% of observed)
+ true stim:day interaction = +2.338 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.08            |  0.20  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+4.68/day, rat SD=0.00, residual SD=12.01, days=6
   16       |  0.56            |  0.60
   24       |  0.82            |  0.84
 
-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/boxa_a2_d0_5/power_result_rate.txt b/analysis/matlab/variations/boxa_a2_d0_5/power_result_rate.txt
new file mode 100644
index 0000000..0eef27a
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d0_5/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- boxa_a2_d0_5   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=4   nrep=120, alpha=0.05
+ground truth: stim:day=+0.04308/day, rat SD=0.03963, residual SD=0.08479, days=6
+
+ true stim:day interaction = +0.04308 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.36            |  0.73  <- observed
+  5        |  0.78            |  0.91
+  8        |  0.98            |  0.98
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.02154 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.11            |  0.25  <- observed
+  5        |  0.26            |  0.38
+  8        |  0.44            |  0.46
+  12       |  0.68            |  0.72
+  16       |  0.81            |  0.81
+  24       |  0.95            |  0.94
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/powersim.m b/analysis/matlab/variations/boxa_a2_d0_5/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/boxa_a2_d0_5/powersim.m
+++ b/analysis/matlab/variations/boxa_a2_d0_5/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/result.txt b/analysis/matlab/variations/boxa_a2_d0_5/result.txt
index e88076d..74da2ac 100644
--- a/analysis/matlab/variations/boxa_a2_d0_5/result.txt
+++ b/analysis/matlab/variations/boxa_a2_d0_5/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: boxa_a2_d0_5
+VARIATION: boxa_a2_d0_5   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(38)=  2.13 F(1)=  4.551  p=0.03941    p=0.03878 (df=42)
 day (learning)             t(38)=  7.35 F(1)= 53.982  p=8.377e-09    p=4.651e-09 (df=42)
 stim (main, window start)  t(38)= -0.08 F(1)=  0.007  p=0.9342    p=0.9342 (df=42)
-interaction 95% CI: [+0.24, +9.11]
+interaction 95% CI: [+0.2389, +9.113]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,7.0)=2.87, p=0.1341
   (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=0.03941, slope diff=+4.68)
-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=0.03941, slope diff=+4.676)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_a2_d0_5/result_rate.txt b/analysis/matlab/variations/boxa_a2_d0_5/result_rate.txt
new file mode 100644
index 0000000..8796b45
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d0_5/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: boxa_a2_d0_5   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A, Electrode-Box-A2
+N = 7 rats, 42 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              42
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -70.223    -59.797    41.112           -82.223 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat       DF    pValue    
+    {'(Intercept)'}          0.19895    0.036526      5.4467    38    3.2657e-06
+    {'day'        }         0.049304    0.010134      4.8651    38    2.0216e-05
+    {'stim'       }        -0.034105    0.055794    -0.61127    38       0.54466
+    {'day:stim'   }         0.043083     0.01548      2.7831    38      0.008341
+
+
+    Lower       Upper   
+       0.125     0.27289
+    0.028788     0.06982
+    -0.14705    0.078844
+    0.011745    0.074421
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.039633
+
+
+    Lower       Upper  
+    0.015473    0.10151
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.084789    0.067081    0.10717
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(38)=  2.78 F(1)=  7.746  p=0.008341    p=0.008621 (df=35)
+day (learning)             t(38)=  4.87 F(1)= 23.669  p=2.022e-05    p=2.41e-05 (df=35)
+stim (main, window start)  t(38)= -0.61 F(1)=  0.374  p=0.5447    p=0.5472 (df=22)
+interaction 95% CI: [+0.01175, +0.07442]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,9.1)=5.64, p=0.0412
+  (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=0.008341, slope diff=+0.04308)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/analyze.m b/analysis/matlab/variations/boxa_a2_d6_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/boxa_a2_d6_10/analyze.m
+++ b/analysis/matlab/variations/boxa_a2_d6_10/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/learning_curve.png b/analysis/matlab/variations/boxa_a2_d6_10/learning_curve.png
index 48596de..fcbb813 100644
Binary files a/analysis/matlab/variations/boxa_a2_d6_10/learning_curve.png and b/analysis/matlab/variations/boxa_a2_d6_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/learning_curve_rate.png b/analysis/matlab/variations/boxa_a2_d6_10/learning_curve_rate.png
new file mode 100644
index 0000000..c1c8d4a
Binary files /dev/null and b/analysis/matlab/variations/boxa_a2_d6_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/logpower_result.txt b/analysis/matlab/variations/boxa_a2_d6_10/logpower_result.txt
index 0ceae98..aadd11a 100644
--- a/analysis/matlab/variations/boxa_a2_d6_10/logpower_result.txt
+++ b/analysis/matlab/variations/boxa_a2_d6_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d6_10
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d6_10   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = # successes (count))
 log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
 observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,26)=0.252  p(resid)=0.6199  p(Satt)=0.6202 (df=
   honest per-animal random slope (log-day): F(1,6.0)=0.19  p=0.6783
 Cohen's f (interaction, partial eta^2=0.004) = 0.065  (small; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+9.52, ratSD=6.32, resSD=8.30) ---
+--- power simulation (log-day ground truth: stim:logday=+9.522, ratSD=6.321, resSD=8.298) ---
 
- true stim:log(day) = +9.52 (100% of observed)
+ true stim:log(day) = +9.522 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.02            |  0.06  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.004) = 0.065  (small; f: .10 small, .25
   16       |  0.14            |  0.16
   24       |  0.32            |  0.34
 
- true stim:log(day) = +4.76 (50% of observed)
+ true stim:log(day) = +4.761 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.06            |  0.10  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.004) = 0.065  (small; f: .10 small, .25
   16       |  0.09            |  0.12
   24       |  0.12            |  0.15
 
-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/boxa_a2_d6_10/logpower_result_rate.txt b/analysis/matlab/variations/boxa_a2_d6_10/logpower_result_rate.txt
new file mode 100644
index 0000000..de8b0bb
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d6_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_a2_d6_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
+observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,26)=0.064  p(resid)=0.8023  p(Satt)=0.8025 (df=24)
+  honest per-animal random slope (log-day): F(1,6.0)=0.04  p=0.8568
+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=-0.03226, ratSD=0.04467, resSD=0.05581) ---
+
+ true stim:log(day) = -0.03226 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.05            |  0.05  <- observed
+  5        |  0.03            |  0.03
+  8        |  0.07            |  0.08
+  12       |  0.12            |  0.12
+  16       |  0.15            |  0.12
+  24       |  0.08            |  0.09
+
+ true stim:log(day) = -0.01613 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.07  <- observed
+  5        |  0.07            |  0.04
+  8        |  0.04            |  0.03
+  12       |  0.06            |  0.07
+  16       |  0.06            |  0.05
+  24       |  0.07            |  0.07
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/logpowersim.m b/analysis/matlab/variations/boxa_a2_d6_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/boxa_a2_d6_10/logpowersim.m
+++ b/analysis/matlab/variations/boxa_a2_d6_10/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/plotcurve.m b/analysis/matlab/variations/boxa_a2_d6_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/boxa_a2_d6_10/plotcurve.m
+++ b/analysis/matlab/variations/boxa_a2_d6_10/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/power_result.txt b/analysis/matlab/variations/boxa_a2_d6_10/power_result.txt
index a531d82..baa3a43 100644
--- a/analysis/matlab/variations/boxa_a2_d6_10/power_result.txt
+++ b/analysis/matlab/variations/boxa_a2_d6_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- boxa_a2_d6_10
+POWER SIMULATION -- boxa_a2_d6_10   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
-ground truth: stim:day=+1.00/day, rat SD=6.34, residual SD=8.21, days=5
+ground truth: stim:day=+1/day, rat SD=6.344, residual SD=8.208, days=5
 
- true stim:day interaction = +1.00 (100% of observed)
+ true stim:day interaction = +1 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.03            |  0.05  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.00/day, rat SD=6.34, residual SD=8.21, days=5
   16       |  0.12            |  0.14
   24       |  0.27            |  0.32
 
- true stim:day interaction = +0.50 (50% of observed)
+ true stim:day interaction = +0.5 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.05            |  0.09  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.00/day, rat SD=6.34, residual SD=8.21, days=5
   16       |  0.08            |  0.11
   24       |  0.12            |  0.12
 
-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/boxa_a2_d6_10/power_result_rate.txt b/analysis/matlab/variations/boxa_a2_d6_10/power_result_rate.txt
new file mode 100644
index 0000000..2974399
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d6_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- boxa_a2_d6_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
+ground truth: stim:day=-0.003749/day, rat SD=0.04471, residual SD=0.05566, days=5
+
+ true stim:day interaction = -0.003749 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.04  <- observed
+  5        |  0.03            |  0.04
+  8        |  0.07            |  0.08
+  12       |  0.11            |  0.13
+  16       |  0.15            |  0.12
+  24       |  0.09            |  0.09
+
+ true stim:day interaction = -0.001874 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.07  <- observed
+  5        |  0.07            |  0.04
+  8        |  0.04            |  0.03
+  12       |  0.05            |  0.07
+  16       |  0.06            |  0.05
+  24       |  0.07            |  0.06
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/powersim.m b/analysis/matlab/variations/boxa_a2_d6_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/boxa_a2_d6_10/powersim.m
+++ b/analysis/matlab/variations/boxa_a2_d6_10/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/result.txt b/analysis/matlab/variations/boxa_a2_d6_10/result.txt
index ff825b2..89a0020 100644
--- a/analysis/matlab/variations/boxa_a2_d6_10/result.txt
+++ b/analysis/matlab/variations/boxa_a2_d6_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: boxa_a2_d6_10
+VARIATION: boxa_a2_d6_10   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(26)=  0.47 F(1)=  0.223  p=0.6409    p=0.6413 (df=24)
 day (learning)             t(26)=  2.11 F(1)=  4.466  p=0.04433    p=0.04517 (df=24)
 stim (main, window start)  t(26)=  1.73 F(1)=  2.983  p=0.09599    p=0.1083 (df=13)
-interaction 95% CI: [-3.36, +5.36]
+interaction 95% CI: [-3.356, +5.356]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.18, p=0.6872
   (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=0.6409, slope diff=+1.00)
-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=0.6409, slope diff=+1)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_a2_d6_10/result_rate.txt b/analysis/matlab/variations/boxa_a2_d6_10/result_rate.txt
new file mode 100644
index 0000000..9e57932
--- /dev/null
+++ b/analysis/matlab/variations/boxa_a2_d6_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: boxa_a2_d6_10   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A, Electrode-Box-A2
+N = 6 rats, 30 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              30
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -67.521    -59.114    39.761           -79.521 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate      SE          tStat       DF
+    {'(Intercept)'}           0.54193    0.035861      15.112    26
+    {'day'        }          0.015949    0.010163      1.5693    26
+    {'stim'       }           0.10168    0.050715      2.0049    26
+    {'day:stim'   }        -0.0037489    0.014373    -0.26083    26
+
+
+    pValue        Lower         Upper   
+    2.1656e-14       0.46821     0.61564
+       0.12868    -0.0049418    0.036839
+       0.05549    -0.0025676     0.20592
+       0.79628     -0.033292    0.025795
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.044709
+
+
+    Lower       Upper   
+    0.021196    0.094306
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.055665    0.041949    0.073866
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(26)= -0.26 F(1)=  0.068  p=0.7963    p=0.7964 (df=24)
+day (learning)             t(26)=  1.57 F(1)=  2.463  p=0.1287    p=0.1297 (df=24)
+stim (main, window start)  t(26)=  2.00 F(1)=  4.020  p=0.05549    p=0.06743 (df=12)
+interaction 95% CI: [-0.03329, +0.02579]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.04, p=0.8467
+  (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=0.7963, slope diff=-0.003749)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_b2_d0_10/analyze.m b/analysis/matlab/variations/boxa_b2_d0_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/boxa_b2_d0_10/analyze.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d0_10/learning_curve.png b/analysis/matlab/variations/boxa_b2_d0_10/learning_curve.png
index 64415b4..99834fa 100644
Binary files a/analysis/matlab/variations/boxa_b2_d0_10/learning_curve.png and b/analysis/matlab/variations/boxa_b2_d0_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/boxa_b2_d0_10/learning_curve_rate.png b/analysis/matlab/variations/boxa_b2_d0_10/learning_curve_rate.png
new file mode 100644
index 0000000..34e00b2
Binary files /dev/null and b/analysis/matlab/variations/boxa_b2_d0_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/boxa_b2_d0_10/logpower_result.txt b/analysis/matlab/variations/boxa_b2_d0_10/logpower_result.txt
index b4a06b3..708e186 100644
--- a/analysis/matlab/variations/boxa_b2_d0_10/logpower_result.txt
+++ b/analysis/matlab/variations/boxa_b2_d0_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_d0_10
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_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=4, control n=3   nrep=120, alpha=0.05
 
@@ -10,9 +10,9 @@ stim x log(day) interaction: F(1,68)=5.859  p(resid)=0.01817  p(Satt)=0.0182 (df
   honest per-animal random slope (log-day): F(1,21.8)=5.17  p=0.03322
 Cohen's f (interaction, partial eta^2=0.013) = 0.115  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+10.40, ratSD=5.52, resSD=12.53) ---
+--- power simulation (log-day ground truth: stim:logday=+10.4, ratSD=5.517, resSD=12.53) ---
 
- true stim:log(day) = +10.40 (100% of observed)
+ true stim:log(day) = +10.4 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.32            |  0.62  <- observed
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.013) = 0.115  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +5.20 (50% of observed)
+ true stim:log(day) = +5.199 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.10            |  0.20  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.013) = 0.115  (small-medium; f: .10 smal
   16       |  0.86            |  0.85
   24       |  0.93            |  0.93
 
-Read the per-animal column as the honest power; the LME column matches the
-paper's power code (anova interaction p, observation-level DF) and is optimistic.
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/boxa_b2_d0_10/logpower_result_rate.txt b/analysis/matlab/variations/boxa_b2_d0_10/logpower_result_rate.txt
new file mode 100644
index 0000000..21c6c85
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d0_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_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=4, control n=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,68)=8.517  p(resid)=0.004766  p(Satt)=0.004783 (df=67)
+  honest per-animal random slope (log-day): F(1,40.6)=8.18  p=0.006648
+Cohen's f (interaction, partial eta^2=0.026) = 0.164  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.08588, ratSD=0.04132, resSD=0.08572) ---
+
+ true stim:log(day) = +0.08588 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.43            |  0.79  <- observed
+  5        |  0.89            |  0.95
+  8        |  0.98            |  0.99
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.04294 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.13            |  0.28  <- observed
+  5        |  0.30            |  0.43
+  8        |  0.65            |  0.62
+  12       |  0.79            |  0.82
+  16       |  0.93            |  0.93
+  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/boxa_b2_d0_10/logpowersim.m b/analysis/matlab/variations/boxa_b2_d0_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/boxa_b2_d0_10/logpowersim.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d0_10/plotcurve.m b/analysis/matlab/variations/boxa_b2_d0_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/boxa_b2_d0_10/plotcurve.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d0_10/power_result.txt b/analysis/matlab/variations/boxa_b2_d0_10/power_result.txt
index 0d19c3b..6840f87 100644
--- a/analysis/matlab/variations/boxa_b2_d0_10/power_result.txt
+++ b/analysis/matlab/variations/boxa_b2_d0_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- boxa_b2_d0_10
+POWER SIMULATION -- boxa_b2_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=4, control n=3   nrep=120, alpha=0.05
-ground truth: stim:day=+1.60/day, rat SD=6.05, residual SD=12.42, days=11
+ground truth: stim:day=+1.604/day, rat SD=6.047, residual SD=12.42, days=11
 
- true stim:day interaction = +1.60 (100% of observed)
+ true stim:day interaction = +1.604 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.17            |  0.33  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.60/day, rat SD=6.05, residual SD=12.42, days=11
   16       |  0.95            |  0.97
   24       |  1.00            |  1.00
 
- true stim:day interaction = +0.80 (50% of observed)
+ true stim:day interaction = +0.802 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.11            |  0.11  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.60/day, rat SD=6.05, residual SD=12.42, days=11
   16       |  0.51            |  0.53
   24       |  0.67            |  0.66
 
-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/boxa_b2_d0_10/power_result_rate.txt b/analysis/matlab/variations/boxa_b2_d0_10/power_result_rate.txt
new file mode 100644
index 0000000..32690f0
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d0_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- boxa_b2_d0_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=4, control n=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01317/day, rat SD=0.04399, residual SD=0.08748, days=11
+
+ true stim:day interaction = +0.01317 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.23            |  0.45  <- observed
+  5        |  0.56            |  0.71
+  8        |  0.82            |  0.86
+  12       |  0.99            |  0.99
+  16       |  0.98            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.006586 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.13  <- observed
+  5        |  0.12            |  0.19
+  8        |  0.33            |  0.34
+  12       |  0.38            |  0.42
+  16       |  0.64            |  0.64
+  24       |  0.77            |  0.77
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/boxa_b2_d0_10/powersim.m b/analysis/matlab/variations/boxa_b2_d0_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/boxa_b2_d0_10/powersim.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d0_10/result.txt b/analysis/matlab/variations/boxa_b2_d0_10/result.txt
index 0e12cda..4a601bc 100644
--- a/analysis/matlab/variations/boxa_b2_d0_10/result.txt
+++ b/analysis/matlab/variations/boxa_b2_d0_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: boxa_b2_d0_10
+VARIATION: boxa_b2_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(68)=  1.63 F(1)=  2.647  p=0.1084    p=0.1084 (df=68)
 day (learning)             t(68)= 10.15 F(1)=103.100  p=2.916e-15    p=2.368e-15 (df=69)
 stim (main, window start)  t(68)=  0.26 F(1)=  0.068  p=0.7946    p=0.7968 (df=18)
-interaction 95% CI: [-0.36, +3.57]
+interaction 95% CI: [-0.3634, +3.571]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,22.2)=2.05, p=0.1666
   (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=0.1084, slope diff=+1.60)
-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=0.1084, slope diff=+1.604)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_b2_d0_10/result_rate.txt b/analysis/matlab/variations/boxa_b2_d0_10/result_rate.txt
new file mode 100644
index 0000000..6335136
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d0_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: boxa_b2_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-A, Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2
+N = 7 rats, 72 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              72
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC        LogLikelihood    Deviance
+    -125.6    -111.94    68.799           -137.6  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat       DF    pValue   
+    {'(Intercept)'}          0.22991     0.038687      5.9428    68     1.07e-07
+    {'day'        }         0.040788    0.0055544      7.3434    68    3.402e-10
+    {'stim'       }        0.0034269     0.050885    0.067345    68       0.9465
+    {'day:stim'   }         0.013172    0.0069458      1.8964    68     0.062154
+
+
+    Lower          Upper   
+        0.15271     0.30711
+       0.029705    0.051872
+      -0.098113     0.10497
+    -0.00068802    0.027032
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.043995
+
+
+    Lower       Upper   
+    0.020897    0.092622
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.087482    0.073647    0.10392
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(68)=  1.90 F(1)=  3.596  p=0.06215    p=0.06217 (df=68)
+day (learning)             t(68)=  7.34 F(1)= 53.926  p=3.402e-10    p=3.124e-10 (df=69)
+stim (main, window start)  t(68)=  0.07 F(1)=  0.005  p=0.9465    p=0.9471 (df=18)
+interaction 95% CI: [-0.000688, +0.02703]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,62.1)=3.56, p=0.06388
+  (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=0.06215, slope diff=+0.01317)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_b2_d0_5/analyze.m b/analysis/matlab/variations/boxa_b2_d0_5/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/boxa_b2_d0_5/analyze.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d0_5/learning_curve.png b/analysis/matlab/variations/boxa_b2_d0_5/learning_curve.png
index 945d772..3ace26d 100644
Binary files a/analysis/matlab/variations/boxa_b2_d0_5/learning_curve.png and b/analysis/matlab/variations/boxa_b2_d0_5/learning_curve.png differ
diff --git a/analysis/matlab/variations/boxa_b2_d0_5/learning_curve_rate.png b/analysis/matlab/variations/boxa_b2_d0_5/learning_curve_rate.png
new file mode 100644
index 0000000..cde4eaa
Binary files /dev/null and b/analysis/matlab/variations/boxa_b2_d0_5/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/boxa_b2_d0_5/logpower_result.txt b/analysis/matlab/variations/boxa_b2_d0_5/logpower_result.txt
index f8c0f9e..16c27b6 100644
--- a/analysis/matlab/variations/boxa_b2_d0_5/logpower_result.txt
+++ b/analysis/matlab/variations/boxa_b2_d0_5/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_d0_5
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_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=4, control n=3   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,38)=3.165  p(resid)=0.08322  p(Satt)=0.08246 (d
   honest per-animal random slope (log-day): F(1,9.4)=2.59  p=0.1407
 Cohen's f (interaction, partial eta^2=0.026) = 0.163  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+13.68, ratSD=0.00, resSD=14.91) ---
+--- power simulation (log-day ground truth: stim:logday=+13.68, ratSD=0, resSD=14.91) ---
 
  true stim:log(day) = +13.68 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.026) = 0.163  (small-medium; f: .10 smal
   16       |  0.96            |  0.97
   24       |  0.98            |  0.99
 
- true stim:log(day) = +6.84 (50% of observed)
+ true stim:log(day) = +6.838 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.14  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.026) = 0.163  (small-medium; f: .10 smal
   16       |  0.43            |  0.49
   24       |  0.70            |  0.75
 
-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/boxa_b2_d0_5/logpower_result_rate.txt b/analysis/matlab/variations/boxa_b2_d0_5/logpower_result_rate.txt
new file mode 100644
index 0000000..21e5a53
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d0_5/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_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=4, control n=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,38)=6.782  p(resid)=0.01307  p(Satt)=0.01342 (df=35)
+  honest per-animal random slope (log-day): F(1,9.8)=4.90  p=0.05187
+Cohen's f (interaction, partial eta^2=0.073) = 0.280  (medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1288, ratSD=0.045, resSD=0.09595) ---
+
+ true stim:log(day) = +0.1288 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.35            |  0.69  <- observed
+  5        |  0.75            |  0.86
+  8        |  0.96            |  0.99
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.0644 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.23  <- observed
+  5        |  0.24            |  0.30
+  8        |  0.38            |  0.42
+  12       |  0.61            |  0.64
+  16       |  0.77            |  0.78
+  24       |  0.93            |  0.95
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/boxa_b2_d0_5/logpowersim.m b/analysis/matlab/variations/boxa_b2_d0_5/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/boxa_b2_d0_5/logpowersim.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d0_5/plotcurve.m b/analysis/matlab/variations/boxa_b2_d0_5/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/boxa_b2_d0_5/plotcurve.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d0_5/power_result.txt b/analysis/matlab/variations/boxa_b2_d0_5/power_result.txt
index acb4cba..387db8a 100644
--- a/analysis/matlab/variations/boxa_b2_d0_5/power_result.txt
+++ b/analysis/matlab/variations/boxa_b2_d0_5/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- boxa_b2_d0_5
+POWER SIMULATION -- boxa_b2_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=4, control n=3   nrep=120, alpha=0.05
-ground truth: stim:day=+4.71/day, rat SD=5.87, residual SD=11.30, days=6
+ground truth: stim:day=+4.707/day, rat SD=5.872, residual SD=11.3, days=6
 
- true stim:day interaction = +4.71 (100% of observed)
+ true stim:day interaction = +4.707 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.27            |  0.59  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+4.71/day, rat SD=5.87, residual SD=11.30, days=6
   16       |  0.99            |  0.99
   24       |  1.00            |  1.00
 
- true stim:day interaction = +2.35 (50% of observed)
+ true stim:day interaction = +2.354 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.10            |  0.20  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+4.71/day, rat SD=5.87, residual SD=11.30, days=6
   16       |  0.62            |  0.62
   24       |  0.85            |  0.87
 
-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/boxa_b2_d0_5/power_result_rate.txt b/analysis/matlab/variations/boxa_b2_d0_5/power_result_rate.txt
new file mode 100644
index 0000000..1e013ca
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d0_5/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- boxa_b2_d0_5   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=4, control n=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.0427/day, rat SD=0.04855, residual SD=0.08495, days=6
+
+ true stim:day interaction = +0.0427 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.35            |  0.74  <- observed
+  5        |  0.78            |  0.91
+  8        |  0.98            |  0.98
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.02135 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.11            |  0.25  <- observed
+  5        |  0.25            |  0.36
+  8        |  0.44            |  0.47
+  12       |  0.68            |  0.73
+  16       |  0.80            |  0.82
+  24       |  0.95            |  0.94
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/boxa_b2_d0_5/powersim.m b/analysis/matlab/variations/boxa_b2_d0_5/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/boxa_b2_d0_5/powersim.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d0_5/result.txt b/analysis/matlab/variations/boxa_b2_d0_5/result.txt
index c30cd85..8766375 100644
--- a/analysis/matlab/variations/boxa_b2_d0_5/result.txt
+++ b/analysis/matlab/variations/boxa_b2_d0_5/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: boxa_b2_d0_5
+VARIATION: boxa_b2_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(38)=  2.28 F(1)=  5.206  p=0.02821    p=0.02871 (df=35)
 day (learning)             t(38)=  6.32 F(1)= 39.947  p=2.071e-07    p=2.928e-07 (df=35)
 stim (main, window start)  t(38)= -0.61 F(1)=  0.378  p=0.5425    p=0.5456 (df=20)
-interaction 95% CI: [+0.53, +8.88]
+interaction 95% CI: [+0.5306, +8.884]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,7.0)=2.92, p=0.131
   (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=0.02821, slope diff=+4.71)
-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=0.02821, slope diff=+4.707)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_b2_d0_5/result_rate.txt b/analysis/matlab/variations/boxa_b2_d0_5/result_rate.txt
new file mode 100644
index 0000000..90cffcd
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d0_5/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: boxa_b2_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-A, Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2
+N = 7 rats, 42 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              42
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC        LogLikelihood    Deviance
+    -68.33    -57.904    40.165           -80.33  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat      DF    pValue    
+    {'(Intercept)'}           0.2186    0.045229     4.8332    38    2.2327e-05
+    {'day'        }         0.043366    0.011725     3.6987    38    0.00068267
+    {'stim'       }        -0.059971    0.059832    -1.0023    38       0.32253
+    {'day:stim'   }         0.042705     0.01551     2.7533    38     0.0089978
+
+
+    Lower       Upper   
+     0.12704     0.31016
+     0.01963    0.067101
+     -0.1811    0.061153
+    0.011306    0.074103
+
+Random effects covariance parameters (95% CIs):
+Group: rat (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.048546
+
+
+    Lower       Upper  
+    0.021809    0.10806
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.084953    0.067211    0.10738
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(38)=  2.75 F(1)=  7.581  p=0.008998    p=0.00929 (df=35)
+day (learning)             t(38)=  3.70 F(1)= 13.680  p=0.0006827    p=0.0007391 (df=35)
+stim (main, window start)  t(38)= -1.00 F(1)=  1.005  p=0.3225    p=0.3289 (df=19)
+interaction 95% CI: [+0.01131, +0.0741]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,8.8)=5.05, p=0.05179
+  (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=0.008998, slope diff=+0.0427)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_b2_d6_10/analyze.m b/analysis/matlab/variations/boxa_b2_d6_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/boxa_b2_d6_10/analyze.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d6_10/learning_curve.png b/analysis/matlab/variations/boxa_b2_d6_10/learning_curve.png
index 4622719..766bfc9 100644
Binary files a/analysis/matlab/variations/boxa_b2_d6_10/learning_curve.png and b/analysis/matlab/variations/boxa_b2_d6_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/boxa_b2_d6_10/learning_curve_rate.png b/analysis/matlab/variations/boxa_b2_d6_10/learning_curve_rate.png
new file mode 100644
index 0000000..c3e955c
Binary files /dev/null and b/analysis/matlab/variations/boxa_b2_d6_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/boxa_b2_d6_10/logpower_result.txt b/analysis/matlab/variations/boxa_b2_d6_10/logpower_result.txt
index 4c4b367..6068b9d 100644
--- a/analysis/matlab/variations/boxa_b2_d6_10/logpower_result.txt
+++ b/analysis/matlab/variations/boxa_b2_d6_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_d6_10
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_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=4, control n=2   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,26)=0.850  p(resid)=0.365  p(Satt)=0.3657 (df=2
   honest per-animal random slope (log-day): F(1,6.0)=0.67  p=0.4431
 Cohen's f (interaction, partial eta^2=0.014) = 0.118  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+18.32, ratSD=6.21, resSD=8.20) ---
+--- power simulation (log-day ground truth: stim:logday=+18.32, ratSD=6.212, resSD=8.198) ---
 
  true stim:log(day) = +18.32 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.014) = 0.118  (small-medium; f: .10 smal
   16       |  0.57            |  0.63
   24       |  0.75            |  0.78
 
- true stim:log(day) = +9.16 (50% of observed)
+ true stim:log(day) = +9.162 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.11
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.014) = 0.118  (small-medium; f: .10 smal
   16       |  0.20            |  0.20
   24       |  0.31            |  0.31
 
-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/boxa_b2_d6_10/logpower_result_rate.txt b/analysis/matlab/variations/boxa_b2_d6_10/logpower_result_rate.txt
new file mode 100644
index 0000000..7f58343
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d6_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- boxa_b2_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=4, control n=2   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,26)=0.195  p(resid)=0.6626  p(Satt)=0.6629 (df=24)
+  honest per-animal random slope (log-day): F(1,6.0)=0.11  p=0.7527
+Cohen's f (interaction, partial eta^2=0.004) = 0.059  (small; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.05955, ratSD=0.03763, resSD=0.05566) ---
+
+ true stim:log(day) = +0.05955 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.02            |  0.04
+  5        |  0.06            |  0.12
+  8        |  0.11            |  0.09
+  12       |  0.11            |  0.14
+  16       |  0.12            |  0.15
+  24       |  0.27            |  0.31
+
+ true stim:log(day) = +0.02978 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.06            |  0.09
+  5        |  0.07            |  0.07
+  8        |  0.04            |  0.07
+  12       |  0.09            |  0.08
+  16       |  0.09            |  0.10
+  24       |  0.12            |  0.15
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/boxa_b2_d6_10/logpowersim.m b/analysis/matlab/variations/boxa_b2_d6_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/boxa_b2_d6_10/logpowersim.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d6_10/plotcurve.m b/analysis/matlab/variations/boxa_b2_d6_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/boxa_b2_d6_10/plotcurve.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d6_10/power_result.txt b/analysis/matlab/variations/boxa_b2_d6_10/power_result.txt
index 8d66ba9..bf0fd85 100644
--- a/analysis/matlab/variations/boxa_b2_d6_10/power_result.txt
+++ b/analysis/matlab/variations/boxa_b2_d6_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- boxa_b2_d6_10
+POWER SIMULATION -- boxa_b2_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=4, control n=2   nrep=120, alpha=0.05
-ground truth: stim:day=+1.90/day, rat SD=6.23, residual SD=8.12, days=5
+ground truth: stim:day=+1.9/day, rat SD=6.231, residual SD=8.123, days=5
 
- true stim:day interaction = +1.90 (100% of observed)
+ true stim:day interaction = +1.9 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.04            |  0.17
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.90/day, rat SD=6.23, residual SD=8.12, days=5
   16       |  0.18            |  0.19
   24       |  0.25            |  0.28
 
-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/boxa_b2_d6_10/power_result_rate.txt b/analysis/matlab/variations/boxa_b2_d6_10/power_result_rate.txt
new file mode 100644
index 0000000..8a249eb
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d6_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- boxa_b2_d6_10   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=4, control n=2   nrep=120, alpha=0.05
+ground truth: stim:day=+0.006202/day, rat SD=0.03766, residual SD=0.05555, days=5
+
+ true stim:day interaction = +0.006202 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.04
+  5        |  0.08            |  0.13
+  8        |  0.08            |  0.08
+  12       |  0.10            |  0.13
+  16       |  0.12            |  0.11
+  24       |  0.24            |  0.24
+
+ true stim:day interaction = +0.003101 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.05            |  0.09
+  5        |  0.07            |  0.07
+  8        |  0.05            |  0.07
+  12       |  0.07            |  0.08
+  16       |  0.08            |  0.10
+  24       |  0.10            |  0.12
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/boxa_b2_d6_10/powersim.m b/analysis/matlab/variations/boxa_b2_d6_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/boxa_b2_d6_10/powersim.m
+++ b/analysis/matlab/variations/boxa_b2_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/boxa_b2_d6_10/result.txt b/analysis/matlab/variations/boxa_b2_d6_10/result.txt
index 4d49b78..12d3d33 100644
--- a/analysis/matlab/variations/boxa_b2_d6_10/result.txt
+++ b/analysis/matlab/variations/boxa_b2_d6_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: boxa_b2_d6_10
+VARIATION: boxa_b2_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(26)=  0.85 F(1)=  0.729  p=0.4009    p=0.4015 (df=24)
 day (learning)             t(26)=  1.32 F(1)=  1.746  p=0.1979    p=0.1989 (df=24)
 stim (main, window start)  t(26)=  1.56 F(1)=  2.448  p=0.1297    p=0.142 (df=13)
-interaction 95% CI: [-2.67, +6.47]
+interaction 95% CI: [-2.673, +6.473]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.61, p=0.4631
   (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=0.4009, slope diff=+1.90)
-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=0.4009, slope diff=+1.9)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/boxa_b2_d6_10/result_rate.txt b/analysis/matlab/variations/boxa_b2_d6_10/result_rate.txt
new file mode 100644
index 0000000..561a131
--- /dev/null
+++ b/analysis/matlab/variations/boxa_b2_d6_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: boxa_b2_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-A, Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2
+N = 6 rats, 30 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              30
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -69.131    -60.724    40.566           -81.131 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat      DF    pValue    
+    {'(Intercept)'}          0.52619    0.040432     13.014    26    6.8005e-13
+    {'day'        }        0.0099391    0.012422    0.80014    26       0.43088
+    {'stim'       }         0.099864    0.049519     2.0167    26      0.054168
+    {'day:stim'   }        0.0062025    0.015213     0.4077    26       0.68683
+
+
+    Lower         Upper   
+       0.44308      0.6093
+     -0.015594    0.035472
+    -0.0019243     0.20165
+     -0.025069    0.037474
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.037656
+
+
+    Lower       Upper   
+    0.016562    0.085613
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.055552    0.041864    0.073715
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(26)=  0.41 F(1)=  0.166  p=0.6868    p=0.6871 (df=24)
+day (learning)             t(26)=  0.80 F(1)=  0.640  p=0.4309    p=0.4315 (df=24)
+stim (main, window start)  t(26)=  2.02 F(1)=  4.067  p=0.05417    p=0.06308 (df=14)
+interaction 95% CI: [-0.02507, +0.03747]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=0.10, p=0.7623
+  (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=0.6868, slope diff=+0.006202)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.