diff --git a/analysis/matlab/variations/unmerge_d0_13/analyze.m b/analysis/matlab/variations/unmerge_d0_13/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/unmerge_d0_13/analyze.m
+++ b/analysis/matlab/variations/unmerge_d0_13/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/unmerge_d0_13/learning_curve.png b/analysis/matlab/variations/unmerge_d0_13/learning_curve.png
index 25f4432..6f6b35c 100644
Binary files a/analysis/matlab/variations/unmerge_d0_13/learning_curve.png and b/analysis/matlab/variations/unmerge_d0_13/learning_curve.png differ
diff --git a/analysis/matlab/variations/unmerge_d0_13/learning_curve_rate.png b/analysis/matlab/variations/unmerge_d0_13/learning_curve_rate.png
new file mode 100644
index 0000000..5c6cd0f
Binary files /dev/null and b/analysis/matlab/variations/unmerge_d0_13/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/unmerge_d0_13/logpower_result.txt b/analysis/matlab/variations/unmerge_d0_13/logpower_result.txt
index 260e0d5..804443f 100644
--- a/analysis/matlab/variations/unmerge_d0_13/logpower_result.txt
+++ b/analysis/matlab/variations/unmerge_d0_13/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_13
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_13   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = # successes (count))
 log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
 observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,66)=8.712  p(resid)=0.004373  p(Satt)=0.004357
   honest per-animal random slope (log-day): F(1,12.9)=6.96  p=0.02062
 Cohen's f (interaction, partial eta^2=0.015) = 0.124  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+11.06, ratSD=5.68, resSD=11.36) ---
+--- power simulation (log-day ground truth: stim:logday=+11.06, ratSD=5.676, resSD=11.36) ---
 
  true stim:log(day) = +11.06 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.015) = 0.124  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +5.53 (50% of observed)
+ true stim:log(day) = +5.528 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.18            |  0.38  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.015) = 0.124  (small-medium; f: .10 smal
   16       |  0.97            |  0.98
   24       |  0.98            |  0.98
 
-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/unmerge_d0_13/logpower_result_rate.txt b/analysis/matlab/variations/unmerge_d0_13/logpower_result_rate.txt
new file mode 100644
index 0000000..b329593
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d0_13/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
+observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,66)=12.243  p(resid)=0.0008416  p(Satt)=0.0008388 (df=66)
+  honest per-animal random slope (log-day): F(1,29.1)=11.46  p=0.00205
+Cohen's f (interaction, partial eta^2=0.030) = 0.176  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.08873, ratSD=0.04364, resSD=0.07673) ---
+
+ true stim:log(day) = +0.08873 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.71            |  0.97  <- observed
+  5        |  1.00            |  1.00
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.04437 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.26            |  0.52  <- observed
+  5        |  0.57            |  0.72
+  8        |  0.85            |  0.90
+  12       |  0.95            |  0.96
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/unmerge_d0_13/logpowersim.m b/analysis/matlab/variations/unmerge_d0_13/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/unmerge_d0_13/logpowersim.m
+++ b/analysis/matlab/variations/unmerge_d0_13/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/unmerge_d0_13/plotcurve.m b/analysis/matlab/variations/unmerge_d0_13/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/unmerge_d0_13/plotcurve.m
+++ b/analysis/matlab/variations/unmerge_d0_13/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/unmerge_d0_13/power_result.txt b/analysis/matlab/variations/unmerge_d0_13/power_result.txt
index ee890a5..919f97c 100644
--- a/analysis/matlab/variations/unmerge_d0_13/power_result.txt
+++ b/analysis/matlab/variations/unmerge_d0_13/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- unmerge_d0_13
+POWER SIMULATION -- unmerge_d0_13   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
-ground truth: stim:day=+1.55/day, rat SD=0.00, residual SD=14.68, days=14
+ground truth: stim:day=+1.547/day, rat SD=0, residual SD=14.68, days=14
 
- true stim:day interaction = +1.55 (100% of observed)
+ true stim:day interaction = +1.547 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.22            |  0.56  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.55/day, rat SD=0.00, residual SD=14.68, days=14
   16       |  0.98            |  0.98
   24       |  1.00            |  1.00
 
- true stim:day interaction = +0.77 (50% of observed)
+ true stim:day interaction = +0.7733 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.12            |  0.22  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.55/day, rat SD=0.00, residual SD=14.68, days=14
   16       |  0.64            |  0.71
   24       |  0.78            |  0.74
 
-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/unmerge_d0_13/power_result_rate.txt b/analysis/matlab/variations/unmerge_d0_13/power_result_rate.txt
new file mode 100644
index 0000000..391aec2
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d0_13/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- unmerge_d0_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01125/day, rat SD=0.03898, residual SD=0.0934, days=14
+
+ true stim:day interaction = +0.01125 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.28            |  0.66  <- observed
+  5        |  0.67            |  0.78
+  8        |  0.92            |  0.94
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.005624 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.15            |  0.25  <- observed
+  5        |  0.23            |  0.25
+  8        |  0.48            |  0.52
+  12       |  0.56            |  0.60
+  16       |  0.75            |  0.79
+  24       |  0.87            |  0.83
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/unmerge_d0_13/powersim.m b/analysis/matlab/variations/unmerge_d0_13/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/unmerge_d0_13/powersim.m
+++ b/analysis/matlab/variations/unmerge_d0_13/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/unmerge_d0_13/result.txt b/analysis/matlab/variations/unmerge_d0_13/result.txt
index 5de1bb3..e3ba5ea 100644
--- a/analysis/matlab/variations/unmerge_d0_13/result.txt
+++ b/analysis/matlab/variations/unmerge_d0_13/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: unmerge_d0_13
+VARIATION: unmerge_d0_13   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(66)=  1.65 F(1)=  2.721  p=0.1038    p=0.1035 (df=70)
 day (learning)             t(66)=  9.09 F(1)= 82.593  p=3.035e-13    p=1.823e-13 (df=70)
 stim (main, window start)  t(66)=  0.95 F(1)=  0.910  p=0.3436    p=0.3434 (df=70)
-interaction 95% CI: [-0.33, +3.42]
+interaction 95% CI: [-0.3252, +3.419]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,12.8)=1.64, p=0.2227
   (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=0.1038, slope diff=+1.55)
-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=0.1038, slope diff=+1.547)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d0_13/result_rate.txt b/analysis/matlab/variations/unmerge_d0_13/result_rate.txt
new file mode 100644
index 0000000..e4a445f
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d0_13/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: unmerge_d0_13   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2
+N = 6 rats, 70 sessions   raw day coverage: treat 0..13, control 0..13
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              70
+    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
+    -114.71    -101.22    63.353           -126.71 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE           tStat     DF    pValue    
+    {'(Intercept)'}         0.25633     0.036798    6.9659    66    1.8623e-09
+    {'day'        }        0.032331    0.0047766    6.7686    66    4.1691e-09
+    {'stim'       }         0.03233     0.051391    0.6291    66       0.53146
+    {'day:stim'   }        0.011248    0.0061725    1.8223    66       0.07294
+
+
+    Lower         Upper   
+       0.18286      0.3298
+      0.022794    0.041868
+     -0.070275     0.13493
+    -0.0010756    0.023572
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.038979
+
+
+    Lower       Upper   
+    0.016492    0.092125
+
+Group: Error
+    Name               Estimate    Lower      Upper  
+    {'Res Std'}        0.093402    0.07855    0.11106
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(66)=  1.82 F(1)=  3.321  p=0.07294    p=0.07278 (df=68)
+day (learning)             t(66)=  6.77 F(1)= 45.814  p=4.169e-09    p=3.37e-09 (df=70)
+stim (main, window start)  t(66)=  0.63 F(1)=  0.396  p=0.5315    p=0.5377 (df=17)
+interaction 95% CI: [-0.001076, +0.02357]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,41.8)=3.20, p=0.08108
+  (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=0.07294, slope diff=+0.01125)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d0_5/analyze.m b/analysis/matlab/variations/unmerge_d0_5/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/unmerge_d0_5/analyze.m
+++ b/analysis/matlab/variations/unmerge_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/unmerge_d0_5/learning_curve.png b/analysis/matlab/variations/unmerge_d0_5/learning_curve.png
index 6ba5896..270abdc 100644
Binary files a/analysis/matlab/variations/unmerge_d0_5/learning_curve.png and b/analysis/matlab/variations/unmerge_d0_5/learning_curve.png differ
diff --git a/analysis/matlab/variations/unmerge_d0_5/learning_curve_rate.png b/analysis/matlab/variations/unmerge_d0_5/learning_curve_rate.png
new file mode 100644
index 0000000..be1356c
Binary files /dev/null and b/analysis/matlab/variations/unmerge_d0_5/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/unmerge_d0_5/logpower_result.txt b/analysis/matlab/variations/unmerge_d0_5/logpower_result.txt
index 701968f..651818e 100644
--- a/analysis/matlab/variations/unmerge_d0_5/logpower_result.txt
+++ b/analysis/matlab/variations/unmerge_d0_5/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d0_5
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_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=3   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,32)=4.849  p(resid)=0.03498  p(Satt)=0.03414 (d
   honest per-animal random slope (log-day): F(1,7.2)=3.65  p=0.09636
 Cohen's f (interaction, partial eta^2=0.039) = 0.200  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+16.48, ratSD=0.00, resSD=13.58) ---
+--- power simulation (log-day ground truth: stim:logday=+16.48, ratSD=0, resSD=13.58) ---
 
  true stim:log(day) = +16.48 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.039) = 0.200  (small-medium; f: .10 smal
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:log(day) = +8.24 (50% of observed)
+ true stim:log(day) = +8.239 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.09            |  0.24  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.039) = 0.200  (small-medium; f: .10 smal
   16       |  0.69            |  0.69
   24       |  0.90            |  0.92
 
-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/unmerge_d0_5/logpower_result_rate.txt b/analysis/matlab/variations/unmerge_d0_5/logpower_result_rate.txt
new file mode 100644
index 0000000..67caa3c
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d0_5/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_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=3   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,32)=10.951  p(resid)=0.002322  p(Satt)=0.00244 (df=30)
+  honest per-animal random slope (log-day): F(1,8.5)=8.07  p=0.02043
+Cohen's f (interaction, partial eta^2=0.109) = 0.350  (medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1553, ratSD=0.04445, resSD=0.08516) ---
+
+ true stim:log(day) = +0.1553 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.54            |  0.90  <- observed
+  5        |  0.93            |  0.97
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.07765 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.13            |  0.38  <- observed
+  5        |  0.44            |  0.54
+  8        |  0.72            |  0.73
+  12       |  0.81            |  0.88
+  16       |  0.94            |  0.97
+  24       |  1.00            |  1.00
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/unmerge_d0_5/logpowersim.m b/analysis/matlab/variations/unmerge_d0_5/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/unmerge_d0_5/logpowersim.m
+++ b/analysis/matlab/variations/unmerge_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/unmerge_d0_5/plotcurve.m b/analysis/matlab/variations/unmerge_d0_5/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/unmerge_d0_5/plotcurve.m
+++ b/analysis/matlab/variations/unmerge_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/unmerge_d0_5/power_result.txt b/analysis/matlab/variations/unmerge_d0_5/power_result.txt
index ba0a995..186e2e0 100644
--- a/analysis/matlab/variations/unmerge_d0_5/power_result.txt
+++ b/analysis/matlab/variations/unmerge_d0_5/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- unmerge_d0_5
+POWER SIMULATION -- unmerge_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=3   nrep=120, alpha=0.05
-ground truth: stim:day=+5.36/day, rat SD=4.85, residual SD=10.70, days=6
+ground truth: stim:day=+5.362/day, rat SD=4.853, residual SD=10.7, days=6
 
- true stim:day interaction = +5.36 (100% of observed)
+ true stim:day interaction = +5.362 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.35            |  0.73  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+5.36/day, rat SD=4.85, residual SD=10.70, days=6
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:day interaction = +2.68 (50% of observed)
+ true stim:day interaction = +2.681 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.11            |  0.24  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+5.36/day, rat SD=4.85, residual SD=10.70, days=6
   16       |  0.79            |  0.81
   24       |  0.95            |  0.94
 
-Read the per-animal column as the honest power. At the observed N this study
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/unmerge_d0_5/power_result_rate.txt b/analysis/matlab/variations/unmerge_d0_5/power_result_rate.txt
new file mode 100644
index 0000000..0978b4a
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d0_5/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- unmerge_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=3   nrep=120, alpha=0.05
+ground truth: stim:day=+0.04902/day, rat SD=0.04642, residual SD=0.07858, days=6
+
+ true stim:day interaction = +0.04902 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.52            |  0.89  <- observed
+  5        |  0.92            |  0.97
+  8        |  1.00            |  1.00
+  12       |  1.00            |  1.00
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.02451 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.12            |  0.34  <- observed
+  5        |  0.40            |  0.48
+  8        |  0.62            |  0.72
+  12       |  0.82            |  0.87
+  16       |  0.94            |  0.95
+  24       |  0.99            |  0.99
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/unmerge_d0_5/powersim.m b/analysis/matlab/variations/unmerge_d0_5/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/unmerge_d0_5/powersim.m
+++ b/analysis/matlab/variations/unmerge_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/unmerge_d0_5/result.txt b/analysis/matlab/variations/unmerge_d0_5/result.txt
index 8224622..aff4159 100644
--- a/analysis/matlab/variations/unmerge_d0_5/result.txt
+++ b/analysis/matlab/variations/unmerge_d0_5/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: unmerge_d0_5
+VARIATION: unmerge_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(32)=  2.57 F(1)=  6.588  p=0.01515    p=0.0155 (df=30)
 day (learning)             t(32)=  6.67 F(1)= 44.528  p=1.571e-07    p=2.162e-07 (df=30)
 stim (main, window start)  t(32)= -0.40 F(1)=  0.163  p=0.6888    p=0.6906 (df=19)
-interaction 95% CI: [+1.11, +9.62]
+interaction 95% CI: [+1.107, +9.617]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,6.0)=3.02, p=0.1331
   (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=0.01515, slope diff=+5.36)
-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=0.01515, slope diff=+5.362)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d0_5/result_rate.txt b/analysis/matlab/variations/unmerge_d0_5/result_rate.txt
new file mode 100644
index 0000000..dfc4e73
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d0_5/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: unmerge_d0_5   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2
+N = 6 rats, 36 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              36
+    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
+    -62.201    -52.7    37.1             -74.201 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat       DF    pValue    
+    {'(Intercept)'}           0.2186    0.042385      5.1574    32    1.2572e-05
+    {'day'        }         0.043366    0.010845      3.9986    32     0.0003516
+    {'stim'       }        -0.053758    0.059942    -0.89683    32       0.37651
+    {'day:stim'   }         0.049022    0.015337      3.1962    32     0.0031273
+
+
+    Lower       Upper   
+     0.13226     0.30494
+    0.021275    0.065457
+    -0.17586     0.06834
+     0.01778    0.080263
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.046423
+
+
+    Lower       Upper  
+    0.019948    0.10804
+
+Group: Error
+    Name               Estimate    Lower       Upper  
+    {'Res Std'}        0.078581    0.061014    0.10121
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(32)=  3.20 F(1)= 10.216  p=0.003127    p=0.00327 (df=30)
+day (learning)             t(32)=  4.00 F(1)= 15.989  p=0.0003516    p=0.0003833 (df=30)
+stim (main, window start)  t(32)= -0.90 F(1)=  0.804  p=0.3765    p=0.3834 (df=16)
+interaction 95% CI: [+0.01778, +0.08026]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,7.1)=6.49, p=0.0379
+  (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=0.003127, slope diff=+0.04902)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d6_10/analyze.m b/analysis/matlab/variations/unmerge_d6_10/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/unmerge_d6_10/analyze.m
+++ b/analysis/matlab/variations/unmerge_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/unmerge_d6_10/learning_curve.png b/analysis/matlab/variations/unmerge_d6_10/learning_curve.png
index c30f023..ceb59d3 100644
Binary files a/analysis/matlab/variations/unmerge_d6_10/learning_curve.png and b/analysis/matlab/variations/unmerge_d6_10/learning_curve.png differ
diff --git a/analysis/matlab/variations/unmerge_d6_10/learning_curve_rate.png b/analysis/matlab/variations/unmerge_d6_10/learning_curve_rate.png
new file mode 100644
index 0000000..481dddd
Binary files /dev/null and b/analysis/matlab/variations/unmerge_d6_10/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/unmerge_d6_10/logpower_result.txt b/analysis/matlab/variations/unmerge_d6_10/logpower_result.txt
index 767499d..6218aef 100644
--- a/analysis/matlab/variations/unmerge_d6_10/logpower_result.txt
+++ b/analysis/matlab/variations/unmerge_d6_10/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d6_10
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_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=2   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,21)=0.581  p(resid)=0.4545  p(Satt)=0.4549 (df=
   honest per-animal random slope (log-day): F(1,5.0)=0.44  p=0.5382
 Cohen's f (interaction, partial eta^2=0.011) = 0.106  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+16.98, ratSD=6.08, resSD=8.72) ---
+--- power simulation (log-day ground truth: stim:logday=+16.98, ratSD=6.081, resSD=8.718) ---
 
  true stim:log(day) = +16.98 (100% of observed)
   N/group  | per-animal power | LME power
@@ -22,7 +22,7 @@ Cohen's f (interaction, partial eta^2=0.011) = 0.106  (small-medium; f: .10 smal
   16       |  0.47            |  0.50
   24       |  0.63            |  0.66
 
- true stim:log(day) = +8.49 (50% of observed)
+ true stim:log(day) = +8.489 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.11  <- observed
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.011) = 0.106  (small-medium; f: .10 smal
   16       |  0.17            |  0.17
   24       |  0.26            |  0.26
 
-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/unmerge_d6_10/logpower_result_rate.txt b/analysis/matlab/variations/unmerge_d6_10/logpower_result_rate.txt
new file mode 100644
index 0000000..db06e2d
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d6_10/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_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=2   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,21)=0.027  p(resid)=0.8704  p(Satt)=0.8705 (df=20)
+  honest per-animal random slope (log-day): F(1,5.0)=0.01  p=0.9117
+Cohen's f (interaction, partial eta^2=0.001) = 0.023  (small; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.02357, ratSD=0.03992, resSD=0.05586) ---
+
+ true stim:log(day) = +0.02357 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.03  <- observed
+  5        |  0.03            |  0.06
+  8        |  0.06            |  0.07
+  12       |  0.08            |  0.09
+  16       |  0.05            |  0.06
+  24       |  0.08            |  0.06
+
+ true stim:log(day) = +0.01179 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.08  <- observed
+  5        |  0.07            |  0.07
+  8        |  0.05            |  0.05
+  12       |  0.07            |  0.07
+  16       |  0.07            |  0.07
+  24       |  0.06            |  0.04
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/unmerge_d6_10/logpowersim.m b/analysis/matlab/variations/unmerge_d6_10/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/unmerge_d6_10/logpowersim.m
+++ b/analysis/matlab/variations/unmerge_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/unmerge_d6_10/plotcurve.m b/analysis/matlab/variations/unmerge_d6_10/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/unmerge_d6_10/plotcurve.m
+++ b/analysis/matlab/variations/unmerge_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/unmerge_d6_10/power_result.txt b/analysis/matlab/variations/unmerge_d6_10/power_result.txt
index f3d18a4..eb55bdd 100644
--- a/analysis/matlab/variations/unmerge_d6_10/power_result.txt
+++ b/analysis/matlab/variations/unmerge_d6_10/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- unmerge_d6_10
+POWER SIMULATION -- unmerge_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=2   nrep=120, alpha=0.05
-ground truth: stim:day=+1.77/day, rat SD=6.11, residual SD=8.63, days=5
+ground truth: stim:day=+1.767/day, rat SD=6.107, residual SD=8.629, days=5
 
- true stim:day interaction = +1.77 (100% of observed)
+ true stim:day interaction = +1.767 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.05            |  0.10  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+1.77/day, rat SD=6.11, residual SD=8.63, days=5
   16       |  0.44            |  0.42
   24       |  0.57            |  0.58
 
- true stim:day interaction = +0.88 (50% of observed)
+ true stim:day interaction = +0.8833 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.07            |  0.11  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+1.77/day, rat SD=6.11, residual SD=8.63, days=5
   16       |  0.13            |  0.17
   24       |  0.22            |  0.23
 
-Read the per-animal column as the honest power. At the observed N this study
-is typically underpowered; per-animal power reaches ~0.8 only at larger N.
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/unmerge_d6_10/power_result_rate.txt b/analysis/matlab/variations/unmerge_d6_10/power_result_rate.txt
new file mode 100644
index 0000000..f441b34
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d6_10/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- unmerge_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=2   nrep=120, alpha=0.05
+ground truth: stim:day=+0.002261/day, rat SD=0.03997, residual SD=0.0557, days=5
+
+ true stim:day interaction = +0.002261 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.04  <- observed
+  5        |  0.03            |  0.06
+  8        |  0.05            |  0.05
+  12       |  0.08            |  0.07
+  16       |  0.05            |  0.07
+  24       |  0.06            |  0.06
+
+ true stim:day interaction = +0.00113 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.03            |  0.08  <- observed
+  5        |  0.07            |  0.07
+  8        |  0.05            |  0.04
+  12       |  0.06            |  0.07
+  16       |  0.07            |  0.07
+  24       |  0.06            |  0.04
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/unmerge_d6_10/powersim.m b/analysis/matlab/variations/unmerge_d6_10/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/unmerge_d6_10/powersim.m
+++ b/analysis/matlab/variations/unmerge_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/unmerge_d6_10/result.txt b/analysis/matlab/variations/unmerge_d6_10/result.txt
index 7064129..04a5132 100644
--- a/analysis/matlab/variations/unmerge_d6_10/result.txt
+++ b/analysis/matlab/variations/unmerge_d6_10/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: unmerge_d6_10
+VARIATION: unmerge_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(21)=  0.71 F(1)=  0.503  p=0.486    p=0.4864 (df=20)
 day (learning)             t(21)=  1.24 F(1)=  1.547  p=0.2273    p=0.2279 (df=20)
 stim (main, window start)  t(21)=  1.73 F(1)=  3.008  p=0.09752    p=0.1098 (df=11)
-interaction 95% CI: [-3.41, +6.95]
+interaction 95% CI: [-3.414, +6.947]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,5.0)=0.40, p=0.5549
   (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=0.486, slope diff=+1.77)
-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=0.486, slope diff=+1.767)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d6_10/result_rate.txt b/analysis/matlab/variations/unmerge_d6_10/result_rate.txt
new file mode 100644
index 0000000..ef071bd
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d6_10/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: unmerge_d6_10   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2
+N = 5 rats, 25 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              25
+    Fixed effects coefficients           4
+    Random effects coefficients          5
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -55.071    -47.758    33.535           -67.071 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE          tStat      DF    pValue    
+    {'(Intercept)'}          0.52619    0.041587     12.653    21    2.7251e-11
+    {'day'        }        0.0099391    0.012456    0.79797    21       0.43382
+    {'stim'       }          0.11742    0.053689      2.187    21      0.040203
+    {'day:stim'   }        0.0022606     0.01608    0.14058    21       0.88954
+
+
+    Lower        Upper   
+       0.4397     0.61267
+    -0.015964    0.035842
+    0.0057634     0.22907
+     -0.03118    0.035701
+
+Random effects covariance parameters (95% CIs):
+Group: rat (5 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.039966
+
+
+    Lower       Upper   
+    0.016761    0.095298
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.055703    0.040859    0.075939
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(21)=  0.14 F(1)=  0.020  p=0.8895    p=0.8896 (df=20)
+day (learning)             t(21)=  0.80 F(1)=  0.637  p=0.4338    p=0.4343 (df=20)
+stim (main, window start)  t(21)=  2.19 F(1)=  4.783  p=0.0402    p=0.05066 (df=11)
+interaction 95% CI: [-0.03118, +0.0357]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,5.0)=0.01, p=0.9219
+  (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=0.8895, slope diff=+0.002261)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d6_13/analyze.m b/analysis/matlab/variations/unmerge_d6_13/analyze.m
index 82da5ed..0d40402 100644
--- a/analysis/matlab/variations/unmerge_d6_13/analyze.m
+++ b/analysis/matlab/variations/unmerge_d6_13/analyze.m
@@ -1,30 +1,58 @@
-% Variation analysis -- the paper's linear mixed model on the successful-reach
-% COUNT, fit on this folder's curated data subset.
+% Variation analysis -- the paper's linear mixed model on this folder's data,
+% for BOTH metrics:
+%   metric = count : behavior = # successes            -> result.txt
+%   metric = rate  : behavior = success / attempts     -> result_rate.txt
+%     (rate uses only sessions with attempts > 0)
 %
 %   model: behavior ~ stim + day + stim:day + (1|rat)
-%     behavior = successful reaches (count per session)
-%     stim     = 1 for the treatment group(s), 0 for the control group(s)
-%     day      = training day within this window (0 = first analyzed day)
-%     rat      = subject (random intercept)
-%
-% Self-contained: reads data.csv beside this script and writes result.txt.
-% Run headless from this folder with:   matlab -batch "analyze"
-% (This is a copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
+%     stim = 1 treatment / 0 control; day = training day within window (0 =
+%     first analyzed day); rat = subject (random intercept).
+% For the interaction we report residual DF, Satterthwaite DF, and the honest
+% per-animal random-slope test. Self-contained: reads data.csv beside this
+% script. Run headless with:  matlab -batch "analyze"
+% (Copy of analysis/matlab/variation_analyze.m; see make_variations.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');            % folder name = variation id
+vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
 
-tbl = table(D.success, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
+Rc = localAnalyze(D, 'count', here, vname);
+Rr = localAnalyze(D, 'rate', here, vname);
+
+% Machine-readable handoff for SUMMARY.csv (count drives it; rate appended).
+VARRESULT = struct('name', vname, 'nRats', Rc.nRats, 'nObs', Rc.nObs, ...
+    'interP', Rc.interP, 'interEst', Rc.interEst, ...
+    'interPsatt', Rc.interPsatt, 'interPrs', Rc.interPrs, ...
+    'stimP', Rc.stimP, 'dayP', Rc.dayP, 'covEqual', Rc.covEqual, ...
+    'interPrate', Rr.interP, 'interEstRate', Rr.interEst, 'interPrsRate', Rr.interPrs);
+
+% ------------------------------------------------------------------ helper
+function R = localAnalyze(D, metric, here, vname)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :);
+    beh = D.success ./ D.total;
+    mlabel = 'success RATE (success/attempts)'; suffix = '_rate';
+else
+    beh = D.success;
+    mlabel = 'success COUNT'; suffix = '';
+end
+R = struct('interP', NaN, 'interEst', NaN, 'interPsatt', NaN, 'interPrs', NaN, ...
+    'stimP', NaN, 'dayP', NaN, 'nRats', numel(unique(D.subject)), ...
+    'nObs', height(D), 'covEqual', false);
+if numel(unique(D.stim)) < 2 || numel(unique(D.day)) < 2
+    localWrite(sprintf('VARIATION: %s [metric: %s]\nInsufficient data for this metric.\n', ...
+        vname, mlabel), here, suffix);
+    return
+end
+
+tbl = table(beh, D.day - min(D.day), double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 m = fitlme(tbl, 'behavior ~ stim + day + stim:day + (1|rat)');
 C = m.Coefficients; A = anova(m); ci = coefCI(m);
-As = anova(m, 'DFMethod', 'satterthwaite');           % Satterthwaite denominator DF
+As = anova(m, 'DFMethod', 'satterthwaite');
 
-% Honest test: refit with a per-animal random SLOPE so the interaction DF
-% collapses toward the animal count (guarded -- may not converge in short windows).
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 wst = warning('off', 'all');
 try
@@ -43,6 +71,7 @@ row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%7.3f  p=%.4g    p=%.4g (df=%.0f
     C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)), ...
     As.pValue(gs(t)), As.DF2(gs(t)));
 
+ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
 maxT = max(D.day(D.stim == 1)); minT = min(D.day(D.stim == 1));
 maxC = max(D.day(D.stim == 0)); minC = min(D.day(D.stim == 0));
 if abs(maxT - maxC) > 2
@@ -50,20 +79,14 @@ if abs(maxT - maxC) > 2
 else
     cov = '(equal day coverage over this window)';
 end
-
-ii = gi('day:stim'); pI = C.pValue(ii); eI = C.Estimate(ii);
-if pI >= 0.05
-    verdict = 'n.s. -- slopes parallel (no differential learning rate)';
-elseif eI > 0
-    verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
-else
-    verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)';
-end
+if pI >= 0.05; verdict = 'n.s. -- slopes parallel (no differential learning rate)';
+elseif eI > 0; verdict = 'SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates)';
+else; verdict = 'SIGNIFICANT negative -- treatment improves SLOWER (groups converge)'; end
 
 bar = repmat('=', 1, 78);
 raw = regexprep(evalc('disp(m)'), '</?strong>', '');
-s = sprintf('%s\nVARIATION: %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)\n')];
+s = sprintf('%s\nVARIATION: %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('day = training day within window (0 = first analyzed day)\n')];
 s = [s sprintf('treatment (stim=1): %s\n', strjoin(cellstr(unique(D.group(D.stim == 1))), ', '))];
 s = [s sprintf('control   (stim=0): %s\n', strjoin(cellstr(unique(D.group(D.stim == 0))), ', '))];
@@ -74,7 +97,7 @@ s = [s sprintf('%-26s %-18s %-12s %s\n%s\n', 'effect', 't(df) / F(df1)', 'p (res
 s = [s row('stim x day (interaction)', 'day:stim')];
 s = [s row('day (learning)', 'day')];
 s = [s row('stim (main, window start)', 'stim')];
-s = [s sprintf('interaction 95%% CI: [%+.2f, %+.2f]\n', ci(ii, 1), ci(ii, 2))];
+s = [s sprintf('interaction 95%% CI: [%+.4g, %+.4g]\n', ci(ii, 1), ci(ii, 2))];
 if rsOk
     s = [s sprintf('HONEST LME (per-animal random slope, day|rat): interaction F(1,%.1f)=%.2f, p=%.4g\n', rsDf, rsF, rsP)];
 else
@@ -82,17 +105,18 @@ else
 end
 s = [s sprintf(['  (Satterthwaite DF ~= residual on this random-intercept model; the random-slope\n' ...
     '   model above is the honest learning-rate test -- DF collapses toward the animal count.)\n'])];
-s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.2f)\n', verdict, pI, eI)];
-s = [s sprintf('Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
+s = [s sprintf('INTERPRETATION: stim x day interaction %s (p=%.4g, slope diff=%+.4g)\n', verdict, pI, eI)];
+s = [s sprintf('Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.\n')];
 
+localWrite(s, here, suffix);
+
+R = struct('interP', pI, 'interEst', eI, 'interPsatt', As.pValue(gs('day:stim')), ...
+    'interPrs', rsP, 'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
+    'nRats', numel(unique(D.subject)), 'nObs', height(D), 'covEqual', abs(maxT - maxC) <= 2);
+end
+
+function localWrite(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'result.txt'), 'w');
-fprintf(fid, '%s', s);
-fclose(fid);
-
-% Machine-readable handoff for the summary table (see make_variations.m).
-VARRESULT = struct('name', vname, 'nRats', numel(unique(D.subject)), ...
-    'nObs', height(D), 'interP', pI, 'interEst', eI, ...
-    'interPsatt', As.pValue(gs('day:stim')), 'interPrs', rsP, ...
-    'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
-    'covEqual', abs(maxT - maxC) <= 2);
+fid = fopen(fullfile(here, ['result' suffix '.txt']), 'w');
+fprintf(fid, '%s', s); fclose(fid);
+end
diff --git a/analysis/matlab/variations/unmerge_d6_13/learning_curve.png b/analysis/matlab/variations/unmerge_d6_13/learning_curve.png
index d5a8c3b..74f844d 100644
Binary files a/analysis/matlab/variations/unmerge_d6_13/learning_curve.png and b/analysis/matlab/variations/unmerge_d6_13/learning_curve.png differ
diff --git a/analysis/matlab/variations/unmerge_d6_13/learning_curve_rate.png b/analysis/matlab/variations/unmerge_d6_13/learning_curve_rate.png
new file mode 100644
index 0000000..41f75d7
Binary files /dev/null and b/analysis/matlab/variations/unmerge_d6_13/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/unmerge_d6_13/logpower_result.txt b/analysis/matlab/variations/unmerge_d6_13/logpower_result.txt
index 94b5279..4b4c81c 100644
--- a/analysis/matlab/variations/unmerge_d6_13/logpower_result.txt
+++ b/analysis/matlab/variations/unmerge_d6_13/logpower_result.txt
@@ -1,7 +1,7 @@
 ==============================================================================
-LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d6_13
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d6_13   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = # successes (count))
 log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
 observed groups: stim n=3, control n=2   nrep=120, alpha=0.05
 
@@ -10,7 +10,7 @@ stim x log(day) interaction: F(1,30)=3.007  p(resid)=0.09315  p(Satt)=0.0931 (df
   honest per-animal random slope (log-day): F(1,12.8)=2.47  p=0.1406
 Cohen's f (interaction, partial eta^2=0.025) = 0.161  (small-medium; f: .10 small, .25 medium, .40 large)
 
---- power simulation (log-day ground truth: stim:logday=+22.97, ratSD=7.14, resSD=7.81) ---
+--- power simulation (log-day ground truth: stim:logday=+22.97, ratSD=7.136, resSD=7.808) ---
 
  true stim:log(day) = +22.97 (100% of observed)
   N/group  | per-animal power | LME power
@@ -32,5 +32,4 @@ Cohen's f (interaction, partial eta^2=0.025) = 0.161  (small-medium; f: .10 smal
   16       |  0.71            |  0.74
   24       |  0.90            |  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/unmerge_d6_13/logpower_result_rate.txt b/analysis/matlab/variations/unmerge_d6_13/logpower_result_rate.txt
new file mode 100644
index 0000000..030d18b
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d6_13/logpower_result_rate.txt
@@ -0,0 +1,35 @@
+==============================================================================
+LOG-DAY MODEL + COHEN'S f + POWER -- unmerge_d6_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = success RATE)
+log(day) uses 1-indexed training day (our day 0 = paper "Day 1")
+observed groups: stim n=3, control n=2   nrep=120, alpha=0.05
+
+--- fitted on real data ---
+stim x log(day) interaction: F(1,30)=1.838  p(resid)=0.1853  p(Satt)=0.1853 (df=30)
+  honest per-animal random slope (log-day): F(1,3.6)=0.01  p=0.9233
+Cohen's f (interaction, partial eta^2=0.016) = 0.129  (small-medium; f: .10 small, .25 medium, .40 large)
+
+--- power simulation (log-day ground truth: stim:logday=+0.1165, ratSD=0.04832, resSD=0.05061) ---
+
+ true stim:log(day) = +0.1165 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.28            |  0.46  <- observed
+  5        |  0.58            |  0.70
+  8        |  0.78            |  0.83
+  12       |  0.90            |  0.89
+  16       |  0.99            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:log(day) = +0.05823 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.08            |  0.17  <- observed
+  5        |  0.18            |  0.27
+  8        |  0.23            |  0.32
+  12       |  0.34            |  0.38
+  16       |  0.47            |  0.51
+  24       |  0.79            |  0.82
+
+Read per-animal as the honest power; LME matches the paper's power code (optimistic).
diff --git a/analysis/matlab/variations/unmerge_d6_13/logpowersim.m b/analysis/matlab/variations/unmerge_d6_13/logpowersim.m
index e0686ee..5e38720 100644
--- a/analysis/matlab/variations/unmerge_d6_13/logpowersim.m
+++ b/analysis/matlab/variations/unmerge_d6_13/logpowersim.m
@@ -1,50 +1,52 @@
-% Variation log-day analysis + Cohen's f + power simulation.
+% Variation log-day analysis + Cohen's f + power simulation, for BOTH metrics:
+%   metric = count : behavior = # successes         -> logpower_result.txt
+%   metric = rate  : behavior = success / attempts  -> logpower_result_rate.txt
 %
-% The paper's power code models behavior against LOG training day, not raw day:
+% The paper's power code models behavior against LOG training day:
 %   behavior ~ stim + log(day) + stim:log(day) + (1|rat).
-% Their day is 1-indexed (1..10); our data.csv day is 0-indexed (day 0 = paper
-% "Day 1"), so log(day + 1) reproduces their transform exactly.
-%
-% This script (a) refits that log-day model on data.csv, (b) reports the
-% interaction (residual DF, Satterthwaite DF, and the honest per-animal
-% random-slope test) and Cohen's f -- the partial-eta^2 effect size of the
-% interaction, var(fitted_full) - var(fitted_no_interaction) over var(behavior)
-% -- and (c) runs the Monte-Carlo power simulation on the log-day model,
-% scoring per-animal (cluster-honest) and LME power across N.
-% Writes logpower_result.txt. Run:  matlab -batch "logpowersim"
+% Their day is 1-indexed; our data.csv day is 0-indexed, so log(day + 1)
+% reproduces their transform (our day 0 = paper "Day 1"). For each metric this
+% refits that model, reports the interaction (residual / Satterthwaite / honest
+% per-animal random-slope DF) and Cohen's f (partial-eta^2 effect size), then
+% runs the Monte-Carlo power sim (per-animal cluster-honest + LME power).
+% Run:  matlab -batch "logpowersim"
 % (Copy of analysis/matlab/variation_logpower.m; see make_variation_logpower.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' column = log(day+1)
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
 
-warnState = warning('off', 'all');
-rng(1);
+localLogPower(D, 'count', here, vname);
+localLogPower(D, 'rate', here, vname);
 
-logday = log(D.day + 1);                       % 0-indexed day -> their log(1-indexed day)
-tbl0 = table(D.success, logday, double(D.stim), categorical(D.subject), ...
+% ---------------------------------------------------------------- per metric
+function localLogPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
+FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';   % 'day' = log(day+1)
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
+logday = log(D.day + 1);
+tbl0 = table(beh, logday, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (success COUNT)\n')];
+s = sprintf('%s\nLOG-DAY MODEL + COHEN''S f + POWER -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: behavior ~ stim + log(day) + stim:log(day) + (1|rat)   (behavior = %s)\n', mlabel)];
 s = [s sprintf('log(day) uses 1-indexed training day (our day 0 = paper "Day 1")\n')];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for this analysis (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for this analysis.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
-% ---- fitted model on the real data ----
 full = fitlme(tbl0, FORMULA);
 An = anova(full); Asatt = anova(full, 'DFMethod', 'satterthwaite');
 ii = strcmp(An.Term, 'day:stim'); is = strcmp(Asatt.Term, 'day:stim');
@@ -52,7 +54,6 @@ reduced = fitlme(tbl0, 'behavior ~ stim + day + (1|rat)');
 eta2part = max((var(fitted(full)) - var(fitted(reduced))) / var(tbl0.behavior), 0);
 cohenf = sqrt(eta2part / (1 - eta2part));
 
-% honest per-animal random-slope interaction
 rsP = NaN; rsDf = NaN; rsF = NaN; rsOk = false;
 try
     mr = fitlme(tbl0, 'behavior ~ stim + day + stim:day + (day|rat)');
@@ -76,19 +77,18 @@ end
 s = [s sprintf('Cohen''s f (interaction, partial eta^2=%.3f) = %.3f  (%s; f: .10 small, .25 medium, .40 large)\n', ...
     eta2part, cohenf, mag)];
 
-% ---- power simulation on the log-day ground truth ----
 cn = full.CoefficientNames; be = full.fixedEffects;
 b0 = be(strcmp(cn, '(Intercept)')); bStim = be(strcmp(cn, 'stim'));
 bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(full); sRat = sqrt(psi{1}); sRes = sqrt(full.MSE);
-days = unique(tbl0.day);                        % the log(day) grid
+days = unique(tbl0.day);
 
-s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.2f, ratSD=%.2f, resSD=%.2f) ---\n', ...
+s = [s sprintf('\n--- power simulation (log-day ground truth: stim:logday=%+.4g, ratSD=%.4g, resSD=%.4g) ---\n', ...
     bInt, sRat, sRes)];
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:log(day) = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:log(day) = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -102,19 +102,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-s = [s sprintf(['\nRead the per-animal column as the honest power; the LME column matches the\n' ...
-    'paper''s power code (anova interaction p, observation-level DF) and is optimistic.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead per-animal as the honest power; LME matches the paper''s power code (optimistic).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'logpower_result.txt'), 'w');
+fid = fopen(fullfile(here, ['logpower_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/unmerge_d6_13/plotcurve.m b/analysis/matlab/variations/unmerge_d6_13/plotcurve.m
index bfc1386..193a44b 100644
--- a/analysis/matlab/variations/unmerge_d6_13/plotcurve.m
+++ b/analysis/matlab/variations/unmerge_d6_13/plotcurve.m
@@ -1,11 +1,12 @@
-% Variation learning-curve plot, in the style of the paper:
+% Variation learning-curve plots, in the style of the paper:
 %   "Lines indicate mean (and SEM) across animals in the anodal (red) and
 %    control (blue) groups."
-% Plots mean +/- SEM successful reaches per training day for the treatment /
-% anodal group (stim = 1, red) and the control group (stim = 0, blue), reading
-% this folder's data.csv and saving learning_curve.png. The per-group N is read
-% from the data (each variation pools different groups), so the legend shows the
-% actual counts. Training day is 1-indexed (our day 0 = the paper's "Day 1").
+% Produces TWO figures from this folder's data.csv:
+%   learning_curve.png       # successes (count)   per training day
+%   learning_curve_rate.png  success rate (success/attempts) per training day
+% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
+% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
+% the x-axis tick labels are drawn vertically.
 % Run:  matlab -batch "plotcurve"
 % (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
 
@@ -14,15 +15,20 @@ if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
 
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);              % 0-indexed
-xd = days + 1;                     % plot as 1-indexed training day (paper axis)
-
+days = unique(D.day);
+xd = days + 1;                    % plot as 1-indexed training day (paper axis)
 red = [0.85 0.10 0.10];
 blue = [0.10 0.30 0.85];
 
-[Ma, Sa, na] = localCurve(D, 1, days);   % anodal / treatment (stim = 1)
-[Mc, Sc, nc] = localCurve(D, 0, days);   % control (stim = 0)
+localPlot(D, days, xd, 'count', '# successes', ...
+    fullfile(here, 'learning_curve.png'), vname, red, blue);
+localPlot(D, days, xd, 'rate', 'success rate', ...
+    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
 
+% ------------------------------------------------------------------ helpers
+function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
+[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
+[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
 fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
 hold on
 e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
@@ -30,26 +36,30 @@ e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWid
 hold off
 legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
     'Location', 'northwest', 'Box', 'off');
-xlabel('training day');
-ylabel('# successes');
+xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
 set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-
-outFile = fullfile(here, 'learning_curve.png');
+xtickangle(90);                  % vertical x-axis tick labels
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);
-fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
 
-% ------------------------------------------------------------------ helper
-function [M, S, n] = localCurve(D, stimVal, days)
-%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+function [M, S, n] = localCurve(D, stimVal, days, metric)
+%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
 subs = unique(D.subject(D.stim == stimVal));
 n = numel(subs);
 X = nan(numel(days), n);
 for j = 1:n
     for i = 1:numel(days)
         r = D.subject == subs(j) & D.day == days(i);
-        if any(r); X(i, j) = mean(D.success(r)); end
+        if ~any(r); continue; end
+        if strcmp(metric, 'rate')
+            tot = sum(D.total(r));
+            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
+        else
+            X(i, j) = mean(D.success(r));
+        end
     end
 end
 M = mean(X, 2, 'omitnan');
diff --git a/analysis/matlab/variations/unmerge_d6_13/power_result.txt b/analysis/matlab/variations/unmerge_d6_13/power_result.txt
index ed3c599..24baa60 100644
--- a/analysis/matlab/variations/unmerge_d6_13/power_result.txt
+++ b/analysis/matlab/variations/unmerge_d6_13/power_result.txt
@@ -1,11 +1,11 @@
 ==============================================================================
-POWER SIMULATION -- unmerge_d6_13
+POWER SIMULATION -- unmerge_d6_13   [metric: # successes (count)]
 ==============================================================================
-model: behavior ~ stim + day + stim:day + (1|rat)   (success COUNT; day within-window)
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = # successes (count); day within-window)
 observed groups: stim n=3, control n=2   nrep=120, alpha=0.05
-ground truth: stim:day=+2.27/day, rat SD=7.12, residual SD=7.73, days=8
+ground truth: stim:day=+2.267/day, rat SD=7.117, residual SD=7.733, days=8
 
- true stim:day interaction = +2.27 (100% of observed)
+ true stim:day interaction = +2.267 (100% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.46            |  0.62  <- observed
@@ -15,7 +15,7 @@ ground truth: stim:day=+2.27/day, rat SD=7.12, residual SD=7.73, days=8
   16       |  1.00            |  1.00
   24       |  1.00            |  1.00
 
- true stim:day interaction = +1.13 (50% of observed)
+ true stim:day interaction = +1.133 (50% of observed)
   N/group  | per-animal power | LME power
   ------------------------------------------
   3        |  0.12            |  0.20  <- observed
@@ -25,5 +25,4 @@ ground truth: stim:day=+2.27/day, rat SD=7.12, residual SD=7.73, days=8
   16       |  0.72            |  0.74
   24       |  0.91            |  0.93
 
-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/unmerge_d6_13/power_result_rate.txt b/analysis/matlab/variations/unmerge_d6_13/power_result_rate.txt
new file mode 100644
index 0000000..77d9da5
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d6_13/power_result_rate.txt
@@ -0,0 +1,28 @@
+==============================================================================
+POWER SIMULATION -- unmerge_d6_13   [metric: success RATE]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE; day within-window)
+observed groups: stim n=3, control n=2   nrep=120, alpha=0.05
+ground truth: stim:day=+0.01216/day, rat SD=0.04815, residual SD=0.05028, days=8
+
+ true stim:day interaction = +0.01216 (100% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.32            |  0.47  <- observed
+  5        |  0.63            |  0.76
+  8        |  0.84            |  0.88
+  12       |  0.91            |  0.96
+  16       |  1.00            |  1.00
+  24       |  1.00            |  1.00
+
+ true stim:day interaction = +0.006078 (50% of observed)
+  N/group  | per-animal power | LME power
+  ------------------------------------------
+  3        |  0.07            |  0.17  <- observed
+  5        |  0.23            |  0.28
+  8        |  0.27            |  0.35
+  12       |  0.39            |  0.40
+  16       |  0.48            |  0.58
+  24       |  0.83            |  0.84
+
+Read the per-animal column as the honest power; LME is optimistic (obs-level DF).
diff --git a/analysis/matlab/variations/unmerge_d6_13/powersim.m b/analysis/matlab/variations/unmerge_d6_13/powersim.m
index 554d032..ecd9c6c 100644
--- a/analysis/matlab/variations/unmerge_d6_13/powersim.m
+++ b/analysis/matlab/variations/unmerge_d6_13/powersim.m
@@ -1,45 +1,49 @@
 % Variation power simulation -- Monte-Carlo power for the paper's stim x day
-% interaction, using THIS folder's data as the ground truth.
+% interaction, using THIS folder's data as the ground truth, for BOTH metrics:
+%   metric = count : behavior = # successes         -> power_result.txt
+%   metric = rate  : behavior = success / attempts  -> power_result_rate.txt
 %
 % Ground truth: fitlme(behavior ~ stim + day + stim:day + (1|rat)) on data.csv
-% (success COUNT; day within-window). Its fixed effects, per-rat intercept SD,
-% and residual SD generate NREP synthetic datasets at each rats-per-group N and
-% each true-effect multiplier (1 = observed slope, 0.5 = half). Each dataset is
-% scored at alpha = 0.05 two ways:
-%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest -- the
-%                honest power, matching the random-slope / per-animal inference)
+% (day within-window). Its fixed effects, per-rat intercept SD, and residual SD
+% generate NREP synthetic datasets at each rats-per-group N and each true-effect
+% multiplier (1 = observed, 0.5 = half). Each is scored at alpha=0.05 by:
+%   per-animal : Welch t on per-rat behavior~day slopes (cluster-honest power)
 %   LME        : the fitlme stim:day p (observation-level DF -- optimistic)
-% Writes power_result.txt beside this script. Run:  matlab -batch "powersim"
+% Writes power_result[_rate].txt. Run:  matlab -batch "powersim"
 % (Copy of analysis/matlab/variation_power.m; see make_variation_power.m.)
 
 here = fileparts(mfilename('fullpath'));
 if isempty(here); here = pwd; end
 vname = regexprep(here, '.*[/\\]', '');
-
 D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+
+localPower(D, 'count', here, vname);
+localPower(D, 'rate', here, vname);
+
+% ---------------------------------------------------------------- per metric
+function localPower(D, metric, here, vname)
+NS = [3 5 8 12 16 24]; EFFMULS = [1 0.5]; NREP = 120;
 FORMULA = 'behavior ~ stim + day + stim:day + (1|rat)';
-NS = [3 5 8 12 16 24];
-EFFMULS = [1 0.5];
-NREP = 120;
-
-warnState = warning('off', 'all');
-rng(1);
-
+if strcmp(metric, 'rate')
+    D = D(D.total > 0, :); beh = D.success ./ D.total; mlabel = 'success RATE'; suffix = '_rate';
+else
+    beh = D.success; mlabel = '# successes (count)'; suffix = '';
+end
+warnState = warning('off', 'all'); rng(1);
 day0 = min(D.day);
-tbl0 = table(D.success, D.day - day0, double(D.stim), categorical(D.subject), ...
+tbl0 = table(beh, D.day - day0, double(D.stim), categorical(D.subject), ...
     'VariableNames', {'behavior', 'day', 'stim', 'rat'});
-
 nStim = numel(unique(D.subject(D.stim == 1)));
 nCtrl = numel(unique(D.subject(D.stim == 0)));
 
 bar = repmat('=', 1, 78);
-s = sprintf('%s\nPOWER SIMULATION -- %s\n%s\n', bar, vname, bar);
-s = [s sprintf('model: %s   (success COUNT; day within-window)\n', FORMULA)];
+s = sprintf('%s\nPOWER SIMULATION -- %s   [metric: %s]\n%s\n', bar, vname, mlabel, bar);
+s = [s sprintf('model: %s   (behavior = %s; day within-window)\n', FORMULA, mlabel)];
 s = [s sprintf('observed groups: stim n=%d, control n=%d   nrep=%d, alpha=0.05\n', nStim, nCtrl, NREP)];
 
 if nStim < 2 || nCtrl < 2 || numel(unique(tbl0.day)) < 2
-    s = [s sprintf('\nInsufficient data for a power simulation (need >=2 animals/group and >=2 days).\n')];
-    localFinish(s, here); warning(warnState); return
+    s = [s sprintf('\nInsufficient data for a power simulation.\n')];
+    localFinish(s, here, suffix); warning(warnState); return
 end
 
 lme = fitlme(tbl0, FORMULA);
@@ -49,14 +53,13 @@ bDay = be(strcmp(cn, 'day')); bInt = be(strcmp(cn, 'day:stim'));
 psi = covarianceParameters(lme); sRat = sqrt(psi{1}); sRes = sqrt(lme.MSE);
 days = (0:max(tbl0.day))';
 
-s = [s sprintf('ground truth: stim:day=%+.2f/day, rat SD=%.2f, residual SD=%.2f, days=%d\n', ...
+s = [s sprintf('ground truth: stim:day=%+.4g/day, rat SD=%.4g, residual SD=%.4g, days=%d\n', ...
     bInt, sRat, sRes, numel(days))];
 
 for eMul = EFFMULS
     bI = bInt * eMul;
-    s = [s sprintf('\n true stim:day interaction = %+.2f (%.0f%% of observed)\n', bI, eMul * 100)]; %#ok<AGROW>
-    s = [s sprintf('  %-8s | per-animal power | LME power\n', 'N/group')]; %#ok<AGROW>
-    s = [s sprintf('  %s\n', repmat('-', 1, 42))]; %#ok<AGROW>
+    s = [s sprintf('\n true stim:day interaction = %+.4g (%.0f%% of observed)\n', bI, eMul * 100)];
+    s = [s sprintf('  %-8s | per-animal power | LME power\n  %s\n', 'N/group', repmat('-', 1, 42))];
     for N = NS
         sigPA = 0; sigL = 0;
         for r = 1:NREP
@@ -70,20 +73,18 @@ for eMul = EFFMULS
         end
         star = '';
         if N == nStim || N == nCtrl; star = '  <- observed'; end
-        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)]; %#ok<AGROW>
+        s = [s sprintf('  %-8d | %5.2f            | %5.2f%s\n', N, sigPA / NREP, sigL / NREP, star)];
     end
 end
-
-s = [s sprintf(['\nRead the per-animal column as the honest power. At the observed N this study\n' ...
-    'is typically underpowered; per-animal power reaches ~0.8 only at larger N.\n'])];
-
-localFinish(s, here);
+s = [s sprintf('\nRead the per-animal column as the honest power; LME is optimistic (obs-level DF).\n')];
+localFinish(s, here, suffix);
 warning(warnState);
+end
 
 % ---------------------------------------------------------------- helpers
-function localFinish(s, here)
+function localFinish(s, here, suffix)
 fprintf('%s', s);
-fid = fopen(fullfile(here, 'power_result.txt'), 'w');
+fid = fopen(fullfile(here, ['power_result' suffix '.txt']), 'w');
 fprintf(fid, '%s', s); fclose(fid);
 end
 
diff --git a/analysis/matlab/variations/unmerge_d6_13/result.txt b/analysis/matlab/variations/unmerge_d6_13/result.txt
index 9948e0c..f0c54dc 100644
--- a/analysis/matlab/variations/unmerge_d6_13/result.txt
+++ b/analysis/matlab/variations/unmerge_d6_13/result.txt
@@ -1,5 +1,5 @@
 ==============================================================================
-VARIATION: unmerge_d6_13
+VARIATION: unmerge_d6_13   [metric: success COUNT]
 ==============================================================================
 model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success COUNT)
 day = training day within window (0 = first analyzed day)
@@ -60,9 +60,9 @@ effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
 stim x day (interaction)   t(30)=  1.71 F(1)=  2.933  p=0.0971    p=0.09701 (df=30)
 day (learning)             t(30)=  1.61 F(1)=  2.598  p=0.1175    p=0.1174 (df=30)
 stim (main, window start)  t(30)=  1.69 F(1)=  2.856  p=0.1014    p=0.1269 (df=9)
-interaction 95% CI: [-0.44, +4.97]
+interaction 95% CI: [-0.4363, +4.97]
 HONEST LME (per-animal random slope, day|rat): interaction F(1,13.9)=2.48, p=0.1382
   (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=0.0971, slope diff=+2.27)
-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=0.0971, slope diff=+2.267)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.
diff --git a/analysis/matlab/variations/unmerge_d6_13/result_rate.txt b/analysis/matlab/variations/unmerge_d6_13/result_rate.txt
new file mode 100644
index 0000000..68bfd2e
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d6_13/result_rate.txt
@@ -0,0 +1,68 @@
+==============================================================================
+VARIATION: unmerge_d6_13   [metric: success RATE (success/attempts)]
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   (behavior = success RATE (success/attempts))
+day = training day within window (0 = first analyzed day)
+treatment (stim=1): Electrode-Box-B2
+control   (stim=0): Electrode-Box-A2
+N = 5 rats, 34 sessions   raw day coverage: treat 6..13, control 6..13
+(equal day coverage over this window)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              34
+    Fixed effects coefficients           4
+    Random effects coefficients          5
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC        BIC        LogLikelihood    Deviance
+    -85.035    -75.877    48.518           -97.035 
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate     SE           tStat      DF    pValue   
+    {'(Intercept)'}          0.53562       0.0416     12.875    30    9.415e-14
+    {'day'        }        0.0032833    0.0069042    0.47554    30      0.63784
+    {'stim'       }          0.10269     0.053584     1.9164    30      0.06488
+    {'day:stim'   }         0.012156    0.0086128     1.4114    30      0.16841
+
+
+    Lower         Upper   
+       0.45066     0.62058
+     -0.010817    0.017384
+    -0.0067429     0.21212
+    -0.0054334    0.029746
+
+Random effects covariance parameters (95% CIs):
+Group: rat (5 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.048154
+
+
+    Lower       Upper   
+    0.023654    0.098032
+
+Group: Error
+    Name               Estimate    Lower       Upper   
+    {'Res Std'}        0.050284    0.038906    0.064989
+
+
+effect                     t(df) / F(df1)     p (resid)    Satterthwaite: p (df)
+----------------------------------------------------------------------------
+stim x day (interaction)   t(30)=  1.41 F(1)=  1.992  p=0.1684    p=0.1684 (df=30)
+day (learning)             t(30)=  0.48 F(1)=  0.226  p=0.6378    p=0.6378 (df=30)
+stim (main, window start)  t(30)=  1.92 F(1)=  3.673  p=0.06488    p=0.09052 (df=8)
+interaction 95% CI: [-0.005433, +0.02975]
+HONEST LME (per-animal random slope, day|rat): interaction F(1,2.3)=0.03, p=0.8749
+  (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=0.1684, slope diff=+0.01216)
+Paper (N=24, count): interaction t(227)=2.68, F(1)=7.12, p=0.008.