diff --git a/analysis/matlab/variations/unmerge_d0_5/learning_curve.png b/analysis/matlab/variations/unmerge_d0_5/learning_curve.png
index fdb347c..85849e3 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
index f86b755..0a31f40 100644
Binary files a/analysis/matlab/variations/unmerge_d0_5/learning_curve_rate.png and b/analysis/matlab/variations/unmerge_d0_5/learning_curve_rate.png differ
diff --git a/analysis/matlab/variations/unmerge_d0_5/plotcurve.asv b/analysis/matlab/variations/unmerge_d0_5/plotcurve.asv
new file mode 100644
index 0000000..1bb7867
--- /dev/null
+++ b/analysis/matlab/variations/unmerge_d0_5/plotcurve.asv
@@ -0,0 +1,79 @@
+% 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."
+% 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.)
+
+here = fileparts(mfilename('fullpath'));
+if isempty(here); here = pwd; end
+vname = regexprep(here, '.*[/\\]', '');
+
+D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
+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];
+
+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);
+e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWidth', 2);
+hold off
+legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
+    'Location', 'northwest', 'Box', 'off');
+xlabel('training day'); ylabel(ylab);
+title(vname, 'Interpreter', 'none');
+
+set(gca, 'XTick', xd, 'XTickLabel', {}, 'Position', [0.13 0.20 0.80 0.70], ...
+    'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off', 'TickDir', 'out');
+drawnow;
+yl = ylim(gca);
+yb = yl(1) - 0.015 * (yl(2) - yl(1));
+for k = 1:numel(xd)
+    text(xd(k), yb, num2str(xd(k)), 'Parent', gca, ...
+        'HorizontalAlignment', 'right', 'VerticalAlignment', 'middle', ...
+        'FontName', 'Arial', 'FontSize', 13, 'Clipping', 'off');
+end
+xlh = get(gca, 'XLabel');            % push the axis label below the vertical ticks
+set(xlh, 'Units', 'normalized', 'Position', [0.5 -0.15 0]);
+drawnow;
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
+end
+
+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); 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');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end
diff --git a/analysis/matlab/variations/unmerge_d0_5/plotcurve.m b/analysis/matlab/variations/unmerge_d0_5/plotcurve.m
index 8399de2..9ae48f2 100644
--- a/analysis/matlab/variations/unmerge_d0_5/plotcurve.m
+++ b/analysis/matlab/variations/unmerge_d0_5/plotcurve.m
@@ -38,21 +38,10 @@ legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)},
     'Location', 'northwest', 'Box', 'off');
 xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
-% Vertical x-axis tick labels. XTickLabelRotation / xtickangle are NOT honored
-% by exportgraphics on a headless invisible figure, so draw the labels manually
-% as rotated text objects (which always render).
-set(gca, 'XTick', xd, 'XTickLabel', {}, 'Position', [0.13 0.20 0.80 0.70], ...
-    'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
-drawnow;
-yl = ylim(gca);
-yb = yl(1) - 0.015 * (yl(2) - yl(1));
-for k = 1:numel(xd)
-    text(xd(k), yb, num2str(xd(k)), 'Parent', gca, 'Rotation', 90, ...
-        'HorizontalAlignment', 'right', 'VerticalAlignment', 'middle', ...
-        'FontName', 'Arial', 'FontSize', 13, 'Clipping', 'off');
-end
-xlh = get(gca, 'XLabel');            % push the axis label below the vertical ticks
-set(xlh, 'Units', 'normalized', 'Position', [0.5 -0.15 0]);
+
+set(gca, 'XTick', xd, 'XTickLabel', {}, 'Position', [0.2 0.30 0.60 0.60], ...
+    'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off', 'TickDir', 'out');
+xticklabels('auto')
 drawnow;
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);