analysis(matlab): add paper-style learning-curve plot to every variation

Add variation_plot.m + make_variation_plot.m, dropping plotcurve.m +
learning_curve.png into all 28 variation folders. Each figure plots mean +/-
SEM successful reaches per training day for the anodal/treatment group (red)
vs control (blue), in the style of the paper ("Lines indicate mean (and SEM)
across animals in the anodal (red) and control (blue) groups"). Per-group N is
read from the data and shown in the legend; training day is 1-indexed (our day
0 = paper Day 1). Headless via exportgraphics. prev_f_full reproduces the
paper's own figure (anodal N=12 vs control N=12).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

analysis/matlab/make_variation_plot.m

@@ -0,0 +1,28 @@
+function make_variation_plot()
+%MAKE_VARIATION_PLOT Add a paper-style learning-curve figure to each variation.
+%   MAKE_VARIATION_PLOT() copies variation_plot.m into every variations/<name>/
+%   folder with a data.csv (as plotcurve.m) and runs it, producing
+%   learning_curve.png (mean +/- SEM successes per training day, anodal red vs
+%   control blue). Re-runnable; picks up new folders.
+
+thisDir = fileparts(mfilename('fullpath'));
+template = fullfile(thisDir, 'variation_plot.m');
+root = fullfile(thisDir, 'variations');
+
+d = dir(root);
+n = 0;
+for i = 1:numel(d)
+    if ~d(i).isdir || ismember(d(i).name, {'.', '..'}); continue; end
+    folder = fullfile(root, d(i).name);
+    if ~exist(fullfile(folder, 'data.csv'), 'file'); continue; end
+    copyfile(template, fullfile(folder, 'plotcurve.m'));
+    localRun(fullfile(folder, 'plotcurve.m'));
+    n = n + 1;
+end
+fprintf('\nAdded plotcurve.m + learning_curve.png to %d variation folders.\n', n);
+
+end
+
+function localRun(scriptPath)
+run(scriptPath);
+end

analysis/matlab/variation_plot.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/boxa_a2_d0_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/boxa_a2_d0_5/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/boxa_a2_d6_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/boxa_b2_d0_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/boxa_b2_d0_5/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/boxa_b2_d6_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/matched_current_d0_3/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_a2_d0_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_a2_d0_13/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_a2_d0_5/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_a2_d6_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_a2_d6_13/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_boxa_d0_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_boxa_d0_13/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_boxa_d0_5/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_boxa_d6_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/naive_boxa_d6_13/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/prev_f_full/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/right_only_d0_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/right_only_d0_13/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/right_only_d0_5/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/right_only_d6_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/right_only_d6_13/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/unmerge_d0_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/unmerge_d0_13/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/unmerge_d0_5/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/unmerge_d6_10/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end

analysis/matlab/variations/unmerge_d6_13/plotcurve.m

@@ -0,0 +1,57 @@
+% Variation learning-curve plot, 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").
+% 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);              % 0-indexed
+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)
+
+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('# successes');
+title(vname, 'Interpreter', 'none');
+set(gca, 'XTick', xd, 'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+
+outFile = fullfile(here, 'learning_curve.png');
+exportgraphics(fig, outFile, 'Resolution', 150);
+close(fig);
+fprintf('%s: wrote learning_curve.png (anodal N=%d, control N=%d)\n', vname, na, nc);
+
+% ------------------------------------------------------------------ helper
+function [M, S, n] = localCurve(D, stimVal, days)
+%LOCALCURVE Per-day mean and SEM of successes across the animals in a group.
+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
+    end
+end
+M = mean(X, 2, 'omitnan');
+S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
+end