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analysis(matlab): per-variation subfolders (grouping x window) for the paper LME

Browse Source 
Add make_variations.m + variation_analyze.m generating 20 self-contained
subfolders under analysis/matlab/variations/, one per grouping x day-window,
each with exactly three files: data.csv (curated subset), analyze.m (a simple
standalone script fitting behavior ~ stim + day + stim:day + (1|rat) on the
success COUNT), and result.txt (its output). Plus variations/SUMMARY.csv.

Groupings (stim=1 / stim=0, other groups dropped):
  unmerge     B2 vs A2
  right_only  B2+Right vs A2            (Box-A dropped)
  naive_a2    B2 vs A2+Naive           (Right, Box-A dropped)
  naive_boxa  B2 vs A2+Naive+Box-A     (Right dropped)
Windows: 0-10, 0-13, 0-5, 6-10, 6-13. All 20 have equal day coverage.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
2026-07-20 20:49:54 -04:00
parent 963fd889b2
commit 969c0205e8
63 changed files with 4343 additions and 0 deletions
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analysis/matlab/variations/SUMMARY.csv
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variation,grouping,window,nRats,nObs,covEqual,interaction_p,interaction_est,stim_p,day_p
unmerge_d0_10,unmerge,d0_10,6,61,1,0.142831730250263,1.55766564374789,0.497389134925754,2.83166776054496e-14
unmerge_d0_13,unmerge,d0_13,6,70,1,0.1037612192755,1.54665472676189,0.343602740986316,3.0346358823857e-13
unmerge_d0_5,unmerge,d0_5,6,36,1,0.015149263054839,5.36190476190475,0.688839435920596,1.57055423419133e-07
unmerge_d6_10,unmerge,d6_10,5,25,1,0.485980598616448,1.76666666666666,0.0975215172907211,0.227259314093653
unmerge_d6_13,unmerge,d6_13,5,34,1,0.0971009786566259,2.26696773140328,0.101395665857404,0.117493504965036
right_only_d0_10,right_only,d0_10,7,72,1,0.072180139606096,1.73004431141938,0.715879551990348,5.94289273347193e-16
right_only_d0_13,right_only,d0_13,7,84,1,0.0480416099228058,1.7001712792199,0.528517264593511,1.17078898575779e-14
right_only_d0_5,right_only,d0_5,7,42,1,0.00589730538358453,5.30714285714286,0.469556667463413,1.45827052706827e-08
right_only_d6_10,right_only,d6_10,6,30,1,0.288979620167748,2.425,0.119238243812818,0.200957769624237
right_only_d6_13,right_only,d6_13,6,42,1,0.0258497485171371,2.75792254325755,0.109557181635453,0.0976080018855909
naive_a2_d0_10,naive_a2,d0_10,10,104,1,0.125891264174292,1.43371876298297,0.141836960529428,1.59796354184479e-27
naive_a2_d0_13,naive_a2,d0_13,10,124,1,0.0397967517409217,1.61366204723722,0.153633906173184,2.5469330031931e-28
naive_a2_d0_5,naive_a2,d0_5,10,59,1,0.00107331575782455,6.96223661591522,0.960550975833492,1.1254222201539e-09
naive_a2_d6_10,naive_a2,d6_10,9,45,1,0.449922139297732,-1.8,0.00929038977362001,8.02730473236241e-05
naive_a2_d6_13,naive_a2,d6_13,9,65,1,0.439759713746994,1.15527349799111,0.0165675905421826,0.00123453301903328
naive_boxa_d0_10,naive_boxa,d0_10,11,115,1,0.194116831474294,1.17792772470162,0.124842156542362,1.7431506758907e-32
naive_boxa_d0_13,naive_boxa,d0_13,11,138,1,0.0848056366333164,1.30637615965008,0.140502409490992,1.21292542612342e-34
naive_boxa_d0_5,naive_boxa,d0_5,11,65,1,0.00254470765200637,6.40329932104724,0.881419104187055,2.50618640106882e-11
naive_boxa_d6_10,naive_boxa,d6_10,10,50,1,0.46817073850354,-1.61904761904762,0.014689379915428,1.87688778586107e-05
naive_boxa_d6_13,naive_boxa,d6_13,10,73,1,0.530807487326757,0.873689202983884,0.0303916991304334,6.44631091768978e-05
1 variation grouping window nRats nObs covEqual interaction_p interaction_est stim_p day_p
2 unmerge_d0_10 unmerge d0_10 6 61 1 0.142831730250263 1.55766564374789 0.497389134925754 2.83166776054496e-14
3 unmerge_d0_13 unmerge d0_13 6 70 1 0.1037612192755 1.54665472676189 0.343602740986316 3.0346358823857e-13
4 unmerge_d0_5 unmerge d0_5 6 36 1 0.015149263054839 5.36190476190475 0.688839435920596 1.57055423419133e-07
5 unmerge_d6_10 unmerge d6_10 5 25 1 0.485980598616448 1.76666666666666 0.0975215172907211 0.227259314093653
6 unmerge_d6_13 unmerge d6_13 5 34 1 0.0971009786566259 2.26696773140328 0.101395665857404 0.117493504965036
7 right_only_d0_10 right_only d0_10 7 72 1 0.072180139606096 1.73004431141938 0.715879551990348 5.94289273347193e-16
8 right_only_d0_13 right_only d0_13 7 84 1 0.0480416099228058 1.7001712792199 0.528517264593511 1.17078898575779e-14
9 right_only_d0_5 right_only d0_5 7 42 1 0.00589730538358453 5.30714285714286 0.469556667463413 1.45827052706827e-08
10 right_only_d6_10 right_only d6_10 6 30 1 0.288979620167748 2.425 0.119238243812818 0.200957769624237
11 right_only_d6_13 right_only d6_13 6 42 1 0.0258497485171371 2.75792254325755 0.109557181635453 0.0976080018855909
12 naive_a2_d0_10 naive_a2 d0_10 10 104 1 0.125891264174292 1.43371876298297 0.141836960529428 1.59796354184479e-27
13 naive_a2_d0_13 naive_a2 d0_13 10 124 1 0.0397967517409217 1.61366204723722 0.153633906173184 2.5469330031931e-28
14 naive_a2_d0_5 naive_a2 d0_5 10 59 1 0.00107331575782455 6.96223661591522 0.960550975833492 1.1254222201539e-09
15 naive_a2_d6_10 naive_a2 d6_10 9 45 1 0.449922139297732 -1.8 0.00929038977362001 8.02730473236241e-05
16 naive_a2_d6_13 naive_a2 d6_13 9 65 1 0.439759713746994 1.15527349799111 0.0165675905421826 0.00123453301903328
17 naive_boxa_d0_10 naive_boxa d0_10 11 115 1 0.194116831474294 1.17792772470162 0.124842156542362 1.7431506758907e-32
18 naive_boxa_d0_13 naive_boxa d0_13 11 138 1 0.0848056366333164 1.30637615965008 0.140502409490992 1.21292542612342e-34
19 naive_boxa_d0_5 naive_boxa d0_5 11 65 1 0.00254470765200637 6.40329932104724 0.881419104187055 2.50618640106882e-11
20 naive_boxa_d6_10 naive_boxa d6_10 10 50 1 0.46817073850354 -1.61904761904762 0.014689379915428 1.87688778586107e-05
21 naive_boxa_d6_13 naive_boxa d6_13 10 73 1 0.530807487326757 0.873689202983884 0.0303916991304334 6.44631091768978e-05
analysis/matlab/variations/naive_a2_d0_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_a2_d0_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-lang-2,Naive,0,0,0,0
Khoai-lang-2,Naive,1,0,0,0
Khoai-lang-2,Naive,2,10,47,0
Khoai-lang-2,Naive,3,11,52,0
Khoai-lang-2,Naive,4,9,56,0
Khoai-lang-2,Naive,5,34,95,0
Khoai-lang-2,Naive,6,21,72,0
Khoai-lang-2,Naive,7,23,99,0
Khoai-lang-2,Naive,8,64,136,0
Khoai-lang-2,Naive,9,75,131,0
Khoai-lang-2,Naive,10,63,134,0
Khoai-tay-2,Naive,1,21,68,0
Khoai-tay-2,Naive,2,6,79,0
Khoai-tay-2,Naive,3,0,83,0
Khoai-tay-2,Naive,4,28,87,0
Khoai-tay-2,Naive,5,31,125,0
Khoai-tay-2,Naive,6,62,144,0
Khoai-tay-2,Naive,7,87,141,0
Khoai-tay-2,Naive,8,105,152,0
Khoai-tay-2,Naive,9,75,148,0
Khoai-tay-2,Naive,10,101,149,0
OM-2,Naive,0,1,7,0
OM-2,Naive,1,11,33,0
OM-2,Naive,2,19,62,0
OM-2,Naive,3,29,117,0
OM-2,Naive,4,73,126,0
OM-2,Naive,5,53,135,0
OM-2,Naive,6,73,138,0
OM-2,Naive,7,80,131,0
OM-2,Naive,8,91,141,0
OM-2,Naive,9,90,135,0
OM-2,Naive,10,95,142,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
Vu-vuong,Naive,0,18,61,0
Vu-vuong,Naive,1,35,91,0
Vu-vuong,Naive,2,61,127,0
Vu-vuong,Naive,3,34,146,0
Vu-vuong,Naive,4,61,141,0
Vu-vuong,Naive,5,37,151,0
Vu-vuong,Naive,6,49,131,0
Vu-vuong,Naive,7,65,149,0
Vu-vuong,Naive,8,67,141,0
Vu-vuong,Naive,9,73,140,0
Vu-vuong,Naive,10,72,131,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-2 Electrode-Box-A2 0 25 97 0
14 Banh-mi-2 Electrode-Box-A2 1 22 101 0
15 Banh-mi-2 Electrode-Box-A2 2 22 119 0
16 Banh-mi-2 Electrode-Box-A2 3 29 118 0
17 Banh-mi-2 Electrode-Box-A2 4 27 136 0
18 Banh-mi-2 Electrode-Box-A2 5 43 146 0
19 Banh-mi-2 Electrode-Box-A2 6 74 146 0
20 Banh-mi-2 Electrode-Box-A2 7 70 148 0
21 Banh-mi-2 Electrode-Box-A2 8 65 130 0
22 Banh-mi-2 Electrode-Box-A2 9 79 151 0
23 Banh-mi-2 Electrode-Box-A2 10 93 152 0
24 Egg-tart-1 Electrode-Box-B2 0 7 56 1
25 Egg-tart-1 Electrode-Box-B2 1 16 78 1
26 Egg-tart-1 Electrode-Box-B2 2 23 103 1
27 Egg-tart-1 Electrode-Box-B2 3 63 120 1
28 Egg-tart-1 Electrode-Box-B2 4 69 132 1
29 Egg-tart-1 Electrode-Box-B2 5 83 136 1
30 Egg-tart-1 Electrode-Box-B2 6 71 142 1
31 Egg-tart-1 Electrode-Box-B2 7 79 138 1
32 Egg-tart-1 Electrode-Box-B2 8 98 142 1
33 Egg-tart-1 Electrode-Box-B2 9 89 139 1
34 Egg-tart-1 Electrode-Box-B2 10 96 143 1
35 Egg-tart-2 Electrode-Box-A2 0 9 32 0
36 Egg-tart-2 Electrode-Box-A2 1 2 38 0
37 Egg-tart-2 Electrode-Box-A2 2 31 93 0
38 Egg-tart-2 Electrode-Box-A2 3 44 101 0
39 Egg-tart-2 Electrode-Box-A2 4 54 131 0
40 Egg-tart-2 Electrode-Box-A2 5 84 139 0
41 Egg-tart-2 Electrode-Box-A2 6 85 145 0
42 Egg-tart-2 Electrode-Box-A2 7 79 143 0
43 Egg-tart-2 Electrode-Box-A2 8 76 131 0
44 Egg-tart-2 Electrode-Box-A2 9 88 149 0
45 Egg-tart-2 Electrode-Box-A2 10 81 151 0
46 Khoai-lang-2 Naive 0 0 0 0
47 Khoai-lang-2 Naive 1 0 0 0
48 Khoai-lang-2 Naive 2 10 47 0
49 Khoai-lang-2 Naive 3 11 52 0
50 Khoai-lang-2 Naive 4 9 56 0
51 Khoai-lang-2 Naive 5 34 95 0
52 Khoai-lang-2 Naive 6 21 72 0
53 Khoai-lang-2 Naive 7 23 99 0
54 Khoai-lang-2 Naive 8 64 136 0
55 Khoai-lang-2 Naive 9 75 131 0
56 Khoai-lang-2 Naive 10 63 134 0
57 Khoai-tay-2 Naive 1 21 68 0
58 Khoai-tay-2 Naive 2 6 79 0
59 Khoai-tay-2 Naive 3 0 83 0
60 Khoai-tay-2 Naive 4 28 87 0
61 Khoai-tay-2 Naive 5 31 125 0
62 Khoai-tay-2 Naive 6 62 144 0
63 Khoai-tay-2 Naive 7 87 141 0
64 Khoai-tay-2 Naive 8 105 152 0
65 Khoai-tay-2 Naive 9 75 148 0
66 Khoai-tay-2 Naive 10 101 149 0
67 OM-2 Naive 0 1 7 0
68 OM-2 Naive 1 11 33 0
69 OM-2 Naive 2 19 62 0
70 OM-2 Naive 3 29 117 0
71 OM-2 Naive 4 73 126 0
72 OM-2 Naive 5 53 135 0
73 OM-2 Naive 6 73 138 0
74 OM-2 Naive 7 80 131 0
75 OM-2 Naive 8 91 141 0
76 OM-2 Naive 9 90 135 0
77 OM-2 Naive 10 95 142 0
78 Root-beer-1 Electrode-Box-B2 0 11 85 1
79 Root-beer-1 Electrode-Box-B2 1 18 76 1
80 Root-beer-1 Electrode-Box-B2 2 40 105 1
81 Root-beer-1 Electrode-Box-B2 3 55 134 1
82 Root-beer-1 Electrode-Box-B2 4 75 136 1
83 Root-beer-1 Electrode-Box-B2 5 64 133 1
84 Root-beer-1 Electrode-Box-B2 6 104 139 1
85 Root-beer-1 Electrode-Box-B2 7 98 148 1
86 Root-beer-1 Electrode-Box-B2 8 81 145 1
87 Root-beer-1 Electrode-Box-B2 9 89 156 1
88 Root-beer-1 Electrode-Box-B2 10 105 158 1
89 Root-beer-2 Electrode-Box-A2 0 22 74 0
90 Root-beer-2 Electrode-Box-A2 1 31 87 0
91 Root-beer-2 Electrode-Box-A2 2 49 134 0
92 Root-beer-2 Electrode-Box-A2 3 31 89 0
93 Root-beer-2 Electrode-Box-A2 4 60 140 0
94 Root-beer-2 Electrode-Box-A2 5 84 147 0
95 Vu-vuong Naive 0 18 61 0
96 Vu-vuong Naive 1 35 91 0
97 Vu-vuong Naive 2 61 127 0
98 Vu-vuong Naive 3 34 146 0
99 Vu-vuong Naive 4 61 141 0
100 Vu-vuong Naive 5 37 151 0
101 Vu-vuong Naive 6 49 131 0
102 Vu-vuong Naive 7 65 149 0
103 Vu-vuong Naive 8 67 141 0
104 Vu-vuong Naive 9 73 140 0
105 Vu-vuong Naive 10 72 131 0
analysis/matlab/variations/naive_a2_d0_10/result.txt
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==============================================================================
VARIATION: naive_a2_d0_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A2, Naive
N = 10 rats, 104 sessions raw day coverage: treat 0..10, control 0..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 104
Fixed effects coefficients 4
Random effects coefficients 10
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
869.17 885.04 -428.58 857.17
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 10.33 4.4952 2.298 100 0.023646
{'day' } 8.0905 0.53615 15.09 100 1.598e-27
{'stim' } 12.019 8.117 1.4807 100 0.14184
{'day:stim' } 1.4337 0.92893 1.5434 100 0.12589
Lower Upper
1.4115 19.248
7.0268 9.1542
-4.0853 28.123
-0.40926 3.2767
Random effects covariance parameters (95% CIs):
Group: rat (10 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 8.753
Lower Upper
5.0208 15.26
Group: Error
Name Estimate Lower Upper
{'Res Std'} 13.78 11.942 15.902
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(100)= 1.54 F(1)= 2.382 p=0.1259
day (learning) t(100)= 15.09 F(1)= 227.710 p=1.598e-27
stim (main, window start) t(100)= 1.48 F(1)= 2.192 p=0.1418
interaction 95% CI: [-0.41, +3.28]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1259, slope diff=+1.43)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_a2_d0_13/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_a2_d0_13/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-1,Electrode-Box-B2,11,121,148,1
Banh-mi-1,Electrode-Box-B2,12,120,149,1
Banh-mi-1,Electrode-Box-B2,13,135,154,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-1,Electrode-Box-B2,11,96,148,1
Egg-tart-1,Electrode-Box-B2,12,101,156,1
Egg-tart-1,Electrode-Box-B2,13,103,152,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Egg-tart-2,Electrode-Box-A2,11,78,152,0
Egg-tart-2,Electrode-Box-A2,12,96,155,0
Egg-tart-2,Electrode-Box-A2,13,84,155,0
Khoai-lang-2,Naive,0,0,0,0
Khoai-lang-2,Naive,1,0,0,0
Khoai-lang-2,Naive,2,10,47,0
Khoai-lang-2,Naive,3,11,52,0
Khoai-lang-2,Naive,4,9,56,0
Khoai-lang-2,Naive,5,34,95,0
Khoai-lang-2,Naive,6,21,72,0
Khoai-lang-2,Naive,7,23,99,0
Khoai-lang-2,Naive,8,64,136,0
Khoai-lang-2,Naive,9,75,131,0
Khoai-lang-2,Naive,10,63,134,0
Khoai-lang-2,Naive,11,59,139,0
Khoai-lang-2,Naive,12,51,129,0
Khoai-lang-2,Naive,13,73,143,0
Khoai-tay-2,Naive,1,21,68,0
Khoai-tay-2,Naive,2,6,79,0
Khoai-tay-2,Naive,3,0,83,0
Khoai-tay-2,Naive,4,28,87,0
Khoai-tay-2,Naive,5,31,125,0
Khoai-tay-2,Naive,6,62,144,0
Khoai-tay-2,Naive,7,87,141,0
Khoai-tay-2,Naive,8,105,152,0
Khoai-tay-2,Naive,9,75,148,0
Khoai-tay-2,Naive,10,101,149,0
Khoai-tay-2,Naive,11,102,154,0
Khoai-tay-2,Naive,12,95,144,0
Khoai-tay-2,Naive,13,77,149,0
OM-2,Naive,0,1,7,0
OM-2,Naive,1,11,33,0
OM-2,Naive,2,19,62,0
OM-2,Naive,3,29,117,0
OM-2,Naive,4,73,126,0
OM-2,Naive,5,53,135,0
OM-2,Naive,6,73,138,0
OM-2,Naive,7,80,131,0
OM-2,Naive,8,91,141,0
OM-2,Naive,9,90,135,0
OM-2,Naive,10,95,142,0
OM-2,Naive,11,60,133,0
OM-2,Naive,12,58,142,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
Vu-vuong,Naive,0,18,61,0
Vu-vuong,Naive,1,35,91,0
Vu-vuong,Naive,2,61,127,0
Vu-vuong,Naive,3,34,146,0
Vu-vuong,Naive,4,61,141,0
Vu-vuong,Naive,5,37,151,0
Vu-vuong,Naive,6,49,131,0
Vu-vuong,Naive,7,65,149,0
Vu-vuong,Naive,8,67,141,0
Vu-vuong,Naive,9,73,140,0
Vu-vuong,Naive,10,72,131,0
Vu-vuong,Naive,11,89,158,0
Vu-vuong,Naive,12,97,154,0
Vu-vuong,Naive,13,93,145,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-1 Electrode-Box-B2 11 121 148 1
14 Banh-mi-1 Electrode-Box-B2 12 120 149 1
15 Banh-mi-1 Electrode-Box-B2 13 135 154 1
16 Banh-mi-2 Electrode-Box-A2 0 25 97 0
17 Banh-mi-2 Electrode-Box-A2 1 22 101 0
18 Banh-mi-2 Electrode-Box-A2 2 22 119 0
19 Banh-mi-2 Electrode-Box-A2 3 29 118 0
20 Banh-mi-2 Electrode-Box-A2 4 27 136 0
21 Banh-mi-2 Electrode-Box-A2 5 43 146 0
22 Banh-mi-2 Electrode-Box-A2 6 74 146 0
23 Banh-mi-2 Electrode-Box-A2 7 70 148 0
24 Banh-mi-2 Electrode-Box-A2 8 65 130 0
25 Banh-mi-2 Electrode-Box-A2 9 79 151 0
26 Banh-mi-2 Electrode-Box-A2 10 93 152 0
27 Egg-tart-1 Electrode-Box-B2 0 7 56 1
28 Egg-tart-1 Electrode-Box-B2 1 16 78 1
29 Egg-tart-1 Electrode-Box-B2 2 23 103 1
30 Egg-tart-1 Electrode-Box-B2 3 63 120 1
31 Egg-tart-1 Electrode-Box-B2 4 69 132 1
32 Egg-tart-1 Electrode-Box-B2 5 83 136 1
33 Egg-tart-1 Electrode-Box-B2 6 71 142 1
34 Egg-tart-1 Electrode-Box-B2 7 79 138 1
35 Egg-tart-1 Electrode-Box-B2 8 98 142 1
36 Egg-tart-1 Electrode-Box-B2 9 89 139 1
37 Egg-tart-1 Electrode-Box-B2 10 96 143 1
38 Egg-tart-1 Electrode-Box-B2 11 96 148 1
39 Egg-tart-1 Electrode-Box-B2 12 101 156 1
40 Egg-tart-1 Electrode-Box-B2 13 103 152 1
41 Egg-tart-2 Electrode-Box-A2 0 9 32 0
42 Egg-tart-2 Electrode-Box-A2 1 2 38 0
43 Egg-tart-2 Electrode-Box-A2 2 31 93 0
44 Egg-tart-2 Electrode-Box-A2 3 44 101 0
45 Egg-tart-2 Electrode-Box-A2 4 54 131 0
46 Egg-tart-2 Electrode-Box-A2 5 84 139 0
47 Egg-tart-2 Electrode-Box-A2 6 85 145 0
48 Egg-tart-2 Electrode-Box-A2 7 79 143 0
49 Egg-tart-2 Electrode-Box-A2 8 76 131 0
50 Egg-tart-2 Electrode-Box-A2 9 88 149 0
51 Egg-tart-2 Electrode-Box-A2 10 81 151 0
52 Egg-tart-2 Electrode-Box-A2 11 78 152 0
53 Egg-tart-2 Electrode-Box-A2 12 96 155 0
54 Egg-tart-2 Electrode-Box-A2 13 84 155 0
55 Khoai-lang-2 Naive 0 0 0 0
56 Khoai-lang-2 Naive 1 0 0 0
57 Khoai-lang-2 Naive 2 10 47 0
58 Khoai-lang-2 Naive 3 11 52 0
59 Khoai-lang-2 Naive 4 9 56 0
60 Khoai-lang-2 Naive 5 34 95 0
61 Khoai-lang-2 Naive 6 21 72 0
62 Khoai-lang-2 Naive 7 23 99 0
63 Khoai-lang-2 Naive 8 64 136 0
64 Khoai-lang-2 Naive 9 75 131 0
65 Khoai-lang-2 Naive 10 63 134 0
66 Khoai-lang-2 Naive 11 59 139 0
67 Khoai-lang-2 Naive 12 51 129 0
68 Khoai-lang-2 Naive 13 73 143 0
69 Khoai-tay-2 Naive 1 21 68 0
70 Khoai-tay-2 Naive 2 6 79 0
71 Khoai-tay-2 Naive 3 0 83 0
72 Khoai-tay-2 Naive 4 28 87 0
73 Khoai-tay-2 Naive 5 31 125 0
74 Khoai-tay-2 Naive 6 62 144 0
75 Khoai-tay-2 Naive 7 87 141 0
76 Khoai-tay-2 Naive 8 105 152 0
77 Khoai-tay-2 Naive 9 75 148 0
78 Khoai-tay-2 Naive 10 101 149 0
79 Khoai-tay-2 Naive 11 102 154 0
80 Khoai-tay-2 Naive 12 95 144 0
81 Khoai-tay-2 Naive 13 77 149 0
82 OM-2 Naive 0 1 7 0
83 OM-2 Naive 1 11 33 0
84 OM-2 Naive 2 19 62 0
85 OM-2 Naive 3 29 117 0
86 OM-2 Naive 4 73 126 0
87 OM-2 Naive 5 53 135 0
88 OM-2 Naive 6 73 138 0
89 OM-2 Naive 7 80 131 0
90 OM-2 Naive 8 91 141 0
91 OM-2 Naive 9 90 135 0
92 OM-2 Naive 10 95 142 0
93 OM-2 Naive 11 60 133 0
94 OM-2 Naive 12 58 142 0
95 Root-beer-1 Electrode-Box-B2 0 11 85 1
96 Root-beer-1 Electrode-Box-B2 1 18 76 1
97 Root-beer-1 Electrode-Box-B2 2 40 105 1
98 Root-beer-1 Electrode-Box-B2 3 55 134 1
99 Root-beer-1 Electrode-Box-B2 4 75 136 1
100 Root-beer-1 Electrode-Box-B2 5 64 133 1
101 Root-beer-1 Electrode-Box-B2 6 104 139 1
102 Root-beer-1 Electrode-Box-B2 7 98 148 1
103 Root-beer-1 Electrode-Box-B2 8 81 145 1
104 Root-beer-1 Electrode-Box-B2 9 89 156 1
105 Root-beer-1 Electrode-Box-B2 10 105 158 1
106 Root-beer-2 Electrode-Box-A2 0 22 74 0
107 Root-beer-2 Electrode-Box-A2 1 31 87 0
108 Root-beer-2 Electrode-Box-A2 2 49 134 0
109 Root-beer-2 Electrode-Box-A2 3 31 89 0
110 Root-beer-2 Electrode-Box-A2 4 60 140 0
111 Root-beer-2 Electrode-Box-A2 5 84 147 0
112 Vu-vuong Naive 0 18 61 0
113 Vu-vuong Naive 1 35 91 0
114 Vu-vuong Naive 2 61 127 0
115 Vu-vuong Naive 3 34 146 0
116 Vu-vuong Naive 4 61 141 0
117 Vu-vuong Naive 5 37 151 0
118 Vu-vuong Naive 6 49 131 0
119 Vu-vuong Naive 7 65 149 0
120 Vu-vuong Naive 8 67 141 0
121 Vu-vuong Naive 9 73 140 0
122 Vu-vuong Naive 10 72 131 0
123 Vu-vuong Naive 11 89 158 0
124 Vu-vuong Naive 12 97 154 0
125 Vu-vuong Naive 13 93 145 0
analysis/matlab/variations/naive_a2_d0_13/result.txt
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==============================================================================
VARIATION: naive_a2_d0_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A2, Naive
N = 10 rats, 124 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 124
Fixed effects coefficients 4
Random effects coefficients 10
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
1055.4 1072.3 -521.69 1043.4
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 16.252 4.4327 3.6664 120 0.00036779
{'day' } 6.4053 0.43948 14.575 120 2.5469e-28
{'stim' } 11.52 8.023 1.4359 120 0.15363
{'day:stim' } 1.6137 0.77637 2.0785 120 0.039797
Lower Upper
7.4757 25.029
5.5352 7.2755
-4.3648 27.405
0.076509 3.1508
Random effects covariance parameters (95% CIs):
Group: rat (10 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 8.4599
Lower Upper
4.8243 14.835
Group: Error
Name Estimate Lower Upper
{'Res Std'} 15.264 13.405 17.381
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(120)= 2.08 F(1)= 4.320 p=0.0398
day (learning) t(120)= 14.57 F(1)= 212.424 p=2.547e-28
stim (main, window start) t(120)= 1.44 F(1)= 2.062 p=0.1536
interaction 95% CI: [+0.08, +3.15]
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.0398, slope diff=+1.61)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_a2_d0_5/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_a2_d0_5/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Khoai-lang-2,Naive,0,0,0,0
Khoai-lang-2,Naive,1,0,0,0
Khoai-lang-2,Naive,2,10,47,0
Khoai-lang-2,Naive,3,11,52,0
Khoai-lang-2,Naive,4,9,56,0
Khoai-lang-2,Naive,5,34,95,0
Khoai-tay-2,Naive,1,21,68,0
Khoai-tay-2,Naive,2,6,79,0
Khoai-tay-2,Naive,3,0,83,0
Khoai-tay-2,Naive,4,28,87,0
Khoai-tay-2,Naive,5,31,125,0
OM-2,Naive,0,1,7,0
OM-2,Naive,1,11,33,0
OM-2,Naive,2,19,62,0
OM-2,Naive,3,29,117,0
OM-2,Naive,4,73,126,0
OM-2,Naive,5,53,135,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
Vu-vuong,Naive,0,18,61,0
Vu-vuong,Naive,1,35,91,0
Vu-vuong,Naive,2,61,127,0
Vu-vuong,Naive,3,34,146,0
Vu-vuong,Naive,4,61,141,0
Vu-vuong,Naive,5,37,151,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-2 Electrode-Box-A2 0 25 97 0
9 Banh-mi-2 Electrode-Box-A2 1 22 101 0
10 Banh-mi-2 Electrode-Box-A2 2 22 119 0
11 Banh-mi-2 Electrode-Box-A2 3 29 118 0
12 Banh-mi-2 Electrode-Box-A2 4 27 136 0
13 Banh-mi-2 Electrode-Box-A2 5 43 146 0
14 Egg-tart-1 Electrode-Box-B2 0 7 56 1
15 Egg-tart-1 Electrode-Box-B2 1 16 78 1
16 Egg-tart-1 Electrode-Box-B2 2 23 103 1
17 Egg-tart-1 Electrode-Box-B2 3 63 120 1
18 Egg-tart-1 Electrode-Box-B2 4 69 132 1
19 Egg-tart-1 Electrode-Box-B2 5 83 136 1
20 Egg-tart-2 Electrode-Box-A2 0 9 32 0
21 Egg-tart-2 Electrode-Box-A2 1 2 38 0
22 Egg-tart-2 Electrode-Box-A2 2 31 93 0
23 Egg-tart-2 Electrode-Box-A2 3 44 101 0
24 Egg-tart-2 Electrode-Box-A2 4 54 131 0
25 Egg-tart-2 Electrode-Box-A2 5 84 139 0
26 Khoai-lang-2 Naive 0 0 0 0
27 Khoai-lang-2 Naive 1 0 0 0
28 Khoai-lang-2 Naive 2 10 47 0
29 Khoai-lang-2 Naive 3 11 52 0
30 Khoai-lang-2 Naive 4 9 56 0
31 Khoai-lang-2 Naive 5 34 95 0
32 Khoai-tay-2 Naive 1 21 68 0
33 Khoai-tay-2 Naive 2 6 79 0
34 Khoai-tay-2 Naive 3 0 83 0
35 Khoai-tay-2 Naive 4 28 87 0
36 Khoai-tay-2 Naive 5 31 125 0
37 OM-2 Naive 0 1 7 0
38 OM-2 Naive 1 11 33 0
39 OM-2 Naive 2 19 62 0
40 OM-2 Naive 3 29 117 0
41 OM-2 Naive 4 73 126 0
42 OM-2 Naive 5 53 135 0
43 Root-beer-1 Electrode-Box-B2 0 11 85 1
44 Root-beer-1 Electrode-Box-B2 1 18 76 1
45 Root-beer-1 Electrode-Box-B2 2 40 105 1
46 Root-beer-1 Electrode-Box-B2 3 55 134 1
47 Root-beer-1 Electrode-Box-B2 4 75 136 1
48 Root-beer-1 Electrode-Box-B2 5 64 133 1
49 Root-beer-2 Electrode-Box-A2 0 22 74 0
50 Root-beer-2 Electrode-Box-A2 1 31 87 0
51 Root-beer-2 Electrode-Box-A2 2 49 134 0
52 Root-beer-2 Electrode-Box-A2 3 31 89 0
53 Root-beer-2 Electrode-Box-A2 4 60 140 0
54 Root-beer-2 Electrode-Box-A2 5 84 147 0
55 Vu-vuong Naive 0 18 61 0
56 Vu-vuong Naive 1 35 91 0
57 Vu-vuong Naive 2 61 127 0
58 Vu-vuong Naive 3 34 146 0
59 Vu-vuong Naive 4 61 141 0
60 Vu-vuong Naive 5 37 151 0
analysis/matlab/variations/naive_a2_d0_5/result.txt
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==============================================================================
VARIATION: naive_a2_d0_5
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A2, Naive
N = 10 rats, 59 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 59
Fixed effects coefficients 4
Random effects coefficients 10
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
489.28 501.74 -238.64 477.28
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 9.0584 5.0267 1.8021 55 0.077018
{'day' } 8.2568 1.1279 7.3206 55 1.1254e-09
{'stim' } 0.44958 9.048 0.049688 55 0.96055
{'day:stim' } 6.9622 2.0162 3.4532 55 0.0010733
Lower Upper
-1.0153 19.132
5.9965 10.517
-17.683 18.582
2.9217 11.003
Random effects covariance parameters (95% CIs):
Group: rat (10 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 9.6432
Lower Upper
5.5002 16.907
Group: Error
Name Estimate Lower Upper
{'Res Std'} 12.109 9.9317 14.763
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(55)= 3.45 F(1)= 11.924 p=0.001073
day (learning) t(55)= 7.32 F(1)= 53.591 p=1.125e-09
stim (main, window start) t(55)= 0.05 F(1)= 0.002 p=0.9606
interaction 95% CI: [+2.92, +11.00]
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.001073, slope diff=+6.96)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_a2_d6_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_a2_d6_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-lang-2,Naive,6,21,72,0
Khoai-lang-2,Naive,7,23,99,0
Khoai-lang-2,Naive,8,64,136,0
Khoai-lang-2,Naive,9,75,131,0
Khoai-lang-2,Naive,10,63,134,0
Khoai-tay-2,Naive,6,62,144,0
Khoai-tay-2,Naive,7,87,141,0
Khoai-tay-2,Naive,8,105,152,0
Khoai-tay-2,Naive,9,75,148,0
Khoai-tay-2,Naive,10,101,149,0
OM-2,Naive,6,73,138,0
OM-2,Naive,7,80,131,0
OM-2,Naive,8,91,141,0
OM-2,Naive,9,90,135,0
OM-2,Naive,10,95,142,0
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Vu-vuong,Naive,6,49,131,0
Vu-vuong,Naive,7,65,149,0
Vu-vuong,Naive,8,67,141,0
Vu-vuong,Naive,9,73,140,0
Vu-vuong,Naive,10,72,131,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-2 Electrode-Box-A2 6 74 146 0
8 Banh-mi-2 Electrode-Box-A2 7 70 148 0
9 Banh-mi-2 Electrode-Box-A2 8 65 130 0
10 Banh-mi-2 Electrode-Box-A2 9 79 151 0
11 Banh-mi-2 Electrode-Box-A2 10 93 152 0
12 Egg-tart-1 Electrode-Box-B2 6 71 142 1
13 Egg-tart-1 Electrode-Box-B2 7 79 138 1
14 Egg-tart-1 Electrode-Box-B2 8 98 142 1
15 Egg-tart-1 Electrode-Box-B2 9 89 139 1
16 Egg-tart-1 Electrode-Box-B2 10 96 143 1
17 Egg-tart-2 Electrode-Box-A2 6 85 145 0
18 Egg-tart-2 Electrode-Box-A2 7 79 143 0
19 Egg-tart-2 Electrode-Box-A2 8 76 131 0
20 Egg-tart-2 Electrode-Box-A2 9 88 149 0
21 Egg-tart-2 Electrode-Box-A2 10 81 151 0
22 Khoai-lang-2 Naive 6 21 72 0
23 Khoai-lang-2 Naive 7 23 99 0
24 Khoai-lang-2 Naive 8 64 136 0
25 Khoai-lang-2 Naive 9 75 131 0
26 Khoai-lang-2 Naive 10 63 134 0
27 Khoai-tay-2 Naive 6 62 144 0
28 Khoai-tay-2 Naive 7 87 141 0
29 Khoai-tay-2 Naive 8 105 152 0
30 Khoai-tay-2 Naive 9 75 148 0
31 Khoai-tay-2 Naive 10 101 149 0
32 OM-2 Naive 6 73 138 0
33 OM-2 Naive 7 80 131 0
34 OM-2 Naive 8 91 141 0
35 OM-2 Naive 9 90 135 0
36 OM-2 Naive 10 95 142 0
37 Root-beer-1 Electrode-Box-B2 6 104 139 1
38 Root-beer-1 Electrode-Box-B2 7 98 148 1
39 Root-beer-1 Electrode-Box-B2 8 81 145 1
40 Root-beer-1 Electrode-Box-B2 9 89 156 1
41 Root-beer-1 Electrode-Box-B2 10 105 158 1
42 Vu-vuong Naive 6 49 131 0
43 Vu-vuong Naive 7 65 149 0
44 Vu-vuong Naive 8 67 141 0
45 Vu-vuong Naive 9 73 140 0
46 Vu-vuong Naive 10 72 131 0
analysis/matlab/variations/naive_a2_d6_10/result.txt
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==============================================================================
VARIATION: naive_a2_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A2, Naive
N = 9 rats, 45 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 45
Fixed effects coefficients 4
Random effects coefficients 9
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
368.51 379.35 -178.25 356.51
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 62.1 5.5902 11.109 41 6.1328e-14
{'day' } 5.9667 1.3623 4.3798 41 8.0273e-05
{'stim' } 26.433 9.6824 2.73 41 0.0092904
{'day:stim' } -1.8 2.3596 -0.76285 41 0.44992
Lower Upper
50.81 73.39
3.2154 8.7179
6.8793 45.987
-6.5653 2.9653
Random effects covariance parameters (95% CIs):
Group: rat (9 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 10.986
Lower Upper
6.3454 19.02
Group: Error
Name Estimate Lower Upper
{'Res Std'} 10.552 8.376 13.294
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(41)= -0.76 F(1)= 0.582 p=0.4499
day (learning) t(41)= 4.38 F(1)= 19.183 p=8.027e-05
stim (main, window start) t(41)= 2.73 F(1)= 7.453 p=0.00929
interaction 95% CI: [-6.57, +2.97]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4499, slope diff=-1.80)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_a2_d6_13/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_a2_d6_13/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-1,Electrode-Box-B2,11,121,148,1
Banh-mi-1,Electrode-Box-B2,12,120,149,1
Banh-mi-1,Electrode-Box-B2,13,135,154,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-1,Electrode-Box-B2,11,96,148,1
Egg-tart-1,Electrode-Box-B2,12,101,156,1
Egg-tart-1,Electrode-Box-B2,13,103,152,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Egg-tart-2,Electrode-Box-A2,11,78,152,0
Egg-tart-2,Electrode-Box-A2,12,96,155,0
Egg-tart-2,Electrode-Box-A2,13,84,155,0
Khoai-lang-2,Naive,6,21,72,0
Khoai-lang-2,Naive,7,23,99,0
Khoai-lang-2,Naive,8,64,136,0
Khoai-lang-2,Naive,9,75,131,0
Khoai-lang-2,Naive,10,63,134,0
Khoai-lang-2,Naive,11,59,139,0
Khoai-lang-2,Naive,12,51,129,0
Khoai-lang-2,Naive,13,73,143,0
Khoai-tay-2,Naive,6,62,144,0
Khoai-tay-2,Naive,7,87,141,0
Khoai-tay-2,Naive,8,105,152,0
Khoai-tay-2,Naive,9,75,148,0
Khoai-tay-2,Naive,10,101,149,0
Khoai-tay-2,Naive,11,102,154,0
Khoai-tay-2,Naive,12,95,144,0
Khoai-tay-2,Naive,13,77,149,0
OM-2,Naive,6,73,138,0
OM-2,Naive,7,80,131,0
OM-2,Naive,8,91,141,0
OM-2,Naive,9,90,135,0
OM-2,Naive,10,95,142,0
OM-2,Naive,11,60,133,0
OM-2,Naive,12,58,142,0
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Vu-vuong,Naive,6,49,131,0
Vu-vuong,Naive,7,65,149,0
Vu-vuong,Naive,8,67,141,0
Vu-vuong,Naive,9,73,140,0
Vu-vuong,Naive,10,72,131,0
Vu-vuong,Naive,11,89,158,0
Vu-vuong,Naive,12,97,154,0
Vu-vuong,Naive,13,93,145,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-1 Electrode-Box-B2 11 121 148 1
8 Banh-mi-1 Electrode-Box-B2 12 120 149 1
9 Banh-mi-1 Electrode-Box-B2 13 135 154 1
10 Banh-mi-2 Electrode-Box-A2 6 74 146 0
11 Banh-mi-2 Electrode-Box-A2 7 70 148 0
12 Banh-mi-2 Electrode-Box-A2 8 65 130 0
13 Banh-mi-2 Electrode-Box-A2 9 79 151 0
14 Banh-mi-2 Electrode-Box-A2 10 93 152 0
15 Egg-tart-1 Electrode-Box-B2 6 71 142 1
16 Egg-tart-1 Electrode-Box-B2 7 79 138 1
17 Egg-tart-1 Electrode-Box-B2 8 98 142 1
18 Egg-tart-1 Electrode-Box-B2 9 89 139 1
19 Egg-tart-1 Electrode-Box-B2 10 96 143 1
20 Egg-tart-1 Electrode-Box-B2 11 96 148 1
21 Egg-tart-1 Electrode-Box-B2 12 101 156 1
22 Egg-tart-1 Electrode-Box-B2 13 103 152 1
23 Egg-tart-2 Electrode-Box-A2 6 85 145 0
24 Egg-tart-2 Electrode-Box-A2 7 79 143 0
25 Egg-tart-2 Electrode-Box-A2 8 76 131 0
26 Egg-tart-2 Electrode-Box-A2 9 88 149 0
27 Egg-tart-2 Electrode-Box-A2 10 81 151 0
28 Egg-tart-2 Electrode-Box-A2 11 78 152 0
29 Egg-tart-2 Electrode-Box-A2 12 96 155 0
30 Egg-tart-2 Electrode-Box-A2 13 84 155 0
31 Khoai-lang-2 Naive 6 21 72 0
32 Khoai-lang-2 Naive 7 23 99 0
33 Khoai-lang-2 Naive 8 64 136 0
34 Khoai-lang-2 Naive 9 75 131 0
35 Khoai-lang-2 Naive 10 63 134 0
36 Khoai-lang-2 Naive 11 59 139 0
37 Khoai-lang-2 Naive 12 51 129 0
38 Khoai-lang-2 Naive 13 73 143 0
39 Khoai-tay-2 Naive 6 62 144 0
40 Khoai-tay-2 Naive 7 87 141 0
41 Khoai-tay-2 Naive 8 105 152 0
42 Khoai-tay-2 Naive 9 75 148 0
43 Khoai-tay-2 Naive 10 101 149 0
44 Khoai-tay-2 Naive 11 102 154 0
45 Khoai-tay-2 Naive 12 95 144 0
46 Khoai-tay-2 Naive 13 77 149 0
47 OM-2 Naive 6 73 138 0
48 OM-2 Naive 7 80 131 0
49 OM-2 Naive 8 91 141 0
50 OM-2 Naive 9 90 135 0
51 OM-2 Naive 10 95 142 0
52 OM-2 Naive 11 60 133 0
53 OM-2 Naive 12 58 142 0
54 Root-beer-1 Electrode-Box-B2 6 104 139 1
55 Root-beer-1 Electrode-Box-B2 7 98 148 1
56 Root-beer-1 Electrode-Box-B2 8 81 145 1
57 Root-beer-1 Electrode-Box-B2 9 89 156 1
58 Root-beer-1 Electrode-Box-B2 10 105 158 1
59 Vu-vuong Naive 6 49 131 0
60 Vu-vuong Naive 7 65 149 0
61 Vu-vuong Naive 8 67 141 0
62 Vu-vuong Naive 9 73 140 0
63 Vu-vuong Naive 10 72 131 0
64 Vu-vuong Naive 11 89 158 0
65 Vu-vuong Naive 12 97 154 0
66 Vu-vuong Naive 13 93 145 0
analysis/matlab/variations/naive_a2_d6_13/result.txt
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==============================================================================
VARIATION: naive_a2_d6_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A2, Naive
N = 9 rats, 65 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 65
Fixed effects coefficients 4
Random effects coefficients 9
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
535.5 548.54 -261.75 523.5
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 66.813 5.1278 13.03 61 2.7998e-19
{'day' } 2.8262 0.83395 3.3889 61 0.0012345
{'stim' } 21.913 8.8928 2.4641 61 0.016568
{'day:stim' } 1.1553 1.4855 0.77769 61 0.43976
Lower Upper
56.56 77.067
1.1586 4.4937
4.1309 39.695
-1.8152 4.1258
Random effects covariance parameters (95% CIs):
Group: rat (9 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 9.7589
Lower Upper
5.6191 16.948
Group: Error
Name Estimate Lower Upper
{'Res Std'} 12.033 10.004 14.473
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(61)= 0.78 F(1)= 0.605 p=0.4398
day (learning) t(61)= 3.39 F(1)= 11.485 p=0.001235
stim (main, window start) t(61)= 2.46 F(1)= 6.072 p=0.01657
interaction 95% CI: [-1.82, +4.13]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4398, slope diff=+1.16)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_boxa_d0_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_boxa_d0_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-lang-2,Naive,0,0,0,0
Khoai-lang-2,Naive,1,0,0,0
Khoai-lang-2,Naive,2,10,47,0
Khoai-lang-2,Naive,3,11,52,0
Khoai-lang-2,Naive,4,9,56,0
Khoai-lang-2,Naive,5,34,95,0
Khoai-lang-2,Naive,6,21,72,0
Khoai-lang-2,Naive,7,23,99,0
Khoai-lang-2,Naive,8,64,136,0
Khoai-lang-2,Naive,9,75,131,0
Khoai-lang-2,Naive,10,63,134,0
Khoai-tay-1,Electrode-Box-A,0,14,62,0
Khoai-tay-1,Electrode-Box-A,1,22,83,0
Khoai-tay-1,Electrode-Box-A,2,6,79,0
Khoai-tay-1,Electrode-Box-A,3,28,97,0
Khoai-tay-1,Electrode-Box-A,4,60,134,0
Khoai-tay-1,Electrode-Box-A,5,75,138,0
Khoai-tay-1,Electrode-Box-A,6,81,137,0
Khoai-tay-1,Electrode-Box-A,7,78,147,0
Khoai-tay-1,Electrode-Box-A,8,91,132,0
Khoai-tay-1,Electrode-Box-A,9,99,146,0
Khoai-tay-1,Electrode-Box-A,10,94,143,0
Khoai-tay-2,Naive,1,21,68,0
Khoai-tay-2,Naive,2,6,79,0
Khoai-tay-2,Naive,3,0,83,0
Khoai-tay-2,Naive,4,28,87,0
Khoai-tay-2,Naive,5,31,125,0
Khoai-tay-2,Naive,6,62,144,0
Khoai-tay-2,Naive,7,87,141,0
Khoai-tay-2,Naive,8,105,152,0
Khoai-tay-2,Naive,9,75,148,0
Khoai-tay-2,Naive,10,101,149,0
OM-2,Naive,0,1,7,0
OM-2,Naive,1,11,33,0
OM-2,Naive,2,19,62,0
OM-2,Naive,3,29,117,0
OM-2,Naive,4,73,126,0
OM-2,Naive,5,53,135,0
OM-2,Naive,6,73,138,0
OM-2,Naive,7,80,131,0
OM-2,Naive,8,91,141,0
OM-2,Naive,9,90,135,0
OM-2,Naive,10,95,142,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
Vu-vuong,Naive,0,18,61,0
Vu-vuong,Naive,1,35,91,0
Vu-vuong,Naive,2,61,127,0
Vu-vuong,Naive,3,34,146,0
Vu-vuong,Naive,4,61,141,0
Vu-vuong,Naive,5,37,151,0
Vu-vuong,Naive,6,49,131,0
Vu-vuong,Naive,7,65,149,0
Vu-vuong,Naive,8,67,141,0
Vu-vuong,Naive,9,73,140,0
Vu-vuong,Naive,10,72,131,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-2 Electrode-Box-A2 0 25 97 0
14 Banh-mi-2 Electrode-Box-A2 1 22 101 0
15 Banh-mi-2 Electrode-Box-A2 2 22 119 0
16 Banh-mi-2 Electrode-Box-A2 3 29 118 0
17 Banh-mi-2 Electrode-Box-A2 4 27 136 0
18 Banh-mi-2 Electrode-Box-A2 5 43 146 0
19 Banh-mi-2 Electrode-Box-A2 6 74 146 0
20 Banh-mi-2 Electrode-Box-A2 7 70 148 0
21 Banh-mi-2 Electrode-Box-A2 8 65 130 0
22 Banh-mi-2 Electrode-Box-A2 9 79 151 0
23 Banh-mi-2 Electrode-Box-A2 10 93 152 0
24 Egg-tart-1 Electrode-Box-B2 0 7 56 1
25 Egg-tart-1 Electrode-Box-B2 1 16 78 1
26 Egg-tart-1 Electrode-Box-B2 2 23 103 1
27 Egg-tart-1 Electrode-Box-B2 3 63 120 1
28 Egg-tart-1 Electrode-Box-B2 4 69 132 1
29 Egg-tart-1 Electrode-Box-B2 5 83 136 1
30 Egg-tart-1 Electrode-Box-B2 6 71 142 1
31 Egg-tart-1 Electrode-Box-B2 7 79 138 1
32 Egg-tart-1 Electrode-Box-B2 8 98 142 1
33 Egg-tart-1 Electrode-Box-B2 9 89 139 1
34 Egg-tart-1 Electrode-Box-B2 10 96 143 1
35 Egg-tart-2 Electrode-Box-A2 0 9 32 0
36 Egg-tart-2 Electrode-Box-A2 1 2 38 0
37 Egg-tart-2 Electrode-Box-A2 2 31 93 0
38 Egg-tart-2 Electrode-Box-A2 3 44 101 0
39 Egg-tart-2 Electrode-Box-A2 4 54 131 0
40 Egg-tart-2 Electrode-Box-A2 5 84 139 0
41 Egg-tart-2 Electrode-Box-A2 6 85 145 0
42 Egg-tart-2 Electrode-Box-A2 7 79 143 0
43 Egg-tart-2 Electrode-Box-A2 8 76 131 0
44 Egg-tart-2 Electrode-Box-A2 9 88 149 0
45 Egg-tart-2 Electrode-Box-A2 10 81 151 0
46 Khoai-lang-2 Naive 0 0 0 0
47 Khoai-lang-2 Naive 1 0 0 0
48 Khoai-lang-2 Naive 2 10 47 0
49 Khoai-lang-2 Naive 3 11 52 0
50 Khoai-lang-2 Naive 4 9 56 0
51 Khoai-lang-2 Naive 5 34 95 0
52 Khoai-lang-2 Naive 6 21 72 0
53 Khoai-lang-2 Naive 7 23 99 0
54 Khoai-lang-2 Naive 8 64 136 0
55 Khoai-lang-2 Naive 9 75 131 0
56 Khoai-lang-2 Naive 10 63 134 0
57 Khoai-tay-1 Electrode-Box-A 0 14 62 0
58 Khoai-tay-1 Electrode-Box-A 1 22 83 0
59 Khoai-tay-1 Electrode-Box-A 2 6 79 0
60 Khoai-tay-1 Electrode-Box-A 3 28 97 0
61 Khoai-tay-1 Electrode-Box-A 4 60 134 0
62 Khoai-tay-1 Electrode-Box-A 5 75 138 0
63 Khoai-tay-1 Electrode-Box-A 6 81 137 0
64 Khoai-tay-1 Electrode-Box-A 7 78 147 0
65 Khoai-tay-1 Electrode-Box-A 8 91 132 0
66 Khoai-tay-1 Electrode-Box-A 9 99 146 0
67 Khoai-tay-1 Electrode-Box-A 10 94 143 0
68 Khoai-tay-2 Naive 1 21 68 0
69 Khoai-tay-2 Naive 2 6 79 0
70 Khoai-tay-2 Naive 3 0 83 0
71 Khoai-tay-2 Naive 4 28 87 0
72 Khoai-tay-2 Naive 5 31 125 0
73 Khoai-tay-2 Naive 6 62 144 0
74 Khoai-tay-2 Naive 7 87 141 0
75 Khoai-tay-2 Naive 8 105 152 0
76 Khoai-tay-2 Naive 9 75 148 0
77 Khoai-tay-2 Naive 10 101 149 0
78 OM-2 Naive 0 1 7 0
79 OM-2 Naive 1 11 33 0
80 OM-2 Naive 2 19 62 0
81 OM-2 Naive 3 29 117 0
82 OM-2 Naive 4 73 126 0
83 OM-2 Naive 5 53 135 0
84 OM-2 Naive 6 73 138 0
85 OM-2 Naive 7 80 131 0
86 OM-2 Naive 8 91 141 0
87 OM-2 Naive 9 90 135 0
88 OM-2 Naive 10 95 142 0
89 Root-beer-1 Electrode-Box-B2 0 11 85 1
90 Root-beer-1 Electrode-Box-B2 1 18 76 1
91 Root-beer-1 Electrode-Box-B2 2 40 105 1
92 Root-beer-1 Electrode-Box-B2 3 55 134 1
93 Root-beer-1 Electrode-Box-B2 4 75 136 1
94 Root-beer-1 Electrode-Box-B2 5 64 133 1
95 Root-beer-1 Electrode-Box-B2 6 104 139 1
96 Root-beer-1 Electrode-Box-B2 7 98 148 1
97 Root-beer-1 Electrode-Box-B2 8 81 145 1
98 Root-beer-1 Electrode-Box-B2 9 89 156 1
99 Root-beer-1 Electrode-Box-B2 10 105 158 1
100 Root-beer-2 Electrode-Box-A2 0 22 74 0
101 Root-beer-2 Electrode-Box-A2 1 31 87 0
102 Root-beer-2 Electrode-Box-A2 2 49 134 0
103 Root-beer-2 Electrode-Box-A2 3 31 89 0
104 Root-beer-2 Electrode-Box-A2 4 60 140 0
105 Root-beer-2 Electrode-Box-A2 5 84 147 0
106 Vu-vuong Naive 0 18 61 0
107 Vu-vuong Naive 1 35 91 0
108 Vu-vuong Naive 2 61 127 0
109 Vu-vuong Naive 3 34 146 0
110 Vu-vuong Naive 4 61 141 0
111 Vu-vuong Naive 5 37 151 0
112 Vu-vuong Naive 6 49 131 0
113 Vu-vuong Naive 7 65 149 0
114 Vu-vuong Naive 8 67 141 0
115 Vu-vuong Naive 9 73 140 0
116 Vu-vuong Naive 10 72 131 0
analysis/matlab/variations/naive_boxa_d0_10/result.txt
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==============================================================================
VARIATION: naive_boxa_d0_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
N = 11 rats, 115 sessions raw day coverage: treat 0..10, control 0..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 115
Fixed effects coefficients 4
Random effects coefficients 11
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
958.48 974.95 -473.24 946.48
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 10.139 4.1642 2.4349 111 0.016488
{'day' } 8.3463 0.49368 16.906 111 1.7432e-32
{'stim' } 12.209 7.8948 1.5465 111 0.12484
{'day:stim' } 1.1779 0.90166 1.3064 111 0.19412
Lower Upper
1.8878 18.391
7.3681 9.3246
-3.4352 27.853
-0.60877 2.9646
Random effects covariance parameters (95% CIs):
Group: rat (11 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 8.6713
Lower Upper
5.112 14.709
Group: Error
Name Estimate Lower Upper
{'Res Std'} 13.706 11.962 15.704
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(111)= 1.31 F(1)= 1.707 p=0.1941
day (learning) t(111)= 16.91 F(1)= 285.824 p=1.743e-32
stim (main, window start) t(111)= 1.55 F(1)= 2.392 p=0.1248
interaction 95% CI: [-0.61, +2.96]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1941, slope diff=+1.18)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_boxa_d0_13/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_boxa_d0_13/data.csv
+139
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-1,Electrode-Box-B2,11,121,148,1
Banh-mi-1,Electrode-Box-B2,12,120,149,1
Banh-mi-1,Electrode-Box-B2,13,135,154,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-1,Electrode-Box-B2,11,96,148,1
Egg-tart-1,Electrode-Box-B2,12,101,156,1
Egg-tart-1,Electrode-Box-B2,13,103,152,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Egg-tart-2,Electrode-Box-A2,11,78,152,0
Egg-tart-2,Electrode-Box-A2,12,96,155,0
Egg-tart-2,Electrode-Box-A2,13,84,155,0
Khoai-lang-2,Naive,0,0,0,0
Khoai-lang-2,Naive,1,0,0,0
Khoai-lang-2,Naive,2,10,47,0
Khoai-lang-2,Naive,3,11,52,0
Khoai-lang-2,Naive,4,9,56,0
Khoai-lang-2,Naive,5,34,95,0
Khoai-lang-2,Naive,6,21,72,0
Khoai-lang-2,Naive,7,23,99,0
Khoai-lang-2,Naive,8,64,136,0
Khoai-lang-2,Naive,9,75,131,0
Khoai-lang-2,Naive,10,63,134,0
Khoai-lang-2,Naive,11,59,139,0
Khoai-lang-2,Naive,12,51,129,0
Khoai-lang-2,Naive,13,73,143,0
Khoai-tay-1,Electrode-Box-A,0,14,62,0
Khoai-tay-1,Electrode-Box-A,1,22,83,0
Khoai-tay-1,Electrode-Box-A,2,6,79,0
Khoai-tay-1,Electrode-Box-A,3,28,97,0
Khoai-tay-1,Electrode-Box-A,4,60,134,0
Khoai-tay-1,Electrode-Box-A,5,75,138,0
Khoai-tay-1,Electrode-Box-A,6,81,137,0
Khoai-tay-1,Electrode-Box-A,7,78,147,0
Khoai-tay-1,Electrode-Box-A,8,91,132,0
Khoai-tay-1,Electrode-Box-A,9,99,146,0
Khoai-tay-1,Electrode-Box-A,10,94,143,0
Khoai-tay-1,Electrode-Box-A,11,110,156,0
Khoai-tay-1,Electrode-Box-A,12,105,143,0
Khoai-tay-1,Electrode-Box-A,13,106,153,0
Khoai-tay-2,Naive,1,21,68,0
Khoai-tay-2,Naive,2,6,79,0
Khoai-tay-2,Naive,3,0,83,0
Khoai-tay-2,Naive,4,28,87,0
Khoai-tay-2,Naive,5,31,125,0
Khoai-tay-2,Naive,6,62,144,0
Khoai-tay-2,Naive,7,87,141,0
Khoai-tay-2,Naive,8,105,152,0
Khoai-tay-2,Naive,9,75,148,0
Khoai-tay-2,Naive,10,101,149,0
Khoai-tay-2,Naive,11,102,154,0
Khoai-tay-2,Naive,12,95,144,0
Khoai-tay-2,Naive,13,77,149,0
OM-2,Naive,0,1,7,0
OM-2,Naive,1,11,33,0
OM-2,Naive,2,19,62,0
OM-2,Naive,3,29,117,0
OM-2,Naive,4,73,126,0
OM-2,Naive,5,53,135,0
OM-2,Naive,6,73,138,0
OM-2,Naive,7,80,131,0
OM-2,Naive,8,91,141,0
OM-2,Naive,9,90,135,0
OM-2,Naive,10,95,142,0
OM-2,Naive,11,60,133,0
OM-2,Naive,12,58,142,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
Vu-vuong,Naive,0,18,61,0
Vu-vuong,Naive,1,35,91,0
Vu-vuong,Naive,2,61,127,0
Vu-vuong,Naive,3,34,146,0
Vu-vuong,Naive,4,61,141,0
Vu-vuong,Naive,5,37,151,0
Vu-vuong,Naive,6,49,131,0
Vu-vuong,Naive,7,65,149,0
Vu-vuong,Naive,8,67,141,0
Vu-vuong,Naive,9,73,140,0
Vu-vuong,Naive,10,72,131,0
Vu-vuong,Naive,11,89,158,0
Vu-vuong,Naive,12,97,154,0
Vu-vuong,Naive,13,93,145,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-1 Electrode-Box-B2 11 121 148 1
14 Banh-mi-1 Electrode-Box-B2 12 120 149 1
15 Banh-mi-1 Electrode-Box-B2 13 135 154 1
16 Banh-mi-2 Electrode-Box-A2 0 25 97 0
17 Banh-mi-2 Electrode-Box-A2 1 22 101 0
18 Banh-mi-2 Electrode-Box-A2 2 22 119 0
19 Banh-mi-2 Electrode-Box-A2 3 29 118 0
20 Banh-mi-2 Electrode-Box-A2 4 27 136 0
21 Banh-mi-2 Electrode-Box-A2 5 43 146 0
22 Banh-mi-2 Electrode-Box-A2 6 74 146 0
23 Banh-mi-2 Electrode-Box-A2 7 70 148 0
24 Banh-mi-2 Electrode-Box-A2 8 65 130 0
25 Banh-mi-2 Electrode-Box-A2 9 79 151 0
26 Banh-mi-2 Electrode-Box-A2 10 93 152 0
27 Egg-tart-1 Electrode-Box-B2 0 7 56 1
28 Egg-tart-1 Electrode-Box-B2 1 16 78 1
29 Egg-tart-1 Electrode-Box-B2 2 23 103 1
30 Egg-tart-1 Electrode-Box-B2 3 63 120 1
31 Egg-tart-1 Electrode-Box-B2 4 69 132 1
32 Egg-tart-1 Electrode-Box-B2 5 83 136 1
33 Egg-tart-1 Electrode-Box-B2 6 71 142 1
34 Egg-tart-1 Electrode-Box-B2 7 79 138 1
35 Egg-tart-1 Electrode-Box-B2 8 98 142 1
36 Egg-tart-1 Electrode-Box-B2 9 89 139 1
37 Egg-tart-1 Electrode-Box-B2 10 96 143 1
38 Egg-tart-1 Electrode-Box-B2 11 96 148 1
39 Egg-tart-1 Electrode-Box-B2 12 101 156 1
40 Egg-tart-1 Electrode-Box-B2 13 103 152 1
41 Egg-tart-2 Electrode-Box-A2 0 9 32 0
42 Egg-tart-2 Electrode-Box-A2 1 2 38 0
43 Egg-tart-2 Electrode-Box-A2 2 31 93 0
44 Egg-tart-2 Electrode-Box-A2 3 44 101 0
45 Egg-tart-2 Electrode-Box-A2 4 54 131 0
46 Egg-tart-2 Electrode-Box-A2 5 84 139 0
47 Egg-tart-2 Electrode-Box-A2 6 85 145 0
48 Egg-tart-2 Electrode-Box-A2 7 79 143 0
49 Egg-tart-2 Electrode-Box-A2 8 76 131 0
50 Egg-tart-2 Electrode-Box-A2 9 88 149 0
51 Egg-tart-2 Electrode-Box-A2 10 81 151 0
52 Egg-tart-2 Electrode-Box-A2 11 78 152 0
53 Egg-tart-2 Electrode-Box-A2 12 96 155 0
54 Egg-tart-2 Electrode-Box-A2 13 84 155 0
55 Khoai-lang-2 Naive 0 0 0 0
56 Khoai-lang-2 Naive 1 0 0 0
57 Khoai-lang-2 Naive 2 10 47 0
58 Khoai-lang-2 Naive 3 11 52 0
59 Khoai-lang-2 Naive 4 9 56 0
60 Khoai-lang-2 Naive 5 34 95 0
61 Khoai-lang-2 Naive 6 21 72 0
62 Khoai-lang-2 Naive 7 23 99 0
63 Khoai-lang-2 Naive 8 64 136 0
64 Khoai-lang-2 Naive 9 75 131 0
65 Khoai-lang-2 Naive 10 63 134 0
66 Khoai-lang-2 Naive 11 59 139 0
67 Khoai-lang-2 Naive 12 51 129 0
68 Khoai-lang-2 Naive 13 73 143 0
69 Khoai-tay-1 Electrode-Box-A 0 14 62 0
70 Khoai-tay-1 Electrode-Box-A 1 22 83 0
71 Khoai-tay-1 Electrode-Box-A 2 6 79 0
72 Khoai-tay-1 Electrode-Box-A 3 28 97 0
73 Khoai-tay-1 Electrode-Box-A 4 60 134 0
74 Khoai-tay-1 Electrode-Box-A 5 75 138 0
75 Khoai-tay-1 Electrode-Box-A 6 81 137 0
76 Khoai-tay-1 Electrode-Box-A 7 78 147 0
77 Khoai-tay-1 Electrode-Box-A 8 91 132 0
78 Khoai-tay-1 Electrode-Box-A 9 99 146 0
79 Khoai-tay-1 Electrode-Box-A 10 94 143 0
80 Khoai-tay-1 Electrode-Box-A 11 110 156 0
81 Khoai-tay-1 Electrode-Box-A 12 105 143 0
82 Khoai-tay-1 Electrode-Box-A 13 106 153 0
83 Khoai-tay-2 Naive 1 21 68 0
84 Khoai-tay-2 Naive 2 6 79 0
85 Khoai-tay-2 Naive 3 0 83 0
86 Khoai-tay-2 Naive 4 28 87 0
87 Khoai-tay-2 Naive 5 31 125 0
88 Khoai-tay-2 Naive 6 62 144 0
89 Khoai-tay-2 Naive 7 87 141 0
90 Khoai-tay-2 Naive 8 105 152 0
91 Khoai-tay-2 Naive 9 75 148 0
92 Khoai-tay-2 Naive 10 101 149 0
93 Khoai-tay-2 Naive 11 102 154 0
94 Khoai-tay-2 Naive 12 95 144 0
95 Khoai-tay-2 Naive 13 77 149 0
96 OM-2 Naive 0 1 7 0
97 OM-2 Naive 1 11 33 0
98 OM-2 Naive 2 19 62 0
99 OM-2 Naive 3 29 117 0
100 OM-2 Naive 4 73 126 0
101 OM-2 Naive 5 53 135 0
102 OM-2 Naive 6 73 138 0
103 OM-2 Naive 7 80 131 0
104 OM-2 Naive 8 91 141 0
105 OM-2 Naive 9 90 135 0
106 OM-2 Naive 10 95 142 0
107 OM-2 Naive 11 60 133 0
108 OM-2 Naive 12 58 142 0
109 Root-beer-1 Electrode-Box-B2 0 11 85 1
110 Root-beer-1 Electrode-Box-B2 1 18 76 1
111 Root-beer-1 Electrode-Box-B2 2 40 105 1
112 Root-beer-1 Electrode-Box-B2 3 55 134 1
113 Root-beer-1 Electrode-Box-B2 4 75 136 1
114 Root-beer-1 Electrode-Box-B2 5 64 133 1
115 Root-beer-1 Electrode-Box-B2 6 104 139 1
116 Root-beer-1 Electrode-Box-B2 7 98 148 1
117 Root-beer-1 Electrode-Box-B2 8 81 145 1
118 Root-beer-1 Electrode-Box-B2 9 89 156 1
119 Root-beer-1 Electrode-Box-B2 10 105 158 1
120 Root-beer-2 Electrode-Box-A2 0 22 74 0
121 Root-beer-2 Electrode-Box-A2 1 31 87 0
122 Root-beer-2 Electrode-Box-A2 2 49 134 0
123 Root-beer-2 Electrode-Box-A2 3 31 89 0
124 Root-beer-2 Electrode-Box-A2 4 60 140 0
125 Root-beer-2 Electrode-Box-A2 5 84 147 0
126 Vu-vuong Naive 0 18 61 0
127 Vu-vuong Naive 1 35 91 0
128 Vu-vuong Naive 2 61 127 0
129 Vu-vuong Naive 3 34 146 0
130 Vu-vuong Naive 4 61 141 0
131 Vu-vuong Naive 5 37 151 0
132 Vu-vuong Naive 6 49 131 0
133 Vu-vuong Naive 7 65 149 0
134 Vu-vuong Naive 8 67 141 0
135 Vu-vuong Naive 9 73 140 0
136 Vu-vuong Naive 10 72 131 0
137 Vu-vuong Naive 11 89 158 0
138 Vu-vuong Naive 12 97 154 0
139 Vu-vuong Naive 13 93 145 0
analysis/matlab/variations/naive_boxa_d0_13/result.txt
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==============================================================================
VARIATION: naive_boxa_d0_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
N = 11 rats, 138 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 138
Fixed effects coefficients 4
Random effects coefficients 11
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
1172.3 1189.8 -580.14 1160.3
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 15.925 4.1997 3.792 134 0.00022515
{'day' } 6.7124 0.401 16.739 134 1.2129e-34
{'stim' } 11.848 7.9908 1.4827 134 0.1405
{'day:stim' } 1.3064 0.75238 1.7363 134 0.084806
Lower Upper
7.619 24.231
5.9193 7.5055
-3.9564 27.652
-0.1817 2.7945
Random effects covariance parameters (95% CIs):
Group: rat (11 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 8.76
Lower Upper
5.1941 14.774
Group: Error
Name Estimate Lower Upper
{'Res Std'} 15.18 13.424 17.167
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(134)= 1.74 F(1)= 3.015 p=0.08481
day (learning) t(134)= 16.74 F(1)= 280.201 p=1.213e-34
stim (main, window start) t(134)= 1.48 F(1)= 2.198 p=0.1405
interaction 95% CI: [-0.18, +2.79]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.08481, slope diff=+1.31)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_boxa_d0_5/analyze.m
+74
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_boxa_d0_5/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Khoai-lang-2,Naive,0,0,0,0
Khoai-lang-2,Naive,1,0,0,0
Khoai-lang-2,Naive,2,10,47,0
Khoai-lang-2,Naive,3,11,52,0
Khoai-lang-2,Naive,4,9,56,0
Khoai-lang-2,Naive,5,34,95,0
Khoai-tay-1,Electrode-Box-A,0,14,62,0
Khoai-tay-1,Electrode-Box-A,1,22,83,0
Khoai-tay-1,Electrode-Box-A,2,6,79,0
Khoai-tay-1,Electrode-Box-A,3,28,97,0
Khoai-tay-1,Electrode-Box-A,4,60,134,0
Khoai-tay-1,Electrode-Box-A,5,75,138,0
Khoai-tay-2,Naive,1,21,68,0
Khoai-tay-2,Naive,2,6,79,0
Khoai-tay-2,Naive,3,0,83,0
Khoai-tay-2,Naive,4,28,87,0
Khoai-tay-2,Naive,5,31,125,0
OM-2,Naive,0,1,7,0
OM-2,Naive,1,11,33,0
OM-2,Naive,2,19,62,0
OM-2,Naive,3,29,117,0
OM-2,Naive,4,73,126,0
OM-2,Naive,5,53,135,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
Vu-vuong,Naive,0,18,61,0
Vu-vuong,Naive,1,35,91,0
Vu-vuong,Naive,2,61,127,0
Vu-vuong,Naive,3,34,146,0
Vu-vuong,Naive,4,61,141,0
Vu-vuong,Naive,5,37,151,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-2 Electrode-Box-A2 0 25 97 0
9 Banh-mi-2 Electrode-Box-A2 1 22 101 0
10 Banh-mi-2 Electrode-Box-A2 2 22 119 0
11 Banh-mi-2 Electrode-Box-A2 3 29 118 0
12 Banh-mi-2 Electrode-Box-A2 4 27 136 0
13 Banh-mi-2 Electrode-Box-A2 5 43 146 0
14 Egg-tart-1 Electrode-Box-B2 0 7 56 1
15 Egg-tart-1 Electrode-Box-B2 1 16 78 1
16 Egg-tart-1 Electrode-Box-B2 2 23 103 1
17 Egg-tart-1 Electrode-Box-B2 3 63 120 1
18 Egg-tart-1 Electrode-Box-B2 4 69 132 1
19 Egg-tart-1 Electrode-Box-B2 5 83 136 1
20 Egg-tart-2 Electrode-Box-A2 0 9 32 0
21 Egg-tart-2 Electrode-Box-A2 1 2 38 0
22 Egg-tart-2 Electrode-Box-A2 2 31 93 0
23 Egg-tart-2 Electrode-Box-A2 3 44 101 0
24 Egg-tart-2 Electrode-Box-A2 4 54 131 0
25 Egg-tart-2 Electrode-Box-A2 5 84 139 0
26 Khoai-lang-2 Naive 0 0 0 0
27 Khoai-lang-2 Naive 1 0 0 0
28 Khoai-lang-2 Naive 2 10 47 0
29 Khoai-lang-2 Naive 3 11 52 0
30 Khoai-lang-2 Naive 4 9 56 0
31 Khoai-lang-2 Naive 5 34 95 0
32 Khoai-tay-1 Electrode-Box-A 0 14 62 0
33 Khoai-tay-1 Electrode-Box-A 1 22 83 0
34 Khoai-tay-1 Electrode-Box-A 2 6 79 0
35 Khoai-tay-1 Electrode-Box-A 3 28 97 0
36 Khoai-tay-1 Electrode-Box-A 4 60 134 0
37 Khoai-tay-1 Electrode-Box-A 5 75 138 0
38 Khoai-tay-2 Naive 1 21 68 0
39 Khoai-tay-2 Naive 2 6 79 0
40 Khoai-tay-2 Naive 3 0 83 0
41 Khoai-tay-2 Naive 4 28 87 0
42 Khoai-tay-2 Naive 5 31 125 0
43 OM-2 Naive 0 1 7 0
44 OM-2 Naive 1 11 33 0
45 OM-2 Naive 2 19 62 0
46 OM-2 Naive 3 29 117 0
47 OM-2 Naive 4 73 126 0
48 OM-2 Naive 5 53 135 0
49 Root-beer-1 Electrode-Box-B2 0 11 85 1
50 Root-beer-1 Electrode-Box-B2 1 18 76 1
51 Root-beer-1 Electrode-Box-B2 2 40 105 1
52 Root-beer-1 Electrode-Box-B2 3 55 134 1
53 Root-beer-1 Electrode-Box-B2 4 75 136 1
54 Root-beer-1 Electrode-Box-B2 5 64 133 1
55 Root-beer-2 Electrode-Box-A2 0 22 74 0
56 Root-beer-2 Electrode-Box-A2 1 31 87 0
57 Root-beer-2 Electrode-Box-A2 2 49 134 0
58 Root-beer-2 Electrode-Box-A2 3 31 89 0
59 Root-beer-2 Electrode-Box-A2 4 60 140 0
60 Root-beer-2 Electrode-Box-A2 5 84 147 0
61 Vu-vuong Naive 0 18 61 0
62 Vu-vuong Naive 1 35 91 0
63 Vu-vuong Naive 2 61 127 0
64 Vu-vuong Naive 3 34 146 0
65 Vu-vuong Naive 4 61 141 0
66 Vu-vuong Naive 5 37 151 0
analysis/matlab/variations/naive_boxa_d0_5/result.txt
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==============================================================================
VARIATION: naive_boxa_d0_5
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
N = 11 rats, 65 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 65
Fixed effects coefficients 4
Random effects coefficients 11
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
540.21 553.25 -264.1 528.21
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 8.2006 4.622 1.7743 61 0.081011
{'day' } 8.8157 1.0827 8.1421 61 2.5062e-11
{'stim' } 1.3073 8.7275 0.1498 61 0.88142
{'day:stim' } 6.4033 2.0341 3.1479 61 0.0025447
Lower Upper
-1.0416 17.443
6.6507 10.981
-16.144 18.759
2.3358 10.471
Random effects covariance parameters (95% CIs):
Group: rat (11 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 9.1032
Lower Upper
5.2068 15.915
Group: Error
Name Estimate Lower Upper
{'Res Std'} 12.477 10.33 15.071
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(61)= 3.15 F(1)= 9.909 p=0.002545
day (learning) t(61)= 8.14 F(1)= 66.293 p=2.506e-11
stim (main, window start) t(61)= 0.15 F(1)= 0.022 p=0.8814
interaction 95% CI: [+2.34, +10.47]
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.002545, slope diff=+6.40)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_boxa_d6_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_boxa_d6_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-lang-2,Naive,6,21,72,0
Khoai-lang-2,Naive,7,23,99,0
Khoai-lang-2,Naive,8,64,136,0
Khoai-lang-2,Naive,9,75,131,0
Khoai-lang-2,Naive,10,63,134,0
Khoai-tay-1,Electrode-Box-A,6,81,137,0
Khoai-tay-1,Electrode-Box-A,7,78,147,0
Khoai-tay-1,Electrode-Box-A,8,91,132,0
Khoai-tay-1,Electrode-Box-A,9,99,146,0
Khoai-tay-1,Electrode-Box-A,10,94,143,0
Khoai-tay-2,Naive,6,62,144,0
Khoai-tay-2,Naive,7,87,141,0
Khoai-tay-2,Naive,8,105,152,0
Khoai-tay-2,Naive,9,75,148,0
Khoai-tay-2,Naive,10,101,149,0
OM-2,Naive,6,73,138,0
OM-2,Naive,7,80,131,0
OM-2,Naive,8,91,141,0
OM-2,Naive,9,90,135,0
OM-2,Naive,10,95,142,0
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Vu-vuong,Naive,6,49,131,0
Vu-vuong,Naive,7,65,149,0
Vu-vuong,Naive,8,67,141,0
Vu-vuong,Naive,9,73,140,0
Vu-vuong,Naive,10,72,131,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-2 Electrode-Box-A2 6 74 146 0
8 Banh-mi-2 Electrode-Box-A2 7 70 148 0
9 Banh-mi-2 Electrode-Box-A2 8 65 130 0
10 Banh-mi-2 Electrode-Box-A2 9 79 151 0
11 Banh-mi-2 Electrode-Box-A2 10 93 152 0
12 Egg-tart-1 Electrode-Box-B2 6 71 142 1
13 Egg-tart-1 Electrode-Box-B2 7 79 138 1
14 Egg-tart-1 Electrode-Box-B2 8 98 142 1
15 Egg-tart-1 Electrode-Box-B2 9 89 139 1
16 Egg-tart-1 Electrode-Box-B2 10 96 143 1
17 Egg-tart-2 Electrode-Box-A2 6 85 145 0
18 Egg-tart-2 Electrode-Box-A2 7 79 143 0
19 Egg-tart-2 Electrode-Box-A2 8 76 131 0
20 Egg-tart-2 Electrode-Box-A2 9 88 149 0
21 Egg-tart-2 Electrode-Box-A2 10 81 151 0
22 Khoai-lang-2 Naive 6 21 72 0
23 Khoai-lang-2 Naive 7 23 99 0
24 Khoai-lang-2 Naive 8 64 136 0
25 Khoai-lang-2 Naive 9 75 131 0
26 Khoai-lang-2 Naive 10 63 134 0
27 Khoai-tay-1 Electrode-Box-A 6 81 137 0
28 Khoai-tay-1 Electrode-Box-A 7 78 147 0
29 Khoai-tay-1 Electrode-Box-A 8 91 132 0
30 Khoai-tay-1 Electrode-Box-A 9 99 146 0
31 Khoai-tay-1 Electrode-Box-A 10 94 143 0
32 Khoai-tay-2 Naive 6 62 144 0
33 Khoai-tay-2 Naive 7 87 141 0
34 Khoai-tay-2 Naive 8 105 152 0
35 Khoai-tay-2 Naive 9 75 148 0
36 Khoai-tay-2 Naive 10 101 149 0
37 OM-2 Naive 6 73 138 0
38 OM-2 Naive 7 80 131 0
39 OM-2 Naive 8 91 141 0
40 OM-2 Naive 9 90 135 0
41 OM-2 Naive 10 95 142 0
42 Root-beer-1 Electrode-Box-B2 6 104 139 1
43 Root-beer-1 Electrode-Box-B2 7 98 148 1
44 Root-beer-1 Electrode-Box-B2 8 81 145 1
45 Root-beer-1 Electrode-Box-B2 9 89 156 1
46 Root-beer-1 Electrode-Box-B2 10 105 158 1
47 Vu-vuong Naive 6 49 131 0
48 Vu-vuong Naive 7 65 149 0
49 Vu-vuong Naive 8 67 141 0
50 Vu-vuong Naive 9 73 140 0
51 Vu-vuong Naive 10 72 131 0
analysis/matlab/variations/naive_boxa_d6_10/result.txt
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==============================================================================
VARIATION: naive_boxa_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
N = 10 rats, 50 sessions raw day coverage: treat 6..10, control 6..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 50
Fixed effects coefficients 4
Random effects coefficients 10
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
405.22 416.69 -196.61 393.22
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 64.543 5.1823 12.454 46 2.4341e-16
{'day' } 5.7857 1.2123 4.7727 46 1.8769e-05
{'stim' } 23.99 9.4616 2.5356 46 0.014689
{'day:stim' } -1.619 2.2133 -0.73152 46 0.46817
Lower Upper
54.111 74.974
3.3456 8.2259
4.9452 43.036
-6.0741 2.836
Random effects covariance parameters (95% CIs):
Group: rat (10 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 11.237
Lower Upper
6.7417 18.73
Group: Error
Name Estimate Lower Upper
{'Res Std'} 10.142 8.1466 12.627
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(46)= -0.73 F(1)= 0.535 p=0.4682
day (learning) t(46)= 4.77 F(1)= 22.779 p=1.877e-05
stim (main, window start) t(46)= 2.54 F(1)= 6.429 p=0.01469
interaction 95% CI: [-6.07, +2.84]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.4682, slope diff=-1.62)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/naive_boxa_d6_13/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/naive_boxa_d6_13/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-1,Electrode-Box-B2,11,121,148,1
Banh-mi-1,Electrode-Box-B2,12,120,149,1
Banh-mi-1,Electrode-Box-B2,13,135,154,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-1,Electrode-Box-B2,11,96,148,1
Egg-tart-1,Electrode-Box-B2,12,101,156,1
Egg-tart-1,Electrode-Box-B2,13,103,152,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Egg-tart-2,Electrode-Box-A2,11,78,152,0
Egg-tart-2,Electrode-Box-A2,12,96,155,0
Egg-tart-2,Electrode-Box-A2,13,84,155,0
Khoai-lang-2,Naive,6,21,72,0
Khoai-lang-2,Naive,7,23,99,0
Khoai-lang-2,Naive,8,64,136,0
Khoai-lang-2,Naive,9,75,131,0
Khoai-lang-2,Naive,10,63,134,0
Khoai-lang-2,Naive,11,59,139,0
Khoai-lang-2,Naive,12,51,129,0
Khoai-lang-2,Naive,13,73,143,0
Khoai-tay-1,Electrode-Box-A,6,81,137,0
Khoai-tay-1,Electrode-Box-A,7,78,147,0
Khoai-tay-1,Electrode-Box-A,8,91,132,0
Khoai-tay-1,Electrode-Box-A,9,99,146,0
Khoai-tay-1,Electrode-Box-A,10,94,143,0
Khoai-tay-1,Electrode-Box-A,11,110,156,0
Khoai-tay-1,Electrode-Box-A,12,105,143,0
Khoai-tay-1,Electrode-Box-A,13,106,153,0
Khoai-tay-2,Naive,6,62,144,0
Khoai-tay-2,Naive,7,87,141,0
Khoai-tay-2,Naive,8,105,152,0
Khoai-tay-2,Naive,9,75,148,0
Khoai-tay-2,Naive,10,101,149,0
Khoai-tay-2,Naive,11,102,154,0
Khoai-tay-2,Naive,12,95,144,0
Khoai-tay-2,Naive,13,77,149,0
OM-2,Naive,6,73,138,0
OM-2,Naive,7,80,131,0
OM-2,Naive,8,91,141,0
OM-2,Naive,9,90,135,0
OM-2,Naive,10,95,142,0
OM-2,Naive,11,60,133,0
OM-2,Naive,12,58,142,0
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Vu-vuong,Naive,6,49,131,0
Vu-vuong,Naive,7,65,149,0
Vu-vuong,Naive,8,67,141,0
Vu-vuong,Naive,9,73,140,0
Vu-vuong,Naive,10,72,131,0
Vu-vuong,Naive,11,89,158,0
Vu-vuong,Naive,12,97,154,0
Vu-vuong,Naive,13,93,145,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-1 Electrode-Box-B2 11 121 148 1
8 Banh-mi-1 Electrode-Box-B2 12 120 149 1
9 Banh-mi-1 Electrode-Box-B2 13 135 154 1
10 Banh-mi-2 Electrode-Box-A2 6 74 146 0
11 Banh-mi-2 Electrode-Box-A2 7 70 148 0
12 Banh-mi-2 Electrode-Box-A2 8 65 130 0
13 Banh-mi-2 Electrode-Box-A2 9 79 151 0
14 Banh-mi-2 Electrode-Box-A2 10 93 152 0
15 Egg-tart-1 Electrode-Box-B2 6 71 142 1
16 Egg-tart-1 Electrode-Box-B2 7 79 138 1
17 Egg-tart-1 Electrode-Box-B2 8 98 142 1
18 Egg-tart-1 Electrode-Box-B2 9 89 139 1
19 Egg-tart-1 Electrode-Box-B2 10 96 143 1
20 Egg-tart-1 Electrode-Box-B2 11 96 148 1
21 Egg-tart-1 Electrode-Box-B2 12 101 156 1
22 Egg-tart-1 Electrode-Box-B2 13 103 152 1
23 Egg-tart-2 Electrode-Box-A2 6 85 145 0
24 Egg-tart-2 Electrode-Box-A2 7 79 143 0
25 Egg-tart-2 Electrode-Box-A2 8 76 131 0
26 Egg-tart-2 Electrode-Box-A2 9 88 149 0
27 Egg-tart-2 Electrode-Box-A2 10 81 151 0
28 Egg-tart-2 Electrode-Box-A2 11 78 152 0
29 Egg-tart-2 Electrode-Box-A2 12 96 155 0
30 Egg-tart-2 Electrode-Box-A2 13 84 155 0
31 Khoai-lang-2 Naive 6 21 72 0
32 Khoai-lang-2 Naive 7 23 99 0
33 Khoai-lang-2 Naive 8 64 136 0
34 Khoai-lang-2 Naive 9 75 131 0
35 Khoai-lang-2 Naive 10 63 134 0
36 Khoai-lang-2 Naive 11 59 139 0
37 Khoai-lang-2 Naive 12 51 129 0
38 Khoai-lang-2 Naive 13 73 143 0
39 Khoai-tay-1 Electrode-Box-A 6 81 137 0
40 Khoai-tay-1 Electrode-Box-A 7 78 147 0
41 Khoai-tay-1 Electrode-Box-A 8 91 132 0
42 Khoai-tay-1 Electrode-Box-A 9 99 146 0
43 Khoai-tay-1 Electrode-Box-A 10 94 143 0
44 Khoai-tay-1 Electrode-Box-A 11 110 156 0
45 Khoai-tay-1 Electrode-Box-A 12 105 143 0
46 Khoai-tay-1 Electrode-Box-A 13 106 153 0
47 Khoai-tay-2 Naive 6 62 144 0
48 Khoai-tay-2 Naive 7 87 141 0
49 Khoai-tay-2 Naive 8 105 152 0
50 Khoai-tay-2 Naive 9 75 148 0
51 Khoai-tay-2 Naive 10 101 149 0
52 Khoai-tay-2 Naive 11 102 154 0
53 Khoai-tay-2 Naive 12 95 144 0
54 Khoai-tay-2 Naive 13 77 149 0
55 OM-2 Naive 6 73 138 0
56 OM-2 Naive 7 80 131 0
57 OM-2 Naive 8 91 141 0
58 OM-2 Naive 9 90 135 0
59 OM-2 Naive 10 95 142 0
60 OM-2 Naive 11 60 133 0
61 OM-2 Naive 12 58 142 0
62 Root-beer-1 Electrode-Box-B2 6 104 139 1
63 Root-beer-1 Electrode-Box-B2 7 98 148 1
64 Root-beer-1 Electrode-Box-B2 8 81 145 1
65 Root-beer-1 Electrode-Box-B2 9 89 156 1
66 Root-beer-1 Electrode-Box-B2 10 105 158 1
67 Vu-vuong Naive 6 49 131 0
68 Vu-vuong Naive 7 65 149 0
69 Vu-vuong Naive 8 67 141 0
70 Vu-vuong Naive 9 73 140 0
71 Vu-vuong Naive 10 72 131 0
72 Vu-vuong Naive 11 89 158 0
73 Vu-vuong Naive 12 97 154 0
74 Vu-vuong Naive 13 93 145 0
analysis/matlab/variations/naive_boxa_d6_13/result.txt
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==============================================================================
VARIATION: naive_boxa_d6_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A, Electrode-Box-A2, Naive
N = 10 rats, 73 sessions raw day coverage: treat 6..13, control 6..13
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 73
Fixed effects coefficients 4
Random effects coefficients 10
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
595.91 609.65 -291.95 583.91
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 68.651 4.9672 13.821 69 1.4737e-21
{'day' } 3.1029 0.72917 4.2555 69 6.4463e-05
{'stim' } 20.083 9.0854 2.2105 69 0.030392
{'day:stim' } 0.87369 1.3869 0.62995 69 0.53081
Lower Upper
58.741 78.56
1.6483 4.5576
1.958 38.208
-1.8931 3.6405
Random effects covariance parameters (95% CIs):
Group: rat (10 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 10.755
Lower Upper
6.51 17.768
Group: Error
Name Estimate Lower Upper
{'Res Std'} 11.525 9.6827 13.718
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(69)= 0.63 F(1)= 0.397 p=0.5308
day (learning) t(69)= 4.26 F(1)= 18.109 p=6.446e-05
stim (main, window start) t(69)= 2.21 F(1)= 4.886 p=0.03039
interaction 95% CI: [-1.89, +3.64]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.5308, slope diff=+0.87)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/right_only_d0_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/right_only_d0_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-lang-1,Right-Electrode,0,3,69,1
Khoai-lang-1,Right-Electrode,1,18,97,1
Khoai-lang-1,Right-Electrode,2,27,84,1
Khoai-lang-1,Right-Electrode,3,46,121,1
Khoai-lang-1,Right-Electrode,4,65,143,1
Khoai-lang-1,Right-Electrode,5,76,141,1
Khoai-lang-1,Right-Electrode,6,79,146,1
Khoai-lang-1,Right-Electrode,7,81,153,1
Khoai-lang-1,Right-Electrode,8,98,144,1
Khoai-lang-1,Right-Electrode,9,101,149,1
Khoai-lang-1,Right-Electrode,10,103,150,1
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-2 Electrode-Box-A2 0 25 97 0
14 Banh-mi-2 Electrode-Box-A2 1 22 101 0
15 Banh-mi-2 Electrode-Box-A2 2 22 119 0
16 Banh-mi-2 Electrode-Box-A2 3 29 118 0
17 Banh-mi-2 Electrode-Box-A2 4 27 136 0
18 Banh-mi-2 Electrode-Box-A2 5 43 146 0
19 Banh-mi-2 Electrode-Box-A2 6 74 146 0
20 Banh-mi-2 Electrode-Box-A2 7 70 148 0
21 Banh-mi-2 Electrode-Box-A2 8 65 130 0
22 Banh-mi-2 Electrode-Box-A2 9 79 151 0
23 Banh-mi-2 Electrode-Box-A2 10 93 152 0
24 Egg-tart-1 Electrode-Box-B2 0 7 56 1
25 Egg-tart-1 Electrode-Box-B2 1 16 78 1
26 Egg-tart-1 Electrode-Box-B2 2 23 103 1
27 Egg-tart-1 Electrode-Box-B2 3 63 120 1
28 Egg-tart-1 Electrode-Box-B2 4 69 132 1
29 Egg-tart-1 Electrode-Box-B2 5 83 136 1
30 Egg-tart-1 Electrode-Box-B2 6 71 142 1
31 Egg-tart-1 Electrode-Box-B2 7 79 138 1
32 Egg-tart-1 Electrode-Box-B2 8 98 142 1
33 Egg-tart-1 Electrode-Box-B2 9 89 139 1
34 Egg-tart-1 Electrode-Box-B2 10 96 143 1
35 Egg-tart-2 Electrode-Box-A2 0 9 32 0
36 Egg-tart-2 Electrode-Box-A2 1 2 38 0
37 Egg-tart-2 Electrode-Box-A2 2 31 93 0
38 Egg-tart-2 Electrode-Box-A2 3 44 101 0
39 Egg-tart-2 Electrode-Box-A2 4 54 131 0
40 Egg-tart-2 Electrode-Box-A2 5 84 139 0
41 Egg-tart-2 Electrode-Box-A2 6 85 145 0
42 Egg-tart-2 Electrode-Box-A2 7 79 143 0
43 Egg-tart-2 Electrode-Box-A2 8 76 131 0
44 Egg-tart-2 Electrode-Box-A2 9 88 149 0
45 Egg-tart-2 Electrode-Box-A2 10 81 151 0
46 Khoai-lang-1 Right-Electrode 0 3 69 1
47 Khoai-lang-1 Right-Electrode 1 18 97 1
48 Khoai-lang-1 Right-Electrode 2 27 84 1
49 Khoai-lang-1 Right-Electrode 3 46 121 1
50 Khoai-lang-1 Right-Electrode 4 65 143 1
51 Khoai-lang-1 Right-Electrode 5 76 141 1
52 Khoai-lang-1 Right-Electrode 6 79 146 1
53 Khoai-lang-1 Right-Electrode 7 81 153 1
54 Khoai-lang-1 Right-Electrode 8 98 144 1
55 Khoai-lang-1 Right-Electrode 9 101 149 1
56 Khoai-lang-1 Right-Electrode 10 103 150 1
57 Root-beer-1 Electrode-Box-B2 0 11 85 1
58 Root-beer-1 Electrode-Box-B2 1 18 76 1
59 Root-beer-1 Electrode-Box-B2 2 40 105 1
60 Root-beer-1 Electrode-Box-B2 3 55 134 1
61 Root-beer-1 Electrode-Box-B2 4 75 136 1
62 Root-beer-1 Electrode-Box-B2 5 64 133 1
63 Root-beer-1 Electrode-Box-B2 6 104 139 1
64 Root-beer-1 Electrode-Box-B2 7 98 148 1
65 Root-beer-1 Electrode-Box-B2 8 81 145 1
66 Root-beer-1 Electrode-Box-B2 9 89 156 1
67 Root-beer-1 Electrode-Box-B2 10 105 158 1
68 Root-beer-2 Electrode-Box-A2 0 22 74 0
69 Root-beer-2 Electrode-Box-A2 1 31 87 0
70 Root-beer-2 Electrode-Box-A2 2 49 134 0
71 Root-beer-2 Electrode-Box-A2 3 31 89 0
72 Root-beer-2 Electrode-Box-A2 4 60 140 0
73 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/right_only_d0_10/result.txt
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==============================================================================
VARIATION: right_only_d0_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2, Right-Electrode
control (stim=0): Electrode-Box-A2
N = 7 rats, 72 sessions raw day coverage: treat 0..10, control 0..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 72
Fixed effects coefficients 4
Random effects coefficients 7
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
581.39 595.05 -284.69 569.39
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 17.35 5.0416 3.4414 68 0.00099395
{'day' } 7.979 0.75658 10.546 68 5.9429e-16
{'stim' } 2.4226 6.6282 0.36549 68 0.71588
{'day:stim' } 1.73 0.94724 1.8264 68 0.07218
Lower Upper
7.2897 27.411
6.4693 9.4888
-10.804 15.649
-0.16015 3.6202
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 5.3461
Lower Upper
2.377 12.024
Group: Error
Name Estimate Lower Upper
{'Res Std'} 11.956 10.063 14.205
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(68)= 1.83 F(1)= 3.336 p=0.07218
day (learning) t(68)= 10.55 F(1)= 111.222 p=5.943e-16
stim (main, window start) t(68)= 0.37 F(1)= 0.134 p=0.7159
interaction 95% CI: [-0.16, +3.62]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.07218, slope diff=+1.73)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/right_only_d0_13/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/right_only_d0_13/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-1,Electrode-Box-B2,11,121,148,1
Banh-mi-1,Electrode-Box-B2,12,120,149,1
Banh-mi-1,Electrode-Box-B2,13,135,154,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-1,Electrode-Box-B2,11,96,148,1
Egg-tart-1,Electrode-Box-B2,12,101,156,1
Egg-tart-1,Electrode-Box-B2,13,103,152,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Egg-tart-2,Electrode-Box-A2,11,78,152,0
Egg-tart-2,Electrode-Box-A2,12,96,155,0
Egg-tart-2,Electrode-Box-A2,13,84,155,0
Khoai-lang-1,Right-Electrode,0,3,69,1
Khoai-lang-1,Right-Electrode,1,18,97,1
Khoai-lang-1,Right-Electrode,2,27,84,1
Khoai-lang-1,Right-Electrode,3,46,121,1
Khoai-lang-1,Right-Electrode,4,65,143,1
Khoai-lang-1,Right-Electrode,5,76,141,1
Khoai-lang-1,Right-Electrode,6,79,146,1
Khoai-lang-1,Right-Electrode,7,81,153,1
Khoai-lang-1,Right-Electrode,8,98,144,1
Khoai-lang-1,Right-Electrode,9,101,149,1
Khoai-lang-1,Right-Electrode,10,103,150,1
Khoai-lang-1,Right-Electrode,11,113,154,1
Khoai-lang-1,Right-Electrode,12,117,148,1
Khoai-lang-1,Right-Electrode,13,114,146,1
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-1 Electrode-Box-B2 11 121 148 1
14 Banh-mi-1 Electrode-Box-B2 12 120 149 1
15 Banh-mi-1 Electrode-Box-B2 13 135 154 1
16 Banh-mi-2 Electrode-Box-A2 0 25 97 0
17 Banh-mi-2 Electrode-Box-A2 1 22 101 0
18 Banh-mi-2 Electrode-Box-A2 2 22 119 0
19 Banh-mi-2 Electrode-Box-A2 3 29 118 0
20 Banh-mi-2 Electrode-Box-A2 4 27 136 0
21 Banh-mi-2 Electrode-Box-A2 5 43 146 0
22 Banh-mi-2 Electrode-Box-A2 6 74 146 0
23 Banh-mi-2 Electrode-Box-A2 7 70 148 0
24 Banh-mi-2 Electrode-Box-A2 8 65 130 0
25 Banh-mi-2 Electrode-Box-A2 9 79 151 0
26 Banh-mi-2 Electrode-Box-A2 10 93 152 0
27 Egg-tart-1 Electrode-Box-B2 0 7 56 1
28 Egg-tart-1 Electrode-Box-B2 1 16 78 1
29 Egg-tart-1 Electrode-Box-B2 2 23 103 1
30 Egg-tart-1 Electrode-Box-B2 3 63 120 1
31 Egg-tart-1 Electrode-Box-B2 4 69 132 1
32 Egg-tart-1 Electrode-Box-B2 5 83 136 1
33 Egg-tart-1 Electrode-Box-B2 6 71 142 1
34 Egg-tart-1 Electrode-Box-B2 7 79 138 1
35 Egg-tart-1 Electrode-Box-B2 8 98 142 1
36 Egg-tart-1 Electrode-Box-B2 9 89 139 1
37 Egg-tart-1 Electrode-Box-B2 10 96 143 1
38 Egg-tart-1 Electrode-Box-B2 11 96 148 1
39 Egg-tart-1 Electrode-Box-B2 12 101 156 1
40 Egg-tart-1 Electrode-Box-B2 13 103 152 1
41 Egg-tart-2 Electrode-Box-A2 0 9 32 0
42 Egg-tart-2 Electrode-Box-A2 1 2 38 0
43 Egg-tart-2 Electrode-Box-A2 2 31 93 0
44 Egg-tart-2 Electrode-Box-A2 3 44 101 0
45 Egg-tart-2 Electrode-Box-A2 4 54 131 0
46 Egg-tart-2 Electrode-Box-A2 5 84 139 0
47 Egg-tart-2 Electrode-Box-A2 6 85 145 0
48 Egg-tart-2 Electrode-Box-A2 7 79 143 0
49 Egg-tart-2 Electrode-Box-A2 8 76 131 0
50 Egg-tart-2 Electrode-Box-A2 9 88 149 0
51 Egg-tart-2 Electrode-Box-A2 10 81 151 0
52 Egg-tart-2 Electrode-Box-A2 11 78 152 0
53 Egg-tart-2 Electrode-Box-A2 12 96 155 0
54 Egg-tart-2 Electrode-Box-A2 13 84 155 0
55 Khoai-lang-1 Right-Electrode 0 3 69 1
56 Khoai-lang-1 Right-Electrode 1 18 97 1
57 Khoai-lang-1 Right-Electrode 2 27 84 1
58 Khoai-lang-1 Right-Electrode 3 46 121 1
59 Khoai-lang-1 Right-Electrode 4 65 143 1
60 Khoai-lang-1 Right-Electrode 5 76 141 1
61 Khoai-lang-1 Right-Electrode 6 79 146 1
62 Khoai-lang-1 Right-Electrode 7 81 153 1
63 Khoai-lang-1 Right-Electrode 8 98 144 1
64 Khoai-lang-1 Right-Electrode 9 101 149 1
65 Khoai-lang-1 Right-Electrode 10 103 150 1
66 Khoai-lang-1 Right-Electrode 11 113 154 1
67 Khoai-lang-1 Right-Electrode 12 117 148 1
68 Khoai-lang-1 Right-Electrode 13 114 146 1
69 Root-beer-1 Electrode-Box-B2 0 11 85 1
70 Root-beer-1 Electrode-Box-B2 1 18 76 1
71 Root-beer-1 Electrode-Box-B2 2 40 105 1
72 Root-beer-1 Electrode-Box-B2 3 55 134 1
73 Root-beer-1 Electrode-Box-B2 4 75 136 1
74 Root-beer-1 Electrode-Box-B2 5 64 133 1
75 Root-beer-1 Electrode-Box-B2 6 104 139 1
76 Root-beer-1 Electrode-Box-B2 7 98 148 1
77 Root-beer-1 Electrode-Box-B2 8 81 145 1
78 Root-beer-1 Electrode-Box-B2 9 89 156 1
79 Root-beer-1 Electrode-Box-B2 10 105 158 1
80 Root-beer-2 Electrode-Box-A2 0 22 74 0
81 Root-beer-2 Electrode-Box-A2 1 31 87 0
82 Root-beer-2 Electrode-Box-A2 2 49 134 0
83 Root-beer-2 Electrode-Box-A2 3 31 89 0
84 Root-beer-2 Electrode-Box-A2 4 60 140 0
85 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/right_only_d0_13/result.txt
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==============================================================================
VARIATION: right_only_d0_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2, Right-Electrode
control (stim=0): Electrode-Box-A2
N = 7 rats, 84 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 84
Fixed effects coefficients 4
Random effects coefficients 7
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
695.18 709.77 -341.59 683.18
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 21.7 4.3761 4.9587 80 3.9065e-06
{'day' } 6.4863 0.68669 9.4459 80 1.1708e-14
{'stim' } 3.5986 5.6847 0.63303 80 0.52852
{'day:stim' } 1.7002 0.84679 2.0078 80 0.048042
Lower Upper
12.991 30.409
5.1198 7.8529
-7.7143 14.911
0.015009 3.3853
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 3.1353e-15
Lower Upper
NaN NaN
Group: Error
Name Estimate Lower Upper
{'Res Std'} 14.12 12.139 16.425
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(80)= 2.01 F(1)= 4.031 p=0.04804
day (learning) t(80)= 9.45 F(1)= 89.224 p=1.171e-14
stim (main, window start) t(80)= 0.63 F(1)= 0.401 p=0.5285
interaction 95% CI: [+0.02, +3.39]
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.04804, slope diff=+1.70)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/right_only_d0_5/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/right_only_d0_5/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Khoai-lang-1,Right-Electrode,0,3,69,1
Khoai-lang-1,Right-Electrode,1,18,97,1
Khoai-lang-1,Right-Electrode,2,27,84,1
Khoai-lang-1,Right-Electrode,3,46,121,1
Khoai-lang-1,Right-Electrode,4,65,143,1
Khoai-lang-1,Right-Electrode,5,76,141,1
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-2 Electrode-Box-A2 0 25 97 0
9 Banh-mi-2 Electrode-Box-A2 1 22 101 0
10 Banh-mi-2 Electrode-Box-A2 2 22 119 0
11 Banh-mi-2 Electrode-Box-A2 3 29 118 0
12 Banh-mi-2 Electrode-Box-A2 4 27 136 0
13 Banh-mi-2 Electrode-Box-A2 5 43 146 0
14 Egg-tart-1 Electrode-Box-B2 0 7 56 1
15 Egg-tart-1 Electrode-Box-B2 1 16 78 1
16 Egg-tart-1 Electrode-Box-B2 2 23 103 1
17 Egg-tart-1 Electrode-Box-B2 3 63 120 1
18 Egg-tart-1 Electrode-Box-B2 4 69 132 1
19 Egg-tart-1 Electrode-Box-B2 5 83 136 1
20 Egg-tart-2 Electrode-Box-A2 0 9 32 0
21 Egg-tart-2 Electrode-Box-A2 1 2 38 0
22 Egg-tart-2 Electrode-Box-A2 2 31 93 0
23 Egg-tart-2 Electrode-Box-A2 3 44 101 0
24 Egg-tart-2 Electrode-Box-A2 4 54 131 0
25 Egg-tart-2 Electrode-Box-A2 5 84 139 0
26 Khoai-lang-1 Right-Electrode 0 3 69 1
27 Khoai-lang-1 Right-Electrode 1 18 97 1
28 Khoai-lang-1 Right-Electrode 2 27 84 1
29 Khoai-lang-1 Right-Electrode 3 46 121 1
30 Khoai-lang-1 Right-Electrode 4 65 143 1
31 Khoai-lang-1 Right-Electrode 5 76 141 1
32 Root-beer-1 Electrode-Box-B2 0 11 85 1
33 Root-beer-1 Electrode-Box-B2 1 18 76 1
34 Root-beer-1 Electrode-Box-B2 2 40 105 1
35 Root-beer-1 Electrode-Box-B2 3 55 134 1
36 Root-beer-1 Electrode-Box-B2 4 75 136 1
37 Root-beer-1 Electrode-Box-B2 5 64 133 1
38 Root-beer-2 Electrode-Box-A2 0 22 74 0
39 Root-beer-2 Electrode-Box-A2 1 31 87 0
40 Root-beer-2 Electrode-Box-A2 2 49 134 0
41 Root-beer-2 Electrode-Box-A2 3 31 89 0
42 Root-beer-2 Electrode-Box-A2 4 60 140 0
43 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/right_only_d0_5/result.txt
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==============================================================================
VARIATION: right_only_d0_5
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2, Right-Electrode
control (stim=0): Electrode-Box-A2
N = 7 rats, 42 sessions raw day coverage: treat 0..5, control 0..5
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 42
Fixed effects coefficients 4
Random effects coefficients 7
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
331.16 341.59 -159.58 319.16
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 12.524 5.1493 2.4321 38 0.019831
{'day' } 9.8571 1.3751 7.1681 38 1.4583e-08
{'stim' } -4.9762 6.8119 -0.73051 38 0.46956
{'day:stim' } 5.3071 1.8191 2.9174 38 0.0058973
Lower Upper
2.0996 22.948
7.0733 12.641
-18.766 8.8138
1.6245 8.9898
Random effects covariance parameters (95% CIs):
Group: rat (7 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 5.2482
Lower Upper
2.2427 12.282
Group: Error
Name Estimate Lower Upper
{'Res Std'} 9.9638 7.8829 12.594
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(38)= 2.92 F(1)= 8.511 p=0.005897
day (learning) t(38)= 7.17 F(1)= 51.382 p=1.458e-08
stim (main, window start) t(38)= -0.73 F(1)= 0.534 p=0.4696
interaction 95% CI: [+1.62, +8.99]
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.005897, slope diff=+5.31)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/right_only_d6_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/right_only_d6_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Khoai-lang-1,Right-Electrode,6,79,146,1
Khoai-lang-1,Right-Electrode,7,81,153,1
Khoai-lang-1,Right-Electrode,8,98,144,1
Khoai-lang-1,Right-Electrode,9,101,149,1
Khoai-lang-1,Right-Electrode,10,103,150,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-2 Electrode-Box-A2 6 74 146 0
8 Banh-mi-2 Electrode-Box-A2 7 70 148 0
9 Banh-mi-2 Electrode-Box-A2 8 65 130 0
10 Banh-mi-2 Electrode-Box-A2 9 79 151 0
11 Banh-mi-2 Electrode-Box-A2 10 93 152 0
12 Egg-tart-1 Electrode-Box-B2 6 71 142 1
13 Egg-tart-1 Electrode-Box-B2 7 79 138 1
14 Egg-tart-1 Electrode-Box-B2 8 98 142 1
15 Egg-tart-1 Electrode-Box-B2 9 89 139 1
16 Egg-tart-1 Electrode-Box-B2 10 96 143 1
17 Egg-tart-2 Electrode-Box-A2 6 85 145 0
18 Egg-tart-2 Electrode-Box-A2 7 79 143 0
19 Egg-tart-2 Electrode-Box-A2 8 76 131 0
20 Egg-tart-2 Electrode-Box-A2 9 88 149 0
21 Egg-tart-2 Electrode-Box-A2 10 81 151 0
22 Khoai-lang-1 Right-Electrode 6 79 146 1
23 Khoai-lang-1 Right-Electrode 7 81 153 1
24 Khoai-lang-1 Right-Electrode 8 98 144 1
25 Khoai-lang-1 Right-Electrode 9 101 149 1
26 Khoai-lang-1 Right-Electrode 10 103 150 1
27 Root-beer-1 Electrode-Box-B2 6 104 139 1
28 Root-beer-1 Electrode-Box-B2 7 98 148 1
29 Root-beer-1 Electrode-Box-B2 8 81 145 1
30 Root-beer-1 Electrode-Box-B2 9 89 156 1
31 Root-beer-1 Electrode-Box-B2 10 105 158 1
analysis/matlab/variations/right_only_d6_10/result.txt
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==============================================================================
VARIATION: right_only_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2, Right-Electrode
control (stim=0): Electrode-Box-A2
N = 6 rats, 30 sessions raw day coverage: treat 6..10, control 6..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 30
Fixed effects coefficients 4
Random effects coefficients 6
Covariance parameters 2
Formula:
behavior ~ 1 + day*stim + (1 | rat)
Model fit statistics:
AIC BIC LogLikelihood Deviance
230.64 239.05 -109.32 218.64
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 74.2 6.0309 12.303 26 2.4061e-12
{'day' } 2.4 1.8291 1.3121 26 0.20096
{'stim' } 11.9 7.3864 1.6111 26 0.11924
{'day:stim' } 2.425 2.2402 1.0825 26 0.28898
Lower Upper
61.803 86.597
-1.3599 6.1599
-3.2829 27.083
-2.1799 7.0299
Random effects covariance parameters (95% CIs):
Group: rat (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 5.7092
Lower Upper
2.5487 12.789
Group: Error
Name Estimate Lower Upper
{'Res Std'} 8.1802 6.1646 10.855
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(26)= 1.08 F(1)= 1.172 p=0.289
day (learning) t(26)= 1.31 F(1)= 1.722 p=0.201
stim (main, window start) t(26)= 1.61 F(1)= 2.596 p=0.1192
interaction 95% CI: [-2.18, +7.03]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.289, slope diff=+2.42)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/right_only_d6_13/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/right_only_d6_13/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-1,Electrode-Box-B2,11,121,148,1
Banh-mi-1,Electrode-Box-B2,12,120,149,1
Banh-mi-1,Electrode-Box-B2,13,135,154,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-1,Electrode-Box-B2,11,96,148,1
Egg-tart-1,Electrode-Box-B2,12,101,156,1
Egg-tart-1,Electrode-Box-B2,13,103,152,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Egg-tart-2,Electrode-Box-A2,11,78,152,0
Egg-tart-2,Electrode-Box-A2,12,96,155,0
Egg-tart-2,Electrode-Box-A2,13,84,155,0
Khoai-lang-1,Right-Electrode,6,79,146,1
Khoai-lang-1,Right-Electrode,7,81,153,1
Khoai-lang-1,Right-Electrode,8,98,144,1
Khoai-lang-1,Right-Electrode,9,101,149,1
Khoai-lang-1,Right-Electrode,10,103,150,1
Khoai-lang-1,Right-Electrode,11,113,154,1
Khoai-lang-1,Right-Electrode,12,117,148,1
Khoai-lang-1,Right-Electrode,13,114,146,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-1 Electrode-Box-B2 11 121 148 1
8 Banh-mi-1 Electrode-Box-B2 12 120 149 1
9 Banh-mi-1 Electrode-Box-B2 13 135 154 1
10 Banh-mi-2 Electrode-Box-A2 6 74 146 0
11 Banh-mi-2 Electrode-Box-A2 7 70 148 0
12 Banh-mi-2 Electrode-Box-A2 8 65 130 0
13 Banh-mi-2 Electrode-Box-A2 9 79 151 0
14 Banh-mi-2 Electrode-Box-A2 10 93 152 0
15 Egg-tart-1 Electrode-Box-B2 6 71 142 1
16 Egg-tart-1 Electrode-Box-B2 7 79 138 1
17 Egg-tart-1 Electrode-Box-B2 8 98 142 1
18 Egg-tart-1 Electrode-Box-B2 9 89 139 1
19 Egg-tart-1 Electrode-Box-B2 10 96 143 1
20 Egg-tart-1 Electrode-Box-B2 11 96 148 1
21 Egg-tart-1 Electrode-Box-B2 12 101 156 1
22 Egg-tart-1 Electrode-Box-B2 13 103 152 1
23 Egg-tart-2 Electrode-Box-A2 6 85 145 0
24 Egg-tart-2 Electrode-Box-A2 7 79 143 0
25 Egg-tart-2 Electrode-Box-A2 8 76 131 0
26 Egg-tart-2 Electrode-Box-A2 9 88 149 0
27 Egg-tart-2 Electrode-Box-A2 10 81 151 0
28 Egg-tart-2 Electrode-Box-A2 11 78 152 0
29 Egg-tart-2 Electrode-Box-A2 12 96 155 0
30 Egg-tart-2 Electrode-Box-A2 13 84 155 0
31 Khoai-lang-1 Right-Electrode 6 79 146 1
32 Khoai-lang-1 Right-Electrode 7 81 153 1
33 Khoai-lang-1 Right-Electrode 8 98 144 1
34 Khoai-lang-1 Right-Electrode 9 101 149 1
35 Khoai-lang-1 Right-Electrode 10 103 150 1
36 Khoai-lang-1 Right-Electrode 11 113 154 1
37 Khoai-lang-1 Right-Electrode 12 117 148 1
38 Khoai-lang-1 Right-Electrode 13 114 146 1
39 Root-beer-1 Electrode-Box-B2 6 104 139 1
40 Root-beer-1 Electrode-Box-B2 7 98 148 1
41 Root-beer-1 Electrode-Box-B2 8 81 145 1
42 Root-beer-1 Electrode-Box-B2 9 89 156 1
43 Root-beer-1 Electrode-Box-B2 10 105 158 1
analysis/matlab/variations/right_only_d6_13/result.txt
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==============================================================================
VARIATION: right_only_d6_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2, Right-Electrode
control (stim=0): Electrode-Box-A2
N = 6 rats, 42 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 42
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
309.98 320.4 -148.99 297.98
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 75.163 5.7598 13.049 38 1.2935e-15
{'day' } 1.7153 1.01 1.6984 38 0.097608
{'stim' } 11.525 7.0338 1.6386 38 0.10956
{'day:stim' } 2.7579 1.1891 2.3193 38 0.02585
Lower Upper
63.503 86.823
-0.32925 3.7599
-2.7138 25.764
0.35071 5.1651
Random effects covariance parameters (95% CIs):
Group: rat (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 6.4688
Lower Upper
3.3423 12.52
Group: Error
Name Estimate Lower Upper
{'Res Std'} 7.3669 5.8525 9.2731
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(38)= 2.32 F(1)= 5.379 p=0.02585
day (learning) t(38)= 1.70 F(1)= 2.885 p=0.09761
stim (main, window start) t(38)= 1.64 F(1)= 2.685 p=0.1096
interaction 95% CI: [+0.35, +5.17]
INTERPRETATION: stim x day interaction SIGNIFICANT positive -- treatment improves FASTER (benefit accumulates) (p=0.02585, slope diff=+2.76)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/unmerge_d0_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/unmerge_d0_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-2 Electrode-Box-A2 0 25 97 0
14 Banh-mi-2 Electrode-Box-A2 1 22 101 0
15 Banh-mi-2 Electrode-Box-A2 2 22 119 0
16 Banh-mi-2 Electrode-Box-A2 3 29 118 0
17 Banh-mi-2 Electrode-Box-A2 4 27 136 0
18 Banh-mi-2 Electrode-Box-A2 5 43 146 0
19 Banh-mi-2 Electrode-Box-A2 6 74 146 0
20 Banh-mi-2 Electrode-Box-A2 7 70 148 0
21 Banh-mi-2 Electrode-Box-A2 8 65 130 0
22 Banh-mi-2 Electrode-Box-A2 9 79 151 0
23 Banh-mi-2 Electrode-Box-A2 10 93 152 0
24 Egg-tart-1 Electrode-Box-B2 0 7 56 1
25 Egg-tart-1 Electrode-Box-B2 1 16 78 1
26 Egg-tart-1 Electrode-Box-B2 2 23 103 1
27 Egg-tart-1 Electrode-Box-B2 3 63 120 1
28 Egg-tart-1 Electrode-Box-B2 4 69 132 1
29 Egg-tart-1 Electrode-Box-B2 5 83 136 1
30 Egg-tart-1 Electrode-Box-B2 6 71 142 1
31 Egg-tart-1 Electrode-Box-B2 7 79 138 1
32 Egg-tart-1 Electrode-Box-B2 8 98 142 1
33 Egg-tart-1 Electrode-Box-B2 9 89 139 1
34 Egg-tart-1 Electrode-Box-B2 10 96 143 1
35 Egg-tart-2 Electrode-Box-A2 0 9 32 0
36 Egg-tart-2 Electrode-Box-A2 1 2 38 0
37 Egg-tart-2 Electrode-Box-A2 2 31 93 0
38 Egg-tart-2 Electrode-Box-A2 3 44 101 0
39 Egg-tart-2 Electrode-Box-A2 4 54 131 0
40 Egg-tart-2 Electrode-Box-A2 5 84 139 0
41 Egg-tart-2 Electrode-Box-A2 6 85 145 0
42 Egg-tart-2 Electrode-Box-A2 7 79 143 0
43 Egg-tart-2 Electrode-Box-A2 8 76 131 0
44 Egg-tart-2 Electrode-Box-A2 9 88 149 0
45 Egg-tart-2 Electrode-Box-A2 10 81 151 0
46 Root-beer-1 Electrode-Box-B2 0 11 85 1
47 Root-beer-1 Electrode-Box-B2 1 18 76 1
48 Root-beer-1 Electrode-Box-B2 2 40 105 1
49 Root-beer-1 Electrode-Box-B2 3 55 134 1
50 Root-beer-1 Electrode-Box-B2 4 75 136 1
51 Root-beer-1 Electrode-Box-B2 5 64 133 1
52 Root-beer-1 Electrode-Box-B2 6 104 139 1
53 Root-beer-1 Electrode-Box-B2 7 98 148 1
54 Root-beer-1 Electrode-Box-B2 8 81 145 1
55 Root-beer-1 Electrode-Box-B2 9 89 156 1
56 Root-beer-1 Electrode-Box-B2 10 105 158 1
57 Root-beer-2 Electrode-Box-A2 0 22 74 0
58 Root-beer-2 Electrode-Box-A2 1 31 87 0
59 Root-beer-2 Electrode-Box-A2 2 49 134 0
60 Root-beer-2 Electrode-Box-A2 3 31 89 0
61 Root-beer-2 Electrode-Box-A2 4 60 140 0
62 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/unmerge_d0_10/result.txt
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==============================================================================
VARIATION: unmerge_d0_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
day = training day within window (0 = first analyzed day)
treatment (stim=1): Electrode-Box-B2
control (stim=0): Electrode-Box-A2
N = 6 rats, 61 sessions raw day coverage: treat 0..10, control 0..10
(equal day coverage over this window)
==============================================================================
FULL MODEL SUMMARY -- fitlme
==============================================================================
Linear mixed-effects model fit by ML
Model information:
Number of observations 61
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
499.59 512.25 -243.79 487.59
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 17.372 5.1906 3.3469 57 0.0014518
{'day' } 7.9666 0.79053 10.077 57 2.8317e-14
{'stim' } 4.9763 7.2862 0.68298 57 0.49739
{'day:stim' } 1.5577 1.0483 1.4858 57 0.14283
Lower Upper
6.9783 27.766
6.3836 9.5496
-9.614 19.567
-0.5416 3.6569
Random effects covariance parameters (95% CIs):
Group: rat (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 5.3536
Lower Upper
2.1542 13.305
Group: Error
Name Estimate Lower Upper
{'Res Std'} 12.508 10.37 15.086
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(57)= 1.49 F(1)= 2.208 p=0.1428
day (learning) t(57)= 10.08 F(1)= 101.556 p=2.832e-14
stim (main, window start) t(57)= 0.68 F(1)= 0.466 p=0.4974
interaction 95% CI: [-0.54, +3.66]
INTERPRETATION: stim x day interaction n.s. -- slopes parallel (no differential learning rate) (p=0.1428, slope diff=+1.56)
Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008.
analysis/matlab/variations/unmerge_d0_13/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/unmerge_d0_13/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-1,Electrode-Box-B2,11,121,148,1
Banh-mi-1,Electrode-Box-B2,12,120,149,1
Banh-mi-1,Electrode-Box-B2,13,135,154,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-1,Electrode-Box-B2,11,96,148,1
Egg-tart-1,Electrode-Box-B2,12,101,156,1
Egg-tart-1,Electrode-Box-B2,13,103,152,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Egg-tart-2,Electrode-Box-A2,11,78,152,0
Egg-tart-2,Electrode-Box-A2,12,96,155,0
Egg-tart-2,Electrode-Box-A2,13,84,155,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-1 Electrode-Box-B2 6 90 131 1
9 Banh-mi-1 Electrode-Box-B2 7 109 148 1
10 Banh-mi-1 Electrode-Box-B2 8 104 137 1
11 Banh-mi-1 Electrode-Box-B2 9 119 150 1
12 Banh-mi-1 Electrode-Box-B2 10 121 158 1
13 Banh-mi-1 Electrode-Box-B2 11 121 148 1
14 Banh-mi-1 Electrode-Box-B2 12 120 149 1
15 Banh-mi-1 Electrode-Box-B2 13 135 154 1
16 Banh-mi-2 Electrode-Box-A2 0 25 97 0
17 Banh-mi-2 Electrode-Box-A2 1 22 101 0
18 Banh-mi-2 Electrode-Box-A2 2 22 119 0
19 Banh-mi-2 Electrode-Box-A2 3 29 118 0
20 Banh-mi-2 Electrode-Box-A2 4 27 136 0
21 Banh-mi-2 Electrode-Box-A2 5 43 146 0
22 Banh-mi-2 Electrode-Box-A2 6 74 146 0
23 Banh-mi-2 Electrode-Box-A2 7 70 148 0
24 Banh-mi-2 Electrode-Box-A2 8 65 130 0
25 Banh-mi-2 Electrode-Box-A2 9 79 151 0
26 Banh-mi-2 Electrode-Box-A2 10 93 152 0
27 Egg-tart-1 Electrode-Box-B2 0 7 56 1
28 Egg-tart-1 Electrode-Box-B2 1 16 78 1
29 Egg-tart-1 Electrode-Box-B2 2 23 103 1
30 Egg-tart-1 Electrode-Box-B2 3 63 120 1
31 Egg-tart-1 Electrode-Box-B2 4 69 132 1
32 Egg-tart-1 Electrode-Box-B2 5 83 136 1
33 Egg-tart-1 Electrode-Box-B2 6 71 142 1
34 Egg-tart-1 Electrode-Box-B2 7 79 138 1
35 Egg-tart-1 Electrode-Box-B2 8 98 142 1
36 Egg-tart-1 Electrode-Box-B2 9 89 139 1
37 Egg-tart-1 Electrode-Box-B2 10 96 143 1
38 Egg-tart-1 Electrode-Box-B2 11 96 148 1
39 Egg-tart-1 Electrode-Box-B2 12 101 156 1
40 Egg-tart-1 Electrode-Box-B2 13 103 152 1
41 Egg-tart-2 Electrode-Box-A2 0 9 32 0
42 Egg-tart-2 Electrode-Box-A2 1 2 38 0
43 Egg-tart-2 Electrode-Box-A2 2 31 93 0
44 Egg-tart-2 Electrode-Box-A2 3 44 101 0
45 Egg-tart-2 Electrode-Box-A2 4 54 131 0
46 Egg-tart-2 Electrode-Box-A2 5 84 139 0
47 Egg-tart-2 Electrode-Box-A2 6 85 145 0
48 Egg-tart-2 Electrode-Box-A2 7 79 143 0
49 Egg-tart-2 Electrode-Box-A2 8 76 131 0
50 Egg-tart-2 Electrode-Box-A2 9 88 149 0
51 Egg-tart-2 Electrode-Box-A2 10 81 151 0
52 Egg-tart-2 Electrode-Box-A2 11 78 152 0
53 Egg-tart-2 Electrode-Box-A2 12 96 155 0
54 Egg-tart-2 Electrode-Box-A2 13 84 155 0
55 Root-beer-1 Electrode-Box-B2 0 11 85 1
56 Root-beer-1 Electrode-Box-B2 1 18 76 1
57 Root-beer-1 Electrode-Box-B2 2 40 105 1
58 Root-beer-1 Electrode-Box-B2 3 55 134 1
59 Root-beer-1 Electrode-Box-B2 4 75 136 1
60 Root-beer-1 Electrode-Box-B2 5 64 133 1
61 Root-beer-1 Electrode-Box-B2 6 104 139 1
62 Root-beer-1 Electrode-Box-B2 7 98 148 1
63 Root-beer-1 Electrode-Box-B2 8 81 145 1
64 Root-beer-1 Electrode-Box-B2 9 89 156 1
65 Root-beer-1 Electrode-Box-B2 10 105 158 1
66 Root-beer-2 Electrode-Box-A2 0 22 74 0
67 Root-beer-2 Electrode-Box-A2 1 31 87 0
68 Root-beer-2 Electrode-Box-A2 2 49 134 0
69 Root-beer-2 Electrode-Box-A2 3 31 89 0
70 Root-beer-2 Electrode-Box-A2 4 60 140 0
71 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/unmerge_d0_13/result.txt
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==============================================================================
VARIATION: unmerge_d0_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
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
586.72 600.22 -287.36 574.72
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 21.7 4.5484 4.7709 66 1.0537e-05
{'day' } 6.4863 0.71372 9.0881 66 3.0346e-13
{'stim' } 6.0226 6.3135 0.95392 66 0.3436
{'day:stim' } 1.5467 0.93755 1.6497 66 0.10376
Lower Upper
12.619 30.781
5.0614 7.9113
-6.5827 18.628
-0.32523 3.4185
Random effects covariance parameters (95% CIs):
Group: rat (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 0
Lower Upper
NaN NaN
Group: Error
Name Estimate Lower Upper
{'Res Std'} 14.676 12.436 17.32
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(66)= 1.65 F(1)= 2.721 p=0.1038
day (learning) t(66)= 9.09 F(1)= 82.593 p=3.035e-13
stim (main, window start) t(66)= 0.95 F(1)= 0.910 p=0.3436
interaction 95% CI: [-0.33, +3.42]
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.
analysis/matlab/variations/unmerge_d0_5/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/unmerge_d0_5/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,0,13,84,1
Banh-mi-1,Electrode-Box-B2,1,35,86,1
Banh-mi-1,Electrode-Box-B2,2,47,110,1
Banh-mi-1,Electrode-Box-B2,3,65,140,1
Banh-mi-1,Electrode-Box-B2,4,70,127,1
Banh-mi-1,Electrode-Box-B2,5,102,142,1
Banh-mi-2,Electrode-Box-A2,0,25,97,0
Banh-mi-2,Electrode-Box-A2,1,22,101,0
Banh-mi-2,Electrode-Box-A2,2,22,119,0
Banh-mi-2,Electrode-Box-A2,3,29,118,0
Banh-mi-2,Electrode-Box-A2,4,27,136,0
Banh-mi-2,Electrode-Box-A2,5,43,146,0
Egg-tart-1,Electrode-Box-B2,0,7,56,1
Egg-tart-1,Electrode-Box-B2,1,16,78,1
Egg-tart-1,Electrode-Box-B2,2,23,103,1
Egg-tart-1,Electrode-Box-B2,3,63,120,1
Egg-tart-1,Electrode-Box-B2,4,69,132,1
Egg-tart-1,Electrode-Box-B2,5,83,136,1
Egg-tart-2,Electrode-Box-A2,0,9,32,0
Egg-tart-2,Electrode-Box-A2,1,2,38,0
Egg-tart-2,Electrode-Box-A2,2,31,93,0
Egg-tart-2,Electrode-Box-A2,3,44,101,0
Egg-tart-2,Electrode-Box-A2,4,54,131,0
Egg-tart-2,Electrode-Box-A2,5,84,139,0
Root-beer-1,Electrode-Box-B2,0,11,85,1
Root-beer-1,Electrode-Box-B2,1,18,76,1
Root-beer-1,Electrode-Box-B2,2,40,105,1
Root-beer-1,Electrode-Box-B2,3,55,134,1
Root-beer-1,Electrode-Box-B2,4,75,136,1
Root-beer-1,Electrode-Box-B2,5,64,133,1
Root-beer-2,Electrode-Box-A2,0,22,74,0
Root-beer-2,Electrode-Box-A2,1,31,87,0
Root-beer-2,Electrode-Box-A2,2,49,134,0
Root-beer-2,Electrode-Box-A2,3,31,89,0
Root-beer-2,Electrode-Box-A2,4,60,140,0
Root-beer-2,Electrode-Box-A2,5,84,147,0
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 0 13 84 1
3 Banh-mi-1 Electrode-Box-B2 1 35 86 1
4 Banh-mi-1 Electrode-Box-B2 2 47 110 1
5 Banh-mi-1 Electrode-Box-B2 3 65 140 1
6 Banh-mi-1 Electrode-Box-B2 4 70 127 1
7 Banh-mi-1 Electrode-Box-B2 5 102 142 1
8 Banh-mi-2 Electrode-Box-A2 0 25 97 0
9 Banh-mi-2 Electrode-Box-A2 1 22 101 0
10 Banh-mi-2 Electrode-Box-A2 2 22 119 0
11 Banh-mi-2 Electrode-Box-A2 3 29 118 0
12 Banh-mi-2 Electrode-Box-A2 4 27 136 0
13 Banh-mi-2 Electrode-Box-A2 5 43 146 0
14 Egg-tart-1 Electrode-Box-B2 0 7 56 1
15 Egg-tart-1 Electrode-Box-B2 1 16 78 1
16 Egg-tart-1 Electrode-Box-B2 2 23 103 1
17 Egg-tart-1 Electrode-Box-B2 3 63 120 1
18 Egg-tart-1 Electrode-Box-B2 4 69 132 1
19 Egg-tart-1 Electrode-Box-B2 5 83 136 1
20 Egg-tart-2 Electrode-Box-A2 0 9 32 0
21 Egg-tart-2 Electrode-Box-A2 1 2 38 0
22 Egg-tart-2 Electrode-Box-A2 2 31 93 0
23 Egg-tart-2 Electrode-Box-A2 3 44 101 0
24 Egg-tart-2 Electrode-Box-A2 4 54 131 0
25 Egg-tart-2 Electrode-Box-A2 5 84 139 0
26 Root-beer-1 Electrode-Box-B2 0 11 85 1
27 Root-beer-1 Electrode-Box-B2 1 18 76 1
28 Root-beer-1 Electrode-Box-B2 2 40 105 1
29 Root-beer-1 Electrode-Box-B2 3 55 134 1
30 Root-beer-1 Electrode-Box-B2 4 75 136 1
31 Root-beer-1 Electrode-Box-B2 5 64 133 1
32 Root-beer-2 Electrode-Box-A2 0 22 74 0
33 Root-beer-2 Electrode-Box-A2 1 31 87 0
34 Root-beer-2 Electrode-Box-A2 2 49 134 0
35 Root-beer-2 Electrode-Box-A2 3 31 89 0
36 Root-beer-2 Electrode-Box-A2 4 60 140 0
37 Root-beer-2 Electrode-Box-A2 5 84 147 0
analysis/matlab/variations/unmerge_d0_5/result.txt
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==============================================================================
VARIATION: unmerge_d0_5
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
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
289.66 299.16 -138.83 277.66
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 12.524 5.2775 2.3731 32 0.023816
{'day' } 9.8571 1.4772 6.6729 32 1.5706e-07
{'stim' } -3.0159 7.4635 -0.40408 32 0.68884
{'day:stim' } 5.3619 2.089 2.5667 32 0.015149
Lower Upper
1.7739 23.274
6.8482 12.866
-18.219 12.187
1.1067 9.6172
Random effects covariance parameters (95% CIs):
Group: rat (6 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 4.8527
Lower Upper
1.7069 13.796
Group: Error
Name Estimate Lower Upper
{'Res Std'} 10.703 8.3104 13.785
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(32)= 2.57 F(1)= 6.588 p=0.01515
day (learning) t(32)= 6.67 F(1)= 44.528 p=1.571e-07
stim (main, window start) t(32)= -0.40 F(1)= 0.163 p=0.6888
interaction 95% CI: [+1.11, +9.62]
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.
analysis/matlab/variations/unmerge_d6_10/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/unmerge_d6_10/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-2 Electrode-Box-A2 6 74 146 0
8 Banh-mi-2 Electrode-Box-A2 7 70 148 0
9 Banh-mi-2 Electrode-Box-A2 8 65 130 0
10 Banh-mi-2 Electrode-Box-A2 9 79 151 0
11 Banh-mi-2 Electrode-Box-A2 10 93 152 0
12 Egg-tart-1 Electrode-Box-B2 6 71 142 1
13 Egg-tart-1 Electrode-Box-B2 7 79 138 1
14 Egg-tart-1 Electrode-Box-B2 8 98 142 1
15 Egg-tart-1 Electrode-Box-B2 9 89 139 1
16 Egg-tart-1 Electrode-Box-B2 10 96 143 1
17 Egg-tart-2 Electrode-Box-A2 6 85 145 0
18 Egg-tart-2 Electrode-Box-A2 7 79 143 0
19 Egg-tart-2 Electrode-Box-A2 8 76 131 0
20 Egg-tart-2 Electrode-Box-A2 9 88 149 0
21 Egg-tart-2 Electrode-Box-A2 10 81 151 0
22 Root-beer-1 Electrode-Box-B2 6 104 139 1
23 Root-beer-1 Electrode-Box-B2 7 98 148 1
24 Root-beer-1 Electrode-Box-B2 8 81 145 1
25 Root-beer-1 Electrode-Box-B2 9 89 156 1
26 Root-beer-1 Electrode-Box-B2 10 105 158 1
analysis/matlab/variations/unmerge_d6_10/result.txt
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==============================================================================
VARIATION: unmerge_d6_10
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
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
196.97 204.29 -92.486 184.97
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 74.2 6.4017 11.591 21 1.3774e-10
{'day' } 2.4 1.9295 1.2439 21 0.22726
{'stim' } 14.333 8.2646 1.7343 21 0.097522
{'day:stim' } 1.7667 2.491 0.70923 21 0.48598
Lower Upper
60.887 87.513
-1.6126 6.4126
-2.8539 31.521
-3.4136 6.9469
Random effects covariance parameters (95% CIs):
Group: rat (5 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 6.1065
Lower Upper
2.5428 14.665
Group: Error
Name Estimate Lower Upper
{'Res Std'} 8.6289 6.3295 11.764
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(21)= 0.71 F(1)= 0.503 p=0.486
day (learning) t(21)= 1.24 F(1)= 1.547 p=0.2273
stim (main, window start) t(21)= 1.73 F(1)= 3.008 p=0.09752
interaction 95% CI: [-3.41, +6.95]
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.
analysis/matlab/variations/unmerge_d6_13/analyze.m
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% Variation analysis -- the paper's linear mixed model on the successful-reach
% COUNT, fit on this folder's curated data subset.
%
% 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.)
here = fileparts(mfilename('fullpath'));
if isempty(here); here = pwd; end
vname = regexprep(here, '.*[/\\]', ''); % folder name = variation id
D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
tbl = table(D.success, 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);
gi = @(t) find(strcmp(C.Name, t), 1);
ga = @(t) find(strcmp(A.Term, t), 1);
row = @(nm, t) sprintf('%-26s t(%d)=%6.2f F(%d)=%8.3f p=%.4g\n', nm, ...
C.DF(gi(t)), C.tStat(gi(t)), A.DF1(ga(t)), A.FStat(ga(t)), C.pValue(gi(t)));
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
cov = '** WARNING: unequal day coverage -- interaction may be confounded. **';
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
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 = [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))), ', '))];
s = [s sprintf('N = %d rats, %d sessions raw day coverage: treat %d..%d, control %d..%d\n', ...
numel(unique(D.subject)), height(D), minT, maxT, minC, maxC)];
s = [s sprintf('%s\n\n%s\nFULL MODEL SUMMARY -- fitlme\n%s\n%s\n', cov, bar, bar, raw)];
s = [s sprintf('%-26s %-13s %-13s %s\n%s\n', 'effect', 't (df)', 'F (df1)', 'p', repmat('-', 1, 66))];
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('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')];
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, ...
'stimP', C.pValue(gi('stim')), 'dayP', C.pValue(gi('day')), ...
'covEqual', abs(maxT - maxC) <= 2);
analysis/matlab/variations/unmerge_d6_13/data.csv
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subject,group,day,success,total,stim
Banh-mi-1,Electrode-Box-B2,6,90,131,1
Banh-mi-1,Electrode-Box-B2,7,109,148,1
Banh-mi-1,Electrode-Box-B2,8,104,137,1
Banh-mi-1,Electrode-Box-B2,9,119,150,1
Banh-mi-1,Electrode-Box-B2,10,121,158,1
Banh-mi-1,Electrode-Box-B2,11,121,148,1
Banh-mi-1,Electrode-Box-B2,12,120,149,1
Banh-mi-1,Electrode-Box-B2,13,135,154,1
Banh-mi-2,Electrode-Box-A2,6,74,146,0
Banh-mi-2,Electrode-Box-A2,7,70,148,0
Banh-mi-2,Electrode-Box-A2,8,65,130,0
Banh-mi-2,Electrode-Box-A2,9,79,151,0
Banh-mi-2,Electrode-Box-A2,10,93,152,0
Egg-tart-1,Electrode-Box-B2,6,71,142,1
Egg-tart-1,Electrode-Box-B2,7,79,138,1
Egg-tart-1,Electrode-Box-B2,8,98,142,1
Egg-tart-1,Electrode-Box-B2,9,89,139,1
Egg-tart-1,Electrode-Box-B2,10,96,143,1
Egg-tart-1,Electrode-Box-B2,11,96,148,1
Egg-tart-1,Electrode-Box-B2,12,101,156,1
Egg-tart-1,Electrode-Box-B2,13,103,152,1
Egg-tart-2,Electrode-Box-A2,6,85,145,0
Egg-tart-2,Electrode-Box-A2,7,79,143,0
Egg-tart-2,Electrode-Box-A2,8,76,131,0
Egg-tart-2,Electrode-Box-A2,9,88,149,0
Egg-tart-2,Electrode-Box-A2,10,81,151,0
Egg-tart-2,Electrode-Box-A2,11,78,152,0
Egg-tart-2,Electrode-Box-A2,12,96,155,0
Egg-tart-2,Electrode-Box-A2,13,84,155,0
Root-beer-1,Electrode-Box-B2,6,104,139,1
Root-beer-1,Electrode-Box-B2,7,98,148,1
Root-beer-1,Electrode-Box-B2,8,81,145,1
Root-beer-1,Electrode-Box-B2,9,89,156,1
Root-beer-1,Electrode-Box-B2,10,105,158,1
1 subject group day success total stim
2 Banh-mi-1 Electrode-Box-B2 6 90 131 1
3 Banh-mi-1 Electrode-Box-B2 7 109 148 1
4 Banh-mi-1 Electrode-Box-B2 8 104 137 1
5 Banh-mi-1 Electrode-Box-B2 9 119 150 1
6 Banh-mi-1 Electrode-Box-B2 10 121 158 1
7 Banh-mi-1 Electrode-Box-B2 11 121 148 1
8 Banh-mi-1 Electrode-Box-B2 12 120 149 1
9 Banh-mi-1 Electrode-Box-B2 13 135 154 1
10 Banh-mi-2 Electrode-Box-A2 6 74 146 0
11 Banh-mi-2 Electrode-Box-A2 7 70 148 0
12 Banh-mi-2 Electrode-Box-A2 8 65 130 0
13 Banh-mi-2 Electrode-Box-A2 9 79 151 0
14 Banh-mi-2 Electrode-Box-A2 10 93 152 0
15 Egg-tart-1 Electrode-Box-B2 6 71 142 1
16 Egg-tart-1 Electrode-Box-B2 7 79 138 1
17 Egg-tart-1 Electrode-Box-B2 8 98 142 1
18 Egg-tart-1 Electrode-Box-B2 9 89 139 1
19 Egg-tart-1 Electrode-Box-B2 10 96 143 1
20 Egg-tart-1 Electrode-Box-B2 11 96 148 1
21 Egg-tart-1 Electrode-Box-B2 12 101 156 1
22 Egg-tart-1 Electrode-Box-B2 13 103 152 1
23 Egg-tart-2 Electrode-Box-A2 6 85 145 0
24 Egg-tart-2 Electrode-Box-A2 7 79 143 0
25 Egg-tart-2 Electrode-Box-A2 8 76 131 0
26 Egg-tart-2 Electrode-Box-A2 9 88 149 0
27 Egg-tart-2 Electrode-Box-A2 10 81 151 0
28 Egg-tart-2 Electrode-Box-A2 11 78 152 0
29 Egg-tart-2 Electrode-Box-A2 12 96 155 0
30 Egg-tart-2 Electrode-Box-A2 13 84 155 0
31 Root-beer-1 Electrode-Box-B2 6 104 139 1
32 Root-beer-1 Electrode-Box-B2 7 98 148 1
33 Root-beer-1 Electrode-Box-B2 8 81 145 1
34 Root-beer-1 Electrode-Box-B2 9 89 156 1
35 Root-beer-1 Electrode-Box-B2 10 105 158 1
analysis/matlab/variations/unmerge_d6_13/result.txt
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==============================================================================
VARIATION: unmerge_d6_13
==============================================================================
model: behavior ~ stim + day + stim:day + (1|rat) (behavior = success COUNT)
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
257.04 266.2 -122.52 245.04
Fixed effects coefficients (95% CIs):
Name Estimate SE tStat DF pValue
{'(Intercept)'} 75.17 6.2311 12.064 30 4.8879e-13
{'day' } 1.7101 1.061 1.6117 30 0.11749
{'stim' } 13.563 8.0252 1.69 30 0.1014
{'day:stim' } 2.267 1.3237 1.7126 30 0.097101
Lower Upper
62.445 87.896
-0.45682 3.877
-2.8271 29.952
-0.43635 4.9703
Random effects covariance parameters (95% CIs):
Group: rat (5 Levels)
Name1 Name2 Type Estimate
{'(Intercept)'} {'(Intercept)'} {'std'} 7.1166
Lower Upper
3.4834 14.539
Group: Error
Name Estimate Lower Upper
{'Res Std'} 7.7328 5.9847 9.9914
effect t (df) F (df1) p
------------------------------------------------------------------
stim x day (interaction) t(30)= 1.71 F(1)= 2.933 p=0.0971
day (learning) t(30)= 1.61 F(1)= 2.598 p=0.1175
stim (main, window start) t(30)= 1.69 F(1)= 2.856 p=0.1014
interaction 95% CI: [-0.44, +4.97]
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.