/
IGT_Toolbox_1_AnalyzeRawBehavior.m
executable file
·496 lines (472 loc) · 25.1 KB
/
IGT_Toolbox_1_AnalyzeRawBehavior.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
%%%%%%%%%%%%%%%% IGTtoolbox Results - Behavioral Measures %%%%%%%%%%%%%%%%%
% This script outputs all the behavioral measures which are independent of
% computational modeling (e.g net scores, directed exploration etc.).
% It computes automatically the statistics according to the type of
% analysis performed:
% - takes into account the independent vs dependent nature of your observations
% - takes into account the number of groups/conditions to decide for ex. if
% t-tests or ANOVAs should be run
% - tests whether your data is normally distributed using a lillietest and
% output both parametric and nonparametric statistics in any case.
% It also plots the different measurements (plot2svg can be used to obtain
% vectorial images).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Romain Ligneul / romain.ligneul@gmail.com / Dec 2018.
% v1.2
% clean the environment
clear all; close all;clc;
% get useful function
addpath(genpath('Tools/OTHERS'));
% load relevant dataset (matching its folder name in IGTdata/)
model_dir = [uigetdir('IGTdata') '/'];
load([model_dir 'IGTdata.mat']);
% set color map
colormap_custom = 'brewer1';
%% Loop over subjects and compute behavioral measures
% in order to avoid polluting the workspace with useless variable, all work
% is saved in structure
for s = 1:length(data)
disp(['subject n°' num2str(s)])
% subject id and condition id
subj_id(s,1) = s;
cond_id(s,1) = data{s}.cond;
try
cond_label{s,1} = data{s}.cond_label;
catch
cond_label{s,1} = num2str(cond_id(s,1));
end
% compute net score
netscore_time(s,:) = cumsum(ismember(data{s}.deck, [3 4]))-cumsum(ismember(data{s}.deck, [1 2]));
netscore_total(s,:) = netscore_time(s,end);
for d=1:4
times_chosen(d,:) = cumsum(data{s}.deck==d);
dumpayoff = cumsum(double(data{s}.deck==d).*(data{s}.win-data{s}.lose))';
avg_payoff(d,:) = dumpayoff./times_chosen(d,:);
end
avg_payoff = [[NaN;NaN;NaN;NaN],avg_payoff(:,1:end-1)];
times_chosen = [[0;0;0;0],times_chosen(:,1:end-1)];
% compute win-stay lose-shift metrics
stay = data{s}.deck(2:end)==data{s}.deck(1:end-1);
prvloss = abs(data{s}.lose(1:end-1))>0;
WSLS(s,:) = [mean(stay(~prvloss)) mean(~stay(prvloss))];
clear stay prvloss
% evaluate repeat after bigpun
bigpun_ind = find(data{s}.lose<=-1000);
bigpun_number(s,1) = numel(bigpun_ind);
if ~isempty(bigpun_ind) && bigpun_ind(end)==100
bigpun_ind(end)=[];
end
if ~isempty(bigpun_ind)
repeat_after_bigpun(s,1) = mean(double(data{s}.deck(bigpun_ind)==data{s}.deck(bigpun_ind+1)));
else
repeat_after_bigpun(s,1) = NaN;
end
clear bigpun_ind
% compute choice entropy and mutual information between successive
% choices
H_total(s,1) = h(data{s}.deck);
MI2choices_total(s,1) = mi(data{s}.deck(1:end-1),data{s}.deck(2:end));
% compute directed exploration indexes
for t = 1:98
directed_exploration3(s,t) = double(numel(unique(data{s}.deck(t:t+2)))==3);
if t<98
directed_exploration4(s,t) = double(numel(unique(data{s}.deck(t:t+3)))==4);
end
end
tt=0;
for t = 1:4:97
tt=tt+1;
directed_exploration4_fixed(s,t) = double(numel(unique(data{s}.deck(t:t+3)))==4);
end
% permute to obtain empirical chance levels on exploration indexes
for r = 1:round(5000/length(data))
perm_deck = data{s}.deck(randperm(length(data{s}.deck)));
for t = 1:length(perm_deck)-2
perm_DE3(r,t) = double(numel(unique(perm_deck(t:t+2)))==3);
end
for t = 1:length(perm_deck)-3
perm_DE4(r,t) = double(numel(unique(perm_deck(t:t+3)))==4);
end
perm_deck = data{s}.deck(randperm(length(data{s}.deck))); tt=0;
for t = 1:4:length(perm_deck)-2
tt=tt+1;
perm_DE3_fixed(r,tt) = double(numel(unique(perm_deck(t:t+2)))==3);
end
tt=0;
for t = 1:length(perm_deck)-3
tt=tt+1;
perm_DE4_fixed(r,tt) = double(numel(unique(perm_deck(t:t+3)))==4);
end
end
chance_DE3(s) = mean2(perm_DE3);
chance_DE4(s) = mean2(perm_DE4);
chance_DE3_fixed(s) = mean2(perm_DE3_fixed);
chance_DE4_fixed(s) = mean2(perm_DE4_fixed);
end
nsubj = s;
ntrial = 100;
%% statistics
% test effects on parameters
[condition_list, ia] = unique(cond_label);
condition_number = cond_id(ia);
% ask user to select conditions to use for statistical inference
condition_selection = listdlg('PromptString', 'Select the conditions you want to include in the analysis:', ...
'ListString', condition_list, 'ListSize', [300 200]);
condition_ids = cellstr(char(condition_list(condition_selection)));
condition_number = condition_number(condition_selection);
% ask user to define test type
if numel(condition_ids)>1
dumstr = questdlg('Are the conditions reflecting repeated measures or independent observation units?', 'Test type', 'Repeated', 'Independent', 'Independent');
test_type = strmatch(dumstr, {'Repeated', 'Independent'});
% ask user to chose what to plot
dumstr = questdlg('Plot all or just selected condtions/groups?', 'Plot type', 'All', 'Selected', 'Selected');
plot_type = strmatch(dumstr, {'All', 'Selected'});
else
test_type=0;
plot_type=1;
end
%%% get means, std and normality tests of relevant variables
gmat = [];
netscore_ymat = [];
WS_ymat = []; LS_ymat = [];
MI_successive_choices_ymat = [];
H_choices_ymat = [];
DE3_ymat = [];
DE4_ymat = [];
gmat = [];
% loop over conditions
for c = 1:length(condition_selection);
gmat = [gmat; condition_selection(c)*ones(sum(condition_number(c)==cond_id),1)];
% net score
stats_all.netscore.mat{c} = netscore_total(condition_number(c)==cond_id);
stats_all.netscore.normality(c) = 1-lillietest(stats_all.netscore.mat{c});
netscore_ymat = [netscore_ymat; stats_all.netscore.mat{c}];
% win stay
stats_all.WS.mat{c} = WSLS(condition_number(c)==cond_id,1);
stats_all.WS.normality(c) = 1-lillietest(stats_all.WS.mat{c});
WS_ymat = [WS_ymat; stats_all.WS.mat{c}];
% lose shift
stats_all.LS.mat{c} = WSLS(condition_number(c)==cond_id,2);
stats_all.LS.normality(c) = 1-lillietest(stats_all.LS.mat{c});
LS_ymat = [LS_ymat; stats_all.LS.mat{c}];
% MI successive choices
stats_all.MI_successive_choices.mat{c} = MI2choices_total(condition_number(c)==cond_id);
stats_all.MI_successive_choices.normality(c) = 1-lillietest(stats_all.MI_successive_choices.mat{c});
MI_successive_choices_ymat = [MI_successive_choices_ymat; stats_all.MI_successive_choices.mat{c}];
% choice entropy total
stats_all.H_choices.mat{c} = H_total(condition_number(c)==cond_id);
stats_all.H_choices.normality(c) = 1-lillietest(stats_all.H_choices.mat{c});
H_choices_ymat = [H_choices_ymat; stats_all.H_choices.mat{c}];
% directed exploration 3
stats_all.DE3.mat{c} = nanmean(directed_exploration3(condition_number(c)==cond_id,:),2);
stats_all.DE4.normality(c) = 1-lillietest(stats_all.DE3.mat{c});
DE3_ymat = [DE3_ymat; stats_all.DE3.mat{c}];
% directed exploration 4
stats_all.DE4.mat{c} = nanmean(directed_exploration4(condition_number(c)==cond_id,:),2);
stats_all.DE4.normality(c) = 1-lillietest(stats_all.DE4.mat{c});
DE4_ymat = [DE4_ymat; stats_all.DE4.mat{c}];
end
stats_all.gmat = gmat;
stats_all.netscore.means = meanbycond(netscore_ymat, gmat,[]);
stats_all.netscore.stds = stdbycond(netscore_ymat, gmat,[]);
stats_all.WS.means = meanbycond(WS_ymat, gmat,[]);
stats_all.WS.stds = stdbycond(WS_ymat, gmat,[]);
stats_all.LS.means = meanbycond(LS_ymat, gmat,[]);
stats_all.LS.stds = stdbycond(LS_ymat, gmat,[]);
stats_all.MI_successive_choices.means = meanbycond(MI_successive_choices_ymat, gmat,[]);
stats_all.MI_successive_choices.stds = stdbycond(MI_successive_choices_ymat, gmat,[]);
stats_all.H_choices.means = meanbycond(H_choices_ymat, gmat,[]);
stats_all.H_choices.stds = stdbycond(H_choices_ymat, gmat,[]);
stats_all.DE3.means = meanbycond(DE3_ymat, gmat,[]);
stats_all.DE3.stds = stdbycond(DE3_ymat, gmat,[]);
stats_all.DE4.means = meanbycond(DE4_ymat, gmat,[]);
stats_all.DE4.stds = stdbycond(DE4_ymat, gmat,[]);
% report the type of tests used
if test_type==2
stats_all.nonparam_tests = 'Wilcoxon unpaired (2 groups) or Kruskall-Wallis (>2 groups)';
stats_all.param_tests = 't-test unpaired (2 groups) or anova1 (>2 groups)';
else
stats_all.netscore.nonparam_tests = 'Wilcoxon paired (2 groups) or Friedmann (>2 groups)';
stats_all.netscore.param_tests = 't-test paired (2 groups) or repeated measure ANOVA (>2 groups)';
end
%%% compute statistics on all measures of interest
if test_type==2
if length(condition_number)==2
% netscore
[stats_all.netscore.nonparam_p, ~, stats_all.netscore.nonparam_stats] = ranksum(netscore_ymat(gmat==min(gmat)),netscore_ymat(gmat==max(gmat)));
[~, stats_all.netscore.param_p, ~, stats_all.netscore.param_stats] = ttest2(netscore_ymat(gmat==min(gmat)),netscore_ymat(gmat==max(gmat)));
% WS
[stats_all.WS.nonparam_p, ~, stats_all.WS.nonparam_stats] = ranksum(WS_ymat(gmat==min(gmat)),WS_ymat(gmat==max(gmat)));
[~, stats_all.WS.param_p, stats_all.WS.param_stats] = ttest2(WS_ymat(gmat==min(gmat)),WS_ymat(gmat==max(gmat)));
% LS
[stats_all.LS.nonparam_p, ~, stats_all.LS.nonparam_stats] = ranksum(LS_ymat(gmat==min(gmat)),LS_ymat(gmat==max(gmat)));
[~, stats_all.LS.param_p, ~, stats_all.LS.param_stats] = ttest2(LS_ymat(gmat==min(gmat)),LS_ymat(gmat==max(gmat)));
% MI_successive_choices
[stats_all.MI_successive_choices.nonparam_p, ~, stats_all.MI_successive_choices.nonparam_stats] = ranksum(MI_successive_choices_ymat(gmat==min(gmat)),MI_successive_choices_ymat(gmat==max(gmat)));
[~, stats_all.MI_successive_choices.param_p, ~, stats_all.MI_successive_choices.param_stats] = ttest2(MI_successive_choices_ymat(gmat==min(gmat)),MI_successive_choices_ymat(gmat==max(gmat)));
% H_choices
[stats_all.H_choices.nonparam_p, ~, stats_all.H_choices.nonparam_stats] = ranksum(H_choices_ymat(gmat==min(gmat)),H_choices_ymat(gmat==max(gmat)));
[~, stats_all.H_choices.param_p, ~, stats_all.H_choices.param_stats] = ttest2(H_choices_ymat(gmat==min(gmat)),H_choices_ymat(gmat==max(gmat)));
% DE3
[stats_all.DE3.nonparam_p, ~, stats_all.DE3.nonparam_stats] = ranksum(DE3_ymat(gmat==min(gmat)),DE3_ymat(gmat==max(gmat)));
[~, stats_all.DE3.param_p, stats_all.DE3.param_stats] = ttest2(DE3_ymat(gmat==min(gmat)),DE3_ymat(gmat==max(gmat)));
% DE4
[stats_all.DE4.nonparam_p, ~, stats_all.DE4.nonparam_stats] = ranksum(DE4_ymat(gmat==min(gmat)),DE4_ymat(gmat==max(gmat)));
[~, stats_all.DE4.param_p, ~, stats_all.DE4.param_stats] = ttest2(DE4_ymat(gmat==min(gmat)),DE4_ymat(gmat==max(gmat)));
elseif length(condition_number)>2
% netscore
[stats_all.netscore.param_p, stats_all.netscore.param_table, stats_all.netscore.stats] = anova1(netscore_ymat, gmat, 'off');
[stats_all.netscore.nonparam_p, stats_all.netscore.nonparam_table, stats_all.netscore.nonparam_stats] = kruskalwallis(netscore_ymat, gmat, 'off');
% WS
[stats_all.WS.param_p, stats_all.WS.param_table, stats_all.WS.stats] = anova1(WS_ymat, gmat, 'off');
[stats_all.WS.nonparam_p, stats_all.WS.nonparam_table, stats_all.WS.nonparam_stats] = kruskalwallis(WS_ymat, gmat, 'off');
% LS
[stats_all.LS.param_p, stats_all.LS.param_table, stats_all.LS.stats] = anova1(LS_ymat, gmat, 'off');
[stats_all.LS.nonparam_p, stats_all.LS.nonparam_table, stats_all.LS.nonparam_stats] = kruskalwallis(LS_ymat, gmat, 'off');
% MI_successive_choices
[stats_all.MI_successive_choices.param_p, stats_all.MI_successive_choices.param_table, stats_all.MI_successive_choices.stats] = anova1(MI_successive_choices_ymat, gmat, 'off');
[stats_all.MI_successive_choices.nonparam_p, stats_all.MI_successive_choices.nonparam_table, stats_all.MI_successive_choices.nonparam_stats] = kruskalwallis(MI_successive_choices_ymat, gmat, 'off');
% H_choices
[stats_all.H_choices.param_p, stats_all.H_choices.param_table, stats_all.H_choices.stats] = anova1(H_choices_ymat, gmat, 'off');
[stats_all.H_choices.nonparam_p, stats_all.H_choices.nonparam_table, stats_all.H_choices.nonparam_stats] = kruskalwallis(H_choices_ymat, gmat, 'off');
% DE3
[stats_all.DE3.param_p, stats_all.DE3.param_table, stats_all.DE3.stats] = anova1(DE3_ymat, gmat, 'off');
[stats_all.DE3.nonparam_p, stats_all.DE3.nonparam_table, stats_all.DE3.nonparam_stats] = kruskalwallis(DE3_ymat, gmat, 'off');
% DE4
[stats_all.DE4.param_p, stats_all.DE4.param_table, stats_all.DE4.stats] = anova1(DE4_ymat, gmat, 'off');
[stats_all.DE4.nonparam_p, stats_all.DE4.nonparam_table, stats_all.DE4.nonparam_stats] = kruskalwallis(DE4_ymat, gmat, 'off');
else
warning('No statistics computed: only one group or condition was defined!');
end
else
if length(condition_number)==2
% netscore
[stats_all.netscore.nonparam_p, ~, stats_all.netscore.nonparam_stats] = signrank(netscore_ymat(gmat==min(gmat)),netscore_ymat(gmat==max(gmat)));
[~, stats_all.netscore.param_p, stats_all.netscore.param_stats] = ttest(netscore_ymat(gmat==min(gmat)),netscore_ymat(gmat==max(gmat)));
% WS
[stats_all.WS.nonparam_p, ~, stats_all.WS.nonparam_stats] = signrank(WS_ymat(gmat==min(gmat)),WS_ymat(gmat==max(gmat)));
[~, stats_all.WS.param_p, stats_all.WS.param_stats] = ttest(WS_ymat(gmat==min(gmat)),WS_ymat(gmat==max(gmat)));
% LS
[stats_all.LS.nonparam_p, ~, stats_all.LS.nonparam_stats] = signrank(LS_ymat(gmat==min(gmat)),LS_ymat(gmat==max(gmat)));
[~, stats_all.LS.param_p, stats_all.LS.param_stats] = ttest(LS_ymat(gmat==min(gmat)),LS_ymat(gmat==max(gmat)));
% MI_successive_choices
[stats_all.MI_successive_choices.nonparam_p, ~, stats_all.MI_successive_choices.nonparam_stats] = signrank(MI_successive_choices_ymat(gmat==min(gmat)),MI_successive_choices_ymat(gmat==max(gmat)));
[~, stats_all.MI_successive_choices.param_p, stats_all.MI_successive_choices.param_stats] = ttest(MI_successive_choices_ymat(gmat==min(gmat)),MI_successive_choices_ymat(gmat==max(gmat)));
% H_choices
[stats_all.H_choices.nonparam_p, ~, stats_all.H_choices.nonparam_stats] = signrank(H_choices_ymat(gmat==min(gmat)),H_choices_ymat(gmat==max(gmat)));
[~, stats_all.H_choices.param_p, stats_all.H_choices.param_stats] = ttest(H_choices_ymat(gmat==min(gmat)), H_choices_ymat(gmat==max(gmat)));
% DE3
[stats_all.DE3.nonparam_p, ~, stats_all.DE3.nonparam_stats] = signrank(DE3_ymat(gmat==min(gmat)),DE3_ymat(gmat==max(gmat)));
[~, stats_all.DE3.param_p, stats_all.DE3.param_stats] = ttest(DE3_ymat(gmat==min(gmat)), DE3_ymat(gmat==max(gmat)));
% DE4
[stats_all.DE4.nonparam_p, ~, stats_all.DE4.nonparam_stats] = signrank(DE4_ymat(gmat==min(gmat)),DE4_ymat(gmat==max(gmat)));
[~, stats_all.DE4.param_p, stats_all.DE4.param_stats] = ttest(DE4_ymat(gmat==min(gmat)), DE4_ymat(gmat==max(gmat)));
elseif length(condition_number)>2
% netscore
[stats_all.netscore.param_p, stats_all.netscore.param_table] = anova_rm(cell2mat(stats_all.netscore.mat), 'off');
[stats_all.netscore.nonparam_p, stats_all.netscore.nonparam_table, stats_all.netscore.nonparam_stats] = friedman(cell2mat(stats_all.netscore.mat{c}));
% WS
[stats_all.WS.param_p, stats_all.WS.param_table] = anova_rm(cell2mat(stats_all.WS.mat), 'off');
[stats_all.WS.nonparam_p, stats_all.WS.nonparam_table, stats_all.WS.nonparam_stats] = friedman(cell2mat(stats_all.WS.mat{c}));
% LS
[stats_all.LS.param_p, stats_all.LS.param_table] = anova_rm(cell2mat(stats_all.LS.mat), 'off');
[stats_all.LS.nonparam_p, stats_all.LS.nonparam_table, stats_all.LS.nonparam_stats] = friedman(cell2mat(stats_all.LS.mat{c}));
% MI_successive_choices
[stats_all.MI_successive_choices.param_p, stats_all.MI_successive_choices.param_table] = anova_rm(cell2mat(stats_all.MI_successive_choices.mat), 'off');
[stats_all.MI_successive_choices.nonparam_p, stats_all.MI_successive_choices.nonparam_table, stats_all.MI_successive_choices.nonparam_stats] = friedman(cell2mat(stats_all.MI_successive_choices.mat{c}));
% H_choices
[stats_all.H_choices.param_p, stats_all.H_choices.param_table] = anova_rm(cell2mat(stats_all.H_choices.mat{c}), 'off');
[stats_all.H_choices.nonparam_p, stats_all.H_choices.nonparam_table, stats_all.H_choices.nonparam_stats] = friedman(cell2mat(stats_all.H_choices.mat));
% DE3
[stats_all.DE3.param_p, stats_all.DE3.param_table] = anova_rm(cell2mat(stats_all.DE3.mat{c}), 'off');
[stats_all.DE3.nonparam_p, stats_all.DE3.nonparam_table, stats_all.DE3.nonparam_stats] = friedman(cell2mat(stats_all.DE3.mat));
% DE4
[stats_all.DE4.param_p, stats_all.DE4.param_table] = anova_rm(cell2mat(stats_all.DE4.mat{c}), 'off');
[stats_all.DE4.nonparam_p, stats_all.DE4.nonparam_table, stats_all.DE4.nonparam_stats] = friedman(cell2mat(stats_all.DE4.mat));
else
warning('No statistics computed: only one group or condition was defined!');
end
end
% log test type
if test_type==2
stats_all.nonparam_tests = 'Wilcoxon unpaired (2 groups) or Kruskall-Wallis (>2 groups)';
stats_all.param_tests = 't-test unpaired (2 groups) or anova1 (>2 groups)';
else
stats_all.netscore.nonparam_tests = 'Wilcoxon paired (2 groups) or Friedmann (>2 groups)';
stats_all.netscore.param_tests = 't-test paired (2 groups) or repeated measure ANOVA (>2 groups)';
end
%% plot and perform statistical test on NET scores
% in time
figure('Name', 'Net Score','units','normalized','position',[0 0 .45 .45]);
clear g;
x=repmat(1:ntrial,nsubj,1);
y=netscore_time;
color=repmat(cond_label,1,ntrial);
if plot_type==1
g(1,1) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(1,1) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(1,1).stat_summary('type', 'sem', 'geom', 'area', 'setylim', true);
g(1,1).set_title('NET score in time');
g(1,1).set_names('x', 'trial', 'y', 'NET score');
g(1,1).set_color_options('map', colormap_custom);
% average
y=netscore_total;
color=cond_label;
x = ones(size(color,1), size(color,2));
if plot_type==1
g(2,1) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(2,1) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(2,1).stat_summary('type', 'sem', 'geom', 'bar', 'dodge', 0.7, 'width', 0.6);
g(2,1).stat_summary('type', 'sem', 'geom', 'errorbar', 'dodge', 0.7, 'width', 0.6, 'setylim', true);
g(2,1).axe_property('Xlim', [0 2], 'xtick', [], 'xticklabel', []); ;
g(2,1).set_title('Final NET score');
g(2,1).set_names('x', 'group', 'y', 'NET score');
g(2,1).set_color_options('map', colormap_custom);
% display panels
g.draw();
%% plot and perform statistical test on other behavioral measures
% in time
figure('Name', 'Other behavioral measures','units','normalized','position',[0.55 0 .45 .45]);
clear g;
% average Win Stay
y=WSLS(:,1);
color=cond_label;
x = ones(size(color,1), size(color,2));
if plot_type==1
g(1,1) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(1,1) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(1,1).stat_summary('type', 'sem', 'geom', 'bar', 'dodge', 0.7, 'width', 0.6);
g(1,1).stat_summary('type', 'sem', 'geom', 'errorbar', 'dodge', 0.7, 'width', 0.6, 'setylim', true);
g(1,1).axe_property('Xlim', [0 2], 'xtick', [], 'xticklabel', []); ;
g(1,1).set_title('Win Stay');
g(1,1).set_names('x', 'group', 'y', 'frequency');
g(1,1).set_color_options('map', colormap_custom);
% average Win Stay
y=WSLS(:,2);
color=cond_label;
x = ones(size(color,1), size(color,2));
if plot_type==1
g(1,2) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(1,2) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(1,2).stat_summary('type', 'sem', 'geom', 'bar', 'dodge', 0.7, 'width', 0.6);
g(1,2).stat_summary('type', 'sem', 'geom', 'errorbar', 'dodge', 0.7, 'width', 0.6, 'setylim', true);
g(1,2).axe_property('Xlim', [0 2], 'xtick', [], 'xticklabel', []); ;
g(1,2).set_title('Lose Shift');
g(1,2).set_names('x', 'group', 'y', 'frequency');
g(1,2).set_color_options('map', colormap_custom);
% choice entropy
y=H_total;
color=cond_label;
x = ones(size(color,1), size(color,2));
if plot_type==1
g(2,1) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(2,1) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(2,1).stat_summary('type', 'sem', 'geom', 'bar', 'dodge', 0.7, 'width', 0.6);
g(2,1).stat_summary('type', 'sem', 'geom', 'errorbar', 'dodge', 0.7, 'width', 0.6, 'setylim', true);
g(2,1).axe_property('Xlim', [0 2], 'xtick', [], 'xticklabel', []); ;
g(2,1).set_title('H(choice)');
g(2,1).set_names('x', 'group', 'y', 'bits');
g(2,1).set_color_options('map', colormap_custom);
% mutual information of successive choices (perseveration measure)
y=MI2choices_total;
color=cond_label;
x = ones(size(color,1), size(color,2));
if plot_type==1
g(2,2) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(2,2) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(2,2).stat_summary('type', 'sem', 'geom', 'bar', 'dodge', 0.7, 'width', 0.6);
g(2,2).stat_summary('type', 'sem', 'geom', 'errorbar', 'dodge', 0.7, 'width', 0.6, 'setylim', true);
g(2,2).axe_property('Xlim', [0 2], 'xtick', [], 'xticklabel', []); ;
g(2,2).set_title('MI(t,t+1)');
g(2,2).set_names('x', 'group', 'y', 'bits');
g(2,2).set_color_options('map', colormap_custom);
% draw
g.draw();
%% plot and perform statistical test on directed exploration
% in time
figure('Name', 'Directed exploration','units','normalized','position',[0 0.45 .45 .45]);
clear g;
x=repmat(1:ntrial-2,nsubj,1);
y=directed_exploration3;
color=repmat(cond_label,1,ntrial-2);
if plot_type==1
g(1,1) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(1,1) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(1,1).stat_summary('type', 'sem', 'geom', 'area', 'setylim', true);
g(1,1).set_title('DE3 in time');
g(1,1).set_names('x', 'trial', 'y', 'pattern frequency');
g(1,1).set_color_options('map', colormap_custom);
g(1,1).geom_hline('yintercept',mean(chance_DE3));
% average
y=nanmean(directed_exploration3,2);
color=cond_label;
x=ones(size(cond_label));
if plot_type==1
g(1,2) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(1,2) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(1,2).stat_summary('type', 'sem', 'geom', 'bar', 'dodge', 0.7, 'width', 0.6);
g(1,2).stat_summary('type', 'sem', 'geom', 'errorbar', 'dodge', 0.7, 'width', 0.6, 'setylim', true);
g(1,2).axe_property('Xlim', [0 2], 'xtick', [], 'xticklabel', []); ;
g(1,2).set_title('DE3 average');
g(1,2).set_names('x', 'group', 'y', 'frequency');
g(1,2).set_color_options('map', colormap_custom);
g(1,2).geom_hline('yintercept',mean(chance_DE3));
% DE4 in time
x=repmat(1:ntrial-3,nsubj,1);
y=directed_exploration4;
color=repmat(cond_label,1,ntrial-3);
if plot_type==1
g(2,1) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(2,1) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(2,1).stat_smooth();%'type', 'sem', 'geom', 'area', 'setylim', true);
g(2,1).set_title('DE4 in time');
g(2,1).set_names('x', 'trial', 'y', 'pattern frequency');
g(2,1).set_color_options('map', colormap_custom);
g(2,1).geom_hline('yintercept',mean(chance_DE4));
g(2,1).axe_property('ylim',[0 0.4]);
% DE4 average
y=nanmean(directed_exploration4,2);
color=cond_label;
x = ones(size(cond_label));
if plot_type==1
g(2,2) = gramm('x', x(:), 'y', y(:), 'color', color(:));
else
subset = repmat(ismember(cond_label, condition_list(condition_selection)),1,size(y,2));
g(2,2) = gramm('x', x(:), 'y', y(:), 'color', color(:), 'subset', subset(:));
end
g(2,2).stat_summary('type', 'sem', 'geom', 'bar', 'dodge', 0.7, 'width', 0.6);
g(2,2).stat_summary('type', 'sem', 'geom', 'errorbar', 'dodge', 0.7, 'width', 0.6, 'setylim', true);
g(2,2).axe_property('Xlim', [0 2], 'xtick', [], 'xticklabel', []); ;
g(2,2).set_title('DE4 average');
g(2,2).set_names('x', 'group', 'y', 'frequency');
g(2,2).set_color_options('map', colormap_custom);
g(2,2).geom_hline('yintercept',mean(chance_DE4));
% draw
g.draw();