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Neural mechanisms of awareness of action.

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MATLAB · 456 lines · 20 KB · no license

  1. load('Y:\HNCT_AoA_Study\AoA_Subjects\allSessionRunAccuracyAwarenessConfidence.mat')
  2. load('Y:\HNCT_AoA_Study\AoA_Subjects\allSessionRunAccuracyUnawarenessConfidence.mat')
  4. % Example data with missing values
  5. timepoints = {'Run 1', 'Run 2', 'Run 3', 'Run 4', 'Run 5', 'Run 6'};
  6. std_calc_data = allSessionRunsUnawareness;
  7. % for row = 1:length(allSessionRunsConfidence)
  8. % allSessionRunsConfidence(row,:) = allSessionRunsConfidence(row,:)-allSessionRunsConfidence(row,1);
  9. % end
  10. % for row = 1:length(allSessionRunsAccuracy)
  11. % allSessionRunsAccuracy(row,:) = allSessionRunsAccuracy(row,:)-allSessionRunsAccuracy(row,1);
  12. % end
  13. % for row = 1:length(allSessionRunsUnawareness)
  14. % allSessionRunsUnawareness(row,:) = allSessionRunsUnawareness(row,:)-allSessionRunsUnawareness(row,1);
  15. % end
  16. %%
  17. types = {'Unawareness','Accuracy','Confidence','Awareness'};
  18. ylabels = {'Percentage Point Change From Run 1','Percentage Point Change From Run 1','Percentile Point Change From Run 1','Percentage Point Change From Run 1'};
  20. for day = 2:3
  21. for graph = 1:4
  22. if day == 2
  23. if graph == 1
  24. data = allSessionRunsUnawareness(strcmp(allSessionRunsDays,'Day2'),:);
  25. elseif graph == 2
  26. data = allSessionRunsAccuracy(strcmp(allSessionRunsDays,'Day2'),:);
  27. elseif graph == 3
  28. data = allSessionRunsConfidence(strcmp(allSessionRunsDays,'Day2'),:);
  29. elseif graph == 4
  30. data = allSessionRunsAwareness(strcmp(allSessionRunsDays,'Day2'),:);
  31. end
  32. elseif day == 3
  33. if graph == 1
  34. data = allSessionRunsUnawareness(strcmp(allSessionRunsDays,'Day3'),:);
  35. elseif graph == 2
  36. data = allSessionRunsAccuracy(strcmp(allSessionRunsDays,'Day3'),:);
  37. elseif graph == 3
  38. data = allSessionRunsConfidence(strcmp(allSessionRunsDays,'Day3'),:);
  39. elseif graph == 4
  40. data = allSessionRunsAwareness(strcmp(allSessionRunsDays,'Day3'),:);
  41. end
  42. end
  43. % Perform pairwise paired t-tests with pairwise complete case analysis
  44. p_values = nan(numel(timepoints), numel(timepoints));
  45. for i = 1:numel(timepoints)
  46. for j = 1:numel(timepoints)
  47. if i == j
  48. continue; % Skip if comparing the same timepoint
  49. end
  51. % Perform pairwise complete case analysis
  52. valid_indices = ~isnan(data(:, i)) & ~isnan(data(:, j));
  53. if any(valid_indices)
  54. x = data(valid_indices, i);
  55. y = data(valid_indices, j);
  56. [~, p_values(i, j)] = ttest(x, y, 'Alpha', 0.05);
  57. else
  58. p_values(i, j) = NaN; % Assign NaN if no complete cases exist
  59. end
  60. end
  61. end
  63. % Apply Benjamini-Hochberg procedure to p-values
  64. p_values_vector = reshape(p_values, [], 1);
  65. valid_p_values = p_values_vector(~isnan(p_values_vector)); % Exclude NaN p-values
  66. adjusted_p_values = zeros(size(p_values));
  67. adjusted_p_values(~isnan(p_values)) = mafdr(valid_p_values, 'BHFDR', true); % Apply Benjamini-Hochberg procedure
  69. % Display adjusted p-values
  70. fprintf('Pairwise paired t-test adjusted p-values (Benjamini-Hochberg procedure):\n');
  71. disp(adjusted_p_values);
  74. p_values_compared_to_run1 = mafdr( p_values(1,2:6), 'BHFDR', true);
  75. disp(['Adjusted p-values for run 1 tests only, ' types{graph} ])
  76. disp(p_values_compared_to_run1)
  79. dataNoNaN = data(valid_indices == 1,:);
  80. dataNoNaN = data;
  81. dataSEM1 = nanstd(std_calc_data(:,1))/sqrt(length(std_calc_data));
  82. dataSEM2 = nanstd(std_calc_data(:,2))/sqrt(length(std_calc_data));
  83. dataSEM3 = nanstd(std_calc_data(:,3))/sqrt(length(std_calc_data));
  84. dataSEM4 = nanstd(std_calc_data(:,4))/sqrt(length(std_calc_data));
  85. dataSEM5 = nanstd(std_calc_data(:,5))/sqrt(length(std_calc_data));
  86. dataSEM6 = nanstd(std_calc_data(:,6))/sqrt(length(std_calc_data));
  87. figure;
  88. hold on
  89. plot(nanmean(dataNoNaN),'LineWidth',3)
  90. errorbar(nanmean(dataNoNaN),[dataSEM1 dataSEM2 dataSEM3 dataSEM4 dataSEM5 dataSEM6],'linestyle','none','LineWidth',2)
  91. title(['Run ' types{graph} ' N = ' num2str(length(dataNoNaN)) ' Sessions, Day ' num2str(day)]);
  93. xlim([1 6])
  94. set(gca,'FontSize',24)
  95. ax = gca;
  96. ax.XTick = unique( round(ax.XTick) );
  97. xlabel('Run')
  98. ylabel('Percentage')
  99. if strcmp(types{graph},'Unawareness')
  100. ylim([10 25])
  101. elseif strcmp(types{graph},'Confidence')
  102. ylim([45 55])
  103. elseif strcmp(types{graph},'Accuracy')
  104. ylim([60 72])
  105. elseif strcmp(types{graph},'Awareness')
  106. ylim([10 30])
  107. end
  108. end
  109. end
  111. %%
  113. allSubjectSessionWeighted = [unique(allSessionRunsSubject)'; cell(4,length(unique(allSessionRunsSubject)))];
  114. for session = 1:length(allSessionRunsAccuracy)
  115. for subject = 1:length(allSubjectSessionWeighted)
  116. if strcmp(allSessionRunsSubject{session},allSubjectSessionWeighted{1,subject})
  117. allSubjectSessionWeighted{2,subject} = [allSubjectSessionWeighted{2,subject}; allSessionRunsAccuracy(session,:)];
  118. allSubjectSessionWeighted{3,subject} = [allSubjectSessionWeighted{3,subject}; allSessionRunsConfidence(session,:)];
  119. allSubjectSessionWeighted{4,subject} = [allSubjectSessionWeighted{4,subject}; allSessionRunsUnawareness(session,:)];
  120. allSubjectSessionWeighted{5,subject} = [allSubjectSessionWeighted{5,subject}; allSessionRunsAwareness(session,:)];
  121. end
  122. end
  123. end
  125. twoDayMeanAccuracy = [];
  126. twoDayMeanConfidence = [];
  127. twoDayMeanUnawareness = [];
  128. twoDayMeanAwareness = [];
  129. for subject = 1:length(allSubjectSessionWeighted)
  131. if size(allSubjectSessionWeighted{2,subject},1) == 2
  132. twoDayMeanAccuracy = [twoDayMeanAccuracy; mean(allSubjectSessionWeighted{2,subject})];
  133. twoDayMeanConfidence = [twoDayMeanConfidence; mean(allSubjectSessionWeighted{3,subject})];
  134. twoDayMeanUnawareness = [twoDayMeanUnawareness; mean(allSubjectSessionWeighted{4,subject})];
  135. twoDayMeanAwareness = [twoDayMeanAwareness; mean(allSubjectSessionWeighted{5,subject})];
  136. elseif size(allSubjectSessionWeighted{2,subject},1) == 1
  137. twoDayMeanAccuracy = [twoDayMeanAccuracy; allSubjectSessionWeighted{2,subject}];
  138. twoDayMeanConfidence = [twoDayMeanConfidence; allSubjectSessionWeighted{3,subject}];
  139. twoDayMeanUnawareness = [twoDayMeanUnawareness; allSubjectSessionWeighted{4,subject}];
  140. twoDayMeanAwareness = [twoDayMeanAwareness; allSubjectSessionWeighted{5,subject}];
  141. end
  142. end
  144. confidence_level = 0.95;
  147. semRun1 = nanstd(twoDayMeanAccuracy(:,1))/sqrt(length(twoDayMeanAccuracy));
  148. semRun2 = nanstd(twoDayMeanAccuracy(:,2))/sqrt(length(twoDayMeanAccuracy));
  149. semRun3 = nanstd(twoDayMeanAccuracy(:,3))/sqrt(length(twoDayMeanAccuracy));
  150. semRun4 = nanstd(twoDayMeanAccuracy(:,4))/sqrt(length(twoDayMeanAccuracy));
  151. semRun5 = nanstd(twoDayMeanAccuracy(:,5))/sqrt(length(twoDayMeanAccuracy));
  152. semRun6 = nanstd(twoDayMeanAccuracy(:,6))/sqrt(length(twoDayMeanAccuracy));
  153. figure;
  154. hold on;
  155. plot(1:6,nanmean(twoDayMeanAccuracy),'LineWidth',3,'Color','blue')
  156. errorbar(nanmean(twoDayMeanAccuracy),[semRun1 semRun2 semRun3 semRun4 semRun5 semRun6],'linestyle','none','LineWidth',3,'Color','red')
  157. set(gca,'FontSize',24)
  158. xlim([1 6])
  159. ylim([60 72])
  160. ax = gca;
  161. ax.XTick = unique( round(ax.XTick) );
  162. xlabel(['Run'])
  163. ylabel('Correct Percentage')
  164. title(['Subject Session Accuracy Averages, N = ' num2str(length(twoDayMeanAccuracy))])
  166. semRun1 = nanstd(twoDayMeanConfidence(:,1))/sqrt(length(twoDayMeanConfidence));
  167. semRun2 = nanstd(twoDayMeanConfidence(:,2))/sqrt(length(twoDayMeanConfidence));
  168. semRun3 = nanstd(twoDayMeanConfidence(:,3))/sqrt(length(twoDayMeanConfidence));
  169. semRun4 = nanstd(twoDayMeanConfidence(:,4))/sqrt(length(twoDayMeanConfidence));
  170. semRun5 = nanstd(twoDayMeanConfidence(:,5))/sqrt(length(twoDayMeanConfidence));
  171. semRun6 = nanstd(twoDayMeanConfidence(:,6))/sqrt(length(twoDayMeanConfidence));
  174. critical_run1 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanConfidence(:,1)))-1);
  175. critical_run2 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanConfidence(:,2)))-1);
  176. critical_run3 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanConfidence(:,3)))-1);
  177. critical_run4 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanConfidence(:,4)))-1);
  178. critical_run5 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanConfidence(:,5)))-1);
  179. critical_run6 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanConfidence(:,6)))-1);
  181. ci95 = [critical_run1*semRun1 critical_run2*semRun2 critical_run3*semRun3 critical_run4*semRun4 critical_run5*semRun5 critical_run6*semRun6];
  183. figure;
  184. hold on;
  185. plot(1:6,nanmean(twoDayMeanConfidence),'LineWidth',3,'Color','b')
  186. % plot(1:6,nanmean(twoDayMeanConfidence)+ci95,'Color','b','LineStyle','--')
  187. % plot(1:6,nanmean(twoDayMeanConfidence)-ci95,'Color','b','LineStyle','--')
  190. % plot(1:6,nanmean(twoDayMeanConfidence)+[semRun1 semRun2 semRun3 semRun4 semRun5 semRun6],'Color','b','LineStyle','--')
  191. % plot(1:6,nanmean(twoDayMeanConfidence)-[semRun1 semRun2 semRun3 semRun4 semRun5 semRun6],'Color','b','LineStyle','--')
  192. errorbar(nanmean(twoDayMeanConfidence),[semRun1 semRun2 semRun3 semRun4 semRun5 semRun6],'linestyle','none','LineWidth',3,'Color','r')
  193. scatter([4 5 6], [52 52 52],256,'*','black','LineWidth',3)
  194. set(gca,'FontSize',24)
  195. xlim([1 6])
  196. ylim([45 55])
  197. ax = gca;
  198. ax.XTick = unique( round(ax.XTick) );
  199. xlabel(['Run'])
  200. ylabel('Confidence Percentile')
  201. title(['Subject Session Confidence Averages, N = ' num2str(length(twoDayMeanConfidence))])
  203. semRun1 = nanstd(twoDayMeanUnawareness(:,1))/sqrt(length(twoDayMeanUnawareness));
  204. semRun2 = nanstd(twoDayMeanUnawareness(:,2))/sqrt(length(twoDayMeanUnawareness));
  205. semRun3 = nanstd(twoDayMeanUnawareness(:,3))/sqrt(length(twoDayMeanUnawareness));
  206. semRun4 = nanstd(twoDayMeanUnawareness(:,4))/sqrt(length(twoDayMeanUnawareness));
  207. semRun5 = nanstd(twoDayMeanUnawareness(:,5))/sqrt(length(twoDayMeanUnawareness));
  208. semRun6 = nanstd(twoDayMeanUnawareness(:,6))/sqrt(length(twoDayMeanUnawareness));
  209. figure;
  210. hold on;
  211. plot(1:6,nanmean(twoDayMeanUnawareness),'LineWidth',3,'Color','b')
  212. errorbar(nanmean(twoDayMeanUnawareness),[semRun1 semRun2 semRun3 semRun4 semRun5 semRun6],'linestyle','none','LineWidth',3,'Color','r')
  213. scatter([5 6], [23 23],256,'*','black','LineWidth',3)
  214. set(gca,'FontSize',24)
  215. xlim([1 6])
  216. ylim([10 25])
  217. ax = gca;
  218. ax.XTick = unique( round(ax.XTick) );
  219. xlabel(['Run'])
  220. ylabel('Unawareness Percentage')
  221. title(['Subject Session Unawareness Averages, N = ' num2str(length(twoDayMeanUnawareness))])
  223. semRun1 = nanstd(twoDayMeanAwareness(:,1))/sqrt(length(twoDayMeanAwareness));
  224. semRun2 = nanstd(twoDayMeanAwareness(:,2))/sqrt(length(twoDayMeanAwareness));
  225. semRun3 = nanstd(twoDayMeanAwareness(:,3))/sqrt(length(twoDayMeanAwareness));
  226. semRun4 = nanstd(twoDayMeanAwareness(:,4))/sqrt(length(twoDayMeanAwareness));
  227. semRun5 = nanstd(twoDayMeanAwareness(:,5))/sqrt(length(twoDayMeanAwareness));
  228. semRun6 = nanstd(twoDayMeanAwareness(:,6))/sqrt(length(twoDayMeanAwareness));
  231. critical_run1 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanAwareness(:,1)))-1);
  232. critical_run2 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanAwareness(:,2)))-1);
  233. critical_run3 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanAwareness(:,3)))-1);
  234. critical_run4 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanAwareness(:,4)))-1);
  235. critical_run5 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanAwareness(:,5)))-1);
  236. critical_run6 = tinv((1+confidence_level)/2,sum(~isnan(twoDayMeanAwareness(:,6)))-1);
  238. ci95 = [critical_run1*semRun1 critical_run2*semRun2 critical_run3*semRun3 critical_run4*semRun4 critical_run5*semRun5 critical_run6*semRun6];
  240. figure;
  241. hold on;
  242. plot(1:6,nanmean(twoDayMeanAwareness),'LineWidth',3,'Color','b')
  243. % plot(1:6,nanmean(twoDayMeanAwareness)+ci95,'Color','b','LineStyle','--')
  244. % plot(1:6,nanmean(twoDayMeanAwareness)-ci95,'Color','b','LineStyle','--')
  247. % plot(1:6,nanmean(twoDayMeanAwareness)+[semRun1 semRun2 semRun3 semRun4 semRun5 semRun6],'Color','b','LineStyle','--')
  248. % plot(1:6,nanmean(twoDayMeanAwareness)-[semRun1 semRun2 semRun3 semRun4 semRun5 semRun6],'Color','b','LineStyle','--')
  249. errorbar(nanmean(twoDayMeanAwareness),[semRun1 semRun2 semRun3 semRun4 semRun5 semRun6],'linestyle','none','LineWidth',3,'Color','r')
  250. set(gca,'FontSize',24)
  251. xlim([1 6])
  252. ylim([15 30])
  253. scatter([2 4 6], [28 28 28],256,'*','black','LineWidth',3)
  254. ax = gca;
  255. ax.XTick = unique( round(ax.XTick) );
  256. xlabel(['Run'])
  257. ylabel('Awareness Percentage')
  258. title(['Subject Session Awareness Averages, N = ' num2str(length(twoDayMeanAwareness))])
  260. %%
  262. pvalue_vector_awareness = [];
  263. pvalue_vector_awareness_signrank = [];
  264. for test = 2:6
  265. [h,p] = ttest(twoDayMeanAwareness(:,1),twoDayMeanAwareness(:,test));
  266. pvalue_vector_awareness = [pvalue_vector_awareness p];
  267. p = signrank(twoDayMeanAwareness(:,1),twoDayMeanAwareness(:,test));
  268. pvalue_vector_awareness_signrank = [pvalue_vector_awareness_signrank p];
  269. end
  271. pvalues_to_run_1_awareness = mafdr( pvalue_vector_unawareness, 'BHFDR', true)
  273. pvalues_to_run_1_signrank_awareness = mafdr( pvalue_vector_awareness_signrank, 'BHFDR', true)
  274. %%
  276. pvalue_vector_unawareness = [];
  277. pvalue_vector_unawareness_signrank = [];
  278. for test = 2:6
  279. [h,p] = ttest(twoDayMeanUnawareness(:,1),twoDayMeanUnawareness(:,test));
  280. pvalue_vector_unawareness = [pvalue_vector_unawareness p];
  281. p = signrank(twoDayMeanUnawareness(:,1),twoDayMeanUnawareness(:,test));
  282. pvalue_vector_unawareness_signrank = [pvalue_vector_unawareness_signrank p];
  283. end
  285. pvalues_to_run_1 = mafdr( pvalue_vector_unawareness, 'BHFDR', true)
  287. pvalues_to_run_1_signrank_unawareness = mafdr( pvalue_vector_unawareness_signrank, 'BHFDR', true)
  289. %%
  290. pvalue_vector_confidence = [];
  291. pvalue_vector_unawareness_signrank = [];
  292. for test = 2:6
  293. [h,p] = ttest(twoDayMeanConfidence(:,1),twoDayMeanConfidence(:,test));
  294. pvalue_vector_confidence = [pvalue_vector_confidence p];
  295. p = signrank(twoDayMeanConfidence(:,1),twoDayMeanConfidence(:,test));
  296. pvalue_vector_unawareness_signrank = [pvalue_vector_unawareness_signrank p];
  297. end
  299. pvalues_to_run_1_confidence = mafdr( pvalue_vector_confidence, 'BHFDR', true)
  301. pvalues_to_run_1_signrank_confidence = mafdr( pvalue_vector_unawareness_signrank, 'BHFDR', true)
  303. %%
  306. pvalue_vector_accuracy = [];
  307. pvalue_vector_accuracy_signrank = [];
  308. for test = 2:6
  309. [h,p] = ttest(twoDayMeanAccuracy(:,1),twoDayMeanAccuracy(:,test));
  310. pvalue_vector_accuracy = [pvalue_vector_accuracy p];
  311. p = signrank(twoDayMeanAccuracy(:,1),twoDayMeanAccuracy(:,test));
  312. pvalue_vector_accuracy_signrank = [pvalue_vector_accuracy_signrank p];
  313. end
  315. pvalues_to_run_1_accuracy = mafdr( pvalue_vector_accuracy, 'BHFDR', true)
  317. pvalues_to_run_1_signrank_accuracy = mafdr( pvalue_vector_accuracy_signrank, 'BHFDR', true)
  320. %%
  322. allSessionRunPearsonsAccuracy = [];
  323. for session = 1:128
  325. currentCoeff = corrcoef(1:6,allSessionRunsAccuracy(session,:));
  326. allSessionRunPearsonsAccuracy = [allSessionRunPearsonsAccuracy currentCoeff(2)];
  327. end
  328. figure
  329. histogram(allSessionRunPearsonsAccuracy)
  330. title('Accuracy')
  331. xlabel('Pearson correlation coefficient')
  333. allSessionRunPearsonsAwareness = [];
  334. for session = 1:128
  336. currentCoeff = corrcoef(1:6,allSessionRunsAwareness(session,:));
  337. allSessionRunPearsonsAwareness = [allSessionRunPearsonsAwareness currentCoeff(2)];
  338. end
  339. figure
  340. histogram(allSessionRunPearsonsAwareness)
  341. title('Awareness')
  342. xlabel('Pearson correlation coefficient')
  344. allSessionRunPearsonsConfidence = [];
  345. for session = 1:128
  347. currentCoeff = corrcoef(1:6,allSessionRunsConfidence(session,:));
  348. allSessionRunPearsonsConfidence = [allSessionRunPearsonsConfidence currentCoeff(2)];
  349. end
  350. figure
  351. histogram(allSessionRunPearsonsConfidence)
  352. title('Confidence')
  353. xlabel('Pearson correlation coefficient')
  355. allSessionRunPearsonsUnawareness = [];
  356. for session = 1:128
  358. currentCoeff = corrcoef(1:6,allSessionRunsUnawareness(session,:));
  359. allSessionRunPearsonsUnawareness = [allSessionRunPearsonsUnawareness currentCoeff(2)];
  360. end
  361. figure
  362. histogram(allSessionRunPearsonsUnawareness)
  363. title('Unawareness')
  364. xlabel('Pearson correlation coefficient')
  366. %%
  367. full_table = [];
  368. for subject = 1:length(allSubjectSessionWeighted)
  369. currentSubjectUnawareness = twoDayMeanUnawareness(subject,:);
  370. currentSubjectAwareness = twoDayMeanAwareness(subject,:);
  371. currentSubjectConfidence = twoDayMeanConfidence(subject,:);
  372. currentSubjectAccuracy = twoDayMeanAccuracy(subject,:);
  373. for run = 1:6
  374. if ~isnan(twoDayMeanAccuracy(subject,run))
  375. currentRow = [twoDayMeanAccuracy(subject,run) twoDayMeanConfidence(subject,run) twoDayMeanAwareness(subject,run) twoDayMeanUnawareness(subject,run) run subject];
  377. end
  378. full_table = [full_table; currentRow];
  379. end
  380. end
  382. full_table = array2table(full_table);
  383. full_table.Properties.VariableNames = {'Accuracy','Confidence','Awareness','Unawareness','Run','Subject'};
  386. % % Convert 'subject' and 'run' to categorical if they are not already
  387. % full_table.Subject = categorical(full_table.Subject);
  388. % full_table.Run = categorical(full_table.Run);
  389. %
  390. % % Fit the repeated measures model
  391. % rm = fitrm(full_table, 'Unawareness ~ Run', 'WithinDesign', full_table.Run, 'WithinModel', 'Run');
  392. %
  393. % % Run the repeated measures ANOVA
  394. % ranovatbl = ranova(rm);
  395. %
  396. % % Display the results
  397. % disp(ranovatbl);
  400. %%
  403. dataTable = array2table(twoDayMeanUnawareness);
  404. dataTable.Properties.VariableNames = {'Run_1','Run_2','Run_3','Run_4','Run_5','Run_6'};
  405. rm = fitrm(dataTable,'Run_1-Run_6')
  406. rm_coeffs_unawareness = rm.Coefficients;
  407. ranovaResultsUnawareness = ranova(rm)
  411. dataTable = array2table(twoDayMeanAwareness);
  412. dataTable.Properties.VariableNames = {'Run_1','Run_2','Run_3','Run_4','Run_5','Run_6'};
  413. rm = fitrm(dataTable,'Run_1-Run_6~1')
  414. rm_coeffs_awareness = rm.Coefficients;
  415. ranovaResultsAwareness = ranova(rm)
  418. dataTable = array2table(twoDayMeanConfidence);
  419. dataTable.Properties.VariableNames = {'Run_1','Run_2','Run_3','Run_4','Run_5','Run_6'};
  420. rm = fitrm(dataTable,'Run_1-Run_6~1')
  421. rm_coeffs_confidence = rm.Coefficients;
  422. ranovaResultsConfidence = ranova(rm)
  427. dataTable = array2table(twoDayMeanAccuracy);
  428. dataTable.Properties.VariableNames = {'Run_1','Run_2','Run_3','Run_4','Run_5','Run_6'};
  429. rm = fitrm(dataTable,'Run_1-Run_6~1')
  430. rm_coeffs_accuracy = rm.Coefficients;
  431. ranovaResultsAccuracy = ranova(rm)
  433. %%
  435. [RHO,PVAL] = corr([nanmean(twoDayMeanAccuracy(:,1)) nanmean(twoDayMeanAccuracy(:,2)) nanmean(twoDayMeanAccuracy(:,3)) nanmean(twoDayMeanAccuracy(:,4)) nanmean(twoDayMeanAccuracy(:,5)) nanmean(twoDayMeanAccuracy(:,6))]',[1 2 3 4 5 6]','Type','Spearman')
  436. accuracyColumn = [nanmean(twoDayMeanAccuracy)'; RHO; PVAL];
  438. [RHO,PVAL] = corr([nanmean(twoDayMeanConfidence(:,1)) nanmean(twoDayMeanConfidence(:,2)) nanmean(twoDayMeanConfidence(:,3)) nanmean(twoDayMeanConfidence(:,4)) nanmean(twoDayMeanConfidence(:,5)) nanmean(twoDayMeanConfidence(:,6))]',[1 2 3 4 5 6]','Type','Spearman')
  439. confidenceColumn = [nanmean(twoDayMeanConfidence)'; RHO; PVAL];
  441. [RHO,PVAL] = corr([nanmean(twoDayMeanAwareness(:,1)) nanmean(twoDayMeanAwareness(:,2)) nanmean(twoDayMeanAwareness(:,3)) nanmean(twoDayMeanAwareness(:,4)) nanmean(twoDayMeanAwareness(:,5)) nanmean(twoDayMeanAwareness(:,6))]',[1 2 3 4 5 6]','Type','Spearman')
  442. awarenessColumn = [nanmean(twoDayMeanAwareness)'; RHO; PVAL];
  444. [RHO,PVAL] = corr([nanmean(twoDayMeanUnawareness(:,1)) nanmean(twoDayMeanUnawareness(:,2)) nanmean(twoDayMeanUnawareness(:,3)) nanmean(twoDayMeanUnawareness(:,4)) nanmean(twoDayMeanUnawareness(:,5)) nanmean(twoDayMeanUnawareness(:,6))]',[1 2 3 4 5 6]','Type','Spearman')
  445. unawarenessColumn = [nanmean(twoDayMeanUnawareness)'; RHO; PVAL];
  447. master_column = round([accuracyColumn confidenceColumn awarenessColumn unawarenessColumn],2);
  449. master_column = array2table(master_column);
  450. master_column.Properties.VariableNames = {'Accuracy','Confidence','Awareness','Unawareness'}
  452. % Assuming you have a table T
  453. % Display table within a figure window
  454. figure;
  455. uitable('Data', master_column{:,:}, 'ColumnName', master_column.Properties.VariableNames,'RowName',{'Run 1','Run 2','Run 3','Run 4','Run 5','Run 6','ρ','p-value'}, 'Units', 'Normalized', 'Position', [0, 0, 1, 1]);
  456. title('Table Visualization');

analyze_run_changes_stats.m at commit 8a9aa7a, no license · at the source

Overview

Authors: David S Jin1,2, Oumayma Agdali1,2, Taruna Yadav1, Sharif I Kronemer1,2, Sydney Kunkler1, Shweta Majumder1, Maya Khurana1, Marie McCusker1,2, Ivory Fu1, Emily J Siff1,2, Aya Khalaf1, Kate L Christison-Lagay1, Shanae L Aerts1,2, Qilong Xin1,2, Jing-Jing Li1, Sarah H McGill1,2, Michael J Crowley3, Hal Blumenfeld1,2,4,5
ORCID iDs: Oumayma Agdali, Taruna Yadav, Marie McCusker, Emily J Siff, Shanae L Aerts, Hal Blumenfeld
  1. Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, USA
  2. Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
  3. Child Study Center, Yale University School of Medicine, New Haven, CT 06519, USA
  4. Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA
  5. Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06520, USA
Institutions: Yale University (United States)
Journal: PNAS nexus, volume 5, issue 7, article pgag220
Dates: received 12 August 2025; accepted 11 May 2026; published online 2 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/pnasnexus/pgag220 · PMID 42416922 · PMCID PMC13340929 · OpenAlex W4401691334
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.08.15.608153
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Keywords: volition, consciousness, attention, perception, event-related potentials
Journal subjects: Biological, Health, and Medical Sciences, Neuroscience
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: NIH (R01 NS134655); Mark Loughridge; Michele Williams Foundation; Betsy; Jonathan Blattmachr Family
Citations: not cited yet (Europe PMC); 109 references in the paper

Abstract

Awareness of action (AoA), or conscious awareness of an action just performed, is an important part of daily experience with major practical and ethical relevance, yet the neural mechanisms of AoA remain largely unknown. The main barrier to studying AoA is a lack of experimental paradigms to directly compare neural activity in aware versus unaware actions. Borrowing from the field of perceptual awareness, where exciting progress has been made by contrastive analysis of aware versus unaware stimuli, we developed a game where participants repeatedly perform nearly identical moves while engaged in a distractor task, and the participants then report awareness or unawareness of the moves they just performed. We found that on short timescales, aware actions had larger neurophysiological signals both preceding and following movement. The differences included both volitional and perceptual event-related potentials (premovement positivity, N140, and P300), as well as frontal midline theta, event-related alpha/beta desynchronization, and postmove blink rates. On longer time scales, we identified a novel positive event-related potential only preceding unaware moves and found behavioral and pupillometric evidence for decreased attention and arousal over minutes concurrent with AoA loss. Our findings reveal three neural mechanisms that may synergistically contribute to AoA: (i) long-term increases in arousal/attentional state at time of action; (ii) increased action-related motor volitional signals; and (iii) increased action-related sensory perceptual signals. Deeper understanding of AoA may ultimately elucidate the causes of variable AoA in daily life and lead to better treatments for impaired AoA in neuropsychiatric disorders.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

BlumenfeldLab/Jin-et-al_2026

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8a9aa7a594d1ff89398980f183f74bcdbea26b73, 4 June 2026
Languages: MATLAB (82)
Size: 89 files, 82 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (24 files), Signal Processing Toolbox (15 files), EEGLAB (12 files), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
83 files
At the source: github.com/BlumenfeldLab/Jin-et-al_2026/

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 82 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

All anonymized data created for the study are available in the Yale Dataverse: https://dataverse.yale.edu/dataverse/aoa/. All code used in this project are in the following GitHub repository: https://github.com/BlumenfeldLab/Jin-et-al_2026/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 5 keywords, 5 funders, 102 references.

Cite

This paper

Jin, D. S., Agdali, O., Yadav, T., Kronemer, S. I., Kunkler, S., Majumder, S., Khurana, M., McCusker, M., Fu, I., Siff, E. J., Khalaf, A., Christison-Lagay, K. L., Aerts, S. L., Xin, Q., Li, J.-J., McGill, S. H., Crowley, M. J., & Blumenfeld, H. (2026). Neural mechanisms of awareness of action. PNAS nexus, 5(7), pgag220. https://doi.org/10.1093/pnasnexus/pgag220

BibTeX

@article{jin2026neural,
author = {Jin, David S and Agdali, Oumayma and Yadav, Taruna and Kronemer, Sharif I and Kunkler, Sydney and Majumder, Shweta and Khurana, Maya and McCusker, Marie and Fu, Ivory and Siff, Emily J and Khalaf, Aya and Christison-Lagay, Kate L and Aerts, Shanae L and Xin, Qilong and Li, Jing-Jing and McGill, Sarah H and Crowley, Michael J and Blumenfeld, Hal},
title = {{Neural mechanisms of awareness of action}},
journal = {PNAS nexus},
year = {2026},
month = jul,
volume = {5},
number = {7},
pages = {pgag220},
publisher = {Oxford University Press},
issn = {2752-6542},
doi = {10.1093/pnasnexus/pgag220},
url = {https://doi.org/10.1093/pnasnexus/pgag220},
pmid = {42416922},
pmcid = {PMC13340929}
}

RIS

TY - JOUR
AU - Jin, David S
AU - Agdali, Oumayma
AU - Yadav, Taruna
AU - Kronemer, Sharif I
AU - Kunkler, Sydney
AU - Majumder, Shweta
AU - Khurana, Maya
AU - McCusker, Marie
AU - Fu, Ivory
AU - Siff, Emily J
AU - Khalaf, Aya
AU - Christison-Lagay, Kate L
AU - Aerts, Shanae L
AU - Xin, Qilong
AU - Li, Jing-Jing
AU - McGill, Sarah H
AU - Crowley, Michael J
AU - Blumenfeld, Hal
TI - Neural mechanisms of awareness of action
T2 - PNAS nexus
J2 - PNAS Nexus
PY - 2026
DA - 2026/07/02
VL - 5
IS - 7
SP - pgag220
SN - 2752-6542
PB - Oxford University Press
DO - 10.1093/pnasnexus/pgag220
UR - https://doi.org/10.1093/pnasnexus/pgag220
LA - en
ER -

CSL-JSON

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"id": "10.1093/pnasnexus/pgag220",
"type": "article-journal",
"title": "Neural mechanisms of awareness of action",
"container-title": "PNAS nexus",
"author": [
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"family": "Jin",
"given": "David S"
},
{
"family": "Agdali",
"given": "Oumayma"
},
{
"family": "Yadav",
"given": "Taruna"
},
{
"family": "Kronemer",
"given": "Sharif I"
},
{
"family": "Kunkler",
"given": "Sydney"
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{
"family": "Majumder",
"given": "Shweta"
},
{
"family": "Khurana",
"given": "Maya"
},
{
"family": "McCusker",
"given": "Marie"
},
{
"family": "Fu",
"given": "Ivory"
},
{
"family": "Siff",
"given": "Emily J"
},
{
"family": "Khalaf",
"given": "Aya"
},
{
"family": "Christison-Lagay",
"given": "Kate L"
},
{
"family": "Aerts",
"given": "Shanae L"
},
{
"family": "Xin",
"given": "Qilong"
},
{
"family": "Li",
"given": "Jing-Jing"
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{
"family": "McGill",
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{
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{
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],
"container-title-short": "PNAS Nexus",
"volume": "5",
"issue": "7",
"page": "pgag220",
"DOI": "10.1093/pnasnexus/pgag220",
"PMID": "42416922",
"PMCID": "PMC13340929",
"ISSN": "2752-6542",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/pnasnexus/pgag220",
"language": "en",
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"date-parts": [
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2026,
7,
2
]
]
}
}

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