Attention-based frontal-posterior coupling for visual consciousness in the human brain

<ol> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.m</li> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.mat</li> <li>DataCode_FigS2_Attentional_Capture_Image_Detecability.mat <ul> <li>.m Code (1) using .mat Data (2 and 3) illustrate main behavioral findings in our manuscript. Panel figures shown in Figure.1 and Figure.S2 could be well replicated using these materials.<br><br></li> </ul> </li> <li>Au_Step06_0601_unit_2C.m</li> <li>Au_Step06_0601_unit_mC.m</li> <li>Train_DSVM_xilei.m</li> <li>Classify_DSVM.m</li> <li>svmclassify.m</li> <li>svmtrain_xilei.m <ul> <li>.m Code (4) and .m code (5) using child .m functions (6, 7, 8 and 9) illustrate core codes used to discriminate neural pattern differences on a 2-class issue (image presence versus image absence) or a 3-class issue (animal, object or face), respectively.&nbsp;</li> </ul> </li> <li>Note_Location_activeChannels_distanceTest.m</li> <li>Note_Location_activeChannels_distanceTest.mat</li> <li>Note_Location_activeChannels.mat <ul> <li>.m Code (10) using .mat Data (11 and 12) illustrate our method used to calculate distance between responsive contacts. Based on that, we also made a statistical inference against a chance-level distribution. Panel figure shown in Figure.2F could be well replicated using these materials.<br><br></li> </ul> </li> <li>easy_ImgC.m <ul> <li>.m Code (13) illustrate our method used to calculate imaginary coherence between responsive contacts. A Rayleigh Z correction was also performed and outputed.<br><br></li> </ul> </li> <li>easy_visibility.m <ul> <li>.m Code (14) illustrate our method used to calculate an index of visibility from which measures of interest tied to an invisible image was subtracted from that of a visible image.&nbsp;</li> </ul> </li> </ol> <p>&nbsp;</p>