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

DataCode_Fig1_Attentional_Capture_Image_Detectability.m DataCode_Fig1_Attentional_Capture_Image_Detectability.mat DataCode_FigS2_Attentional_Capture_Image_Detecability.mat .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. Au_Step06_0601_unit_2C.m Au_Step06_0601_unit_mC.m Train_DSVM_xilei.m Classify_DSVM.m svmclassify.m svmtrain_xilei.m .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. Note_Location_activeChannels_distanceTest.m Note_Location_activeChannels_distanceTest.mat Note_Location_activeChannels.mat .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. easy_ImgC.m .m Code (13) illustrate our method used to calculate imaginary coherence between responsive contacts. A Rayleigh Z correction was also performed and outputed. easy_visibility.m .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.