Investigation of effective connectivity of the illusory face detection network based on nonlinear dynamic causal models analysis
Jimin Liang · Journal of Xidian University · 2011
In order to extract the activation patterns of top-down face processing,the present study uses an experimental paradigm in which participants detect illusory faces in pure noise images.The nonlinear dynamic causal models(DCM) analysis,which has a perfect neural theory foundation,is used to investigate the effective connectivity of the illusory face detection network under the top-down processing mechanism.The optimal network model indicates that the occipital face area(OFA) serves as a key generator of illusory face detection.Under directing top-down visual attention exerted by the inferior parietal lobule(IPL),OFA searches for the pure noise images for face-like features,and then provides those face-like feature information to the fusiform face area(FFA) for further holistic face processing.