Computational mechanisms underlying cortical responses to the affordance properties of visual scenes.

ABSTRACT: Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and the factors that account for their success are unknown. Here we develop a set of techniques for using CNNs to gain insights into the computational mechanisms underlying cortical responses. We focused on responses in the occipital place area (OPA), a scene-selective region of dorsal occipitoparietal cortex. In a previous study, we showed that fMRI activation patterns in the OPA contain information about the navigational affordances of scenes; that is, information about where one can and cannot move within the immediate environment. We hypothesi

SUBMITTER: Bonner MF 

PROVIDER: S-EPMC5933806 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature