Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior

eLife

Michelle R Greene¹  Chris I Baker²  Iris IA Groen³  Christopher Baldassano⁴  Li Fei-Fei⁵  Diane M Beck⁶ 

[1] Department of Psychology, New York University, New York City, United States;Department of Psychology, University of Illinois, Urbana-Champaign, United States;Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, United States;Neuroscience Program, Bates College, Maine, United States;Princeton Neuroscience Institute, Princeton University, Princeton, United States;Stanford Vision Lab, Stanford University, Stanford, United States;

关键词: scene perception;    variance partitioning;    behavioral categorization;    deep neural network;    computational model;    fMRI;   

DOI  :  10.7554/eLife.32962

来源: DOAJ

【 摘 要 】

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

【 授权许可】

Unknown