Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 07, 2018
- Source ID
- 10.7554/elife.32962
Entities
People
- Chris Baker
- Christopher Baldassano
- Diane M Beck
- Fei-Fei Li
- Iris Isabelle Anna Groen
- Michelle R Greene
Organizations
- Bates College
- Dutch Research Council
- National Institutes of Health
- New York University
- Office of Naval Research
- Princeton University
- Stanford University
- University of Illinois Urbana–Champaign