Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior
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.
Article and author information
National Institutes of Health (ZIAMH002909)
- Iris IA Groen
- Chris I Baker
Netherlands Organization for Scientific Research (Rubicon Fellowship)
- Iris IA Groen
Office of Naval Research (Multidisciplinary Research Initiative Grant N000141410671)
- Li Fei-Fei
- Diane M Beck
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: All participants had normal or corrected-to-normal vision and gave written informed consent as part of the study protocol (93 M-0170, NCT00001360) prior to participation in the study. The study was approved by the Institutional Review Board of the National Institutes of Health and was conducted according to the Declaration of Helsinki.
- Doris Y Tsao, California Institute of Technology, United States
- Received: October 19, 2017
- Accepted: March 2, 2018
- Accepted Manuscript published: March 7, 2018 (version 1)
- Version of Record published: March 20, 2018 (version 2)
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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