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

  1. Iris IA Groen  Is a corresponding author
  2. Michelle R Greene
  3. Christopher Baldassano
  4. Li Fei-Fei
  5. Diane M Beck
  6. Chris I Baker
  1. National Institutes of Health, United States
  2. Bates College, United States
  3. Princeton University, United States
  4. Stanford University, United States
  5. University of Illinois at Urbana-Champaign, United States

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.

Article and author information

Author details

  1. Iris IA Groen

    Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, United States
    For correspondence
    iris.groen@nih.gov
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5536-6128
  2. Michelle R Greene

    Neuroscience Program, Bates College, Lewiston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Christopher Baldassano

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3540-5019
  4. Li Fei-Fei

    Stanford Vision Lab, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Diane M Beck

    Department of Psychology, University of Illinois at Urbana-Champaign, Urbana-Champaign, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Chris I Baker

    Laboratory of Brain and Cognition, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6861-8964

Funding

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.

Reviewing Editor

  1. Doris Y Tsao, California Institute of Technology, United States

Ethics

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.

Version history

  1. Received: October 19, 2017
  2. Accepted: March 2, 2018
  3. Accepted Manuscript published: March 7, 2018 (version 1)
  4. Version of Record published: March 20, 2018 (version 2)

Copyright

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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  1. Iris IA Groen
  2. Michelle R Greene
  3. Christopher Baldassano
  4. Li Fei-Fei
  5. Diane M Beck
  6. Chris I Baker
(2018)
Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior
eLife 7:e32962.
https://doi.org/10.7554/eLife.32962

Share this article

https://doi.org/10.7554/eLife.32962

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