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Direct perception of affective valence from vision

Author

Listed:
  • Saeedeh Sadeghi

    (Cornell University
    California Institute of Technology)

  • Zijin Gu

    (Cornell University and Cornell Tech)

  • Eve Rosa

    (Cornell University)

  • Amy Kuceyeski

    (Weill Cornell Medicine
    Cornell University)

  • Adam K. Anderson

    (Cornell University)

Abstract

Subjective feelings are thought to arise from conceptual and bodily states. We examine whether the valence of feelings may also be decoded directly from objective ecological statistics of the visual environment. We train a visual valence (VV) machine learning model of low-level image statistics on nearly 8000 emotionally charged photographs. The VV model predicts human valence ratings of images and transfers even more robustly to abstract paintings. In human observers, limiting conceptual analysis of images enhances VV contributions to valence experience, increasing correspondence with machine perception of valence. In the brain, VV resides in lower to mid-level visual regions, where neural activity submitted to deep generative networks synthesizes new images containing positive versus negative VV. There are distinct modes of valence experience, one derived indirectly from meaning, and the other embedded in ecological statistics, affording direct perception of subjective valence as an apparent objective property of the external world.

Suggested Citation

  • Saeedeh Sadeghi & Zijin Gu & Eve Rosa & Amy Kuceyeski & Adam K. Anderson, 2024. "Direct perception of affective valence from vision," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53668-6
    DOI: 10.1038/s41467-024-53668-6
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    References listed on IDEAS

    as
    1. Li Wang & Sarah Gillis-Smith & Yueqing Peng & Juen Zhang & Xiaoke Chen & C. Daniel Salzman & Nicholas J. P. Ryba & Charles S. Zuker, 2018. "The coding of valence and identity in the mammalian taste system," Nature, Nature, vol. 558(7708), pages 127-131, June.
    2. Kiyohito Iigaya & Sanghyun Yi & Iman A. Wahle & Koranis Tanwisuth & John P. O’Doherty, 2021. "Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features," Nature Human Behaviour, Nature, vol. 5(6), pages 743-755, June.
    3. L Jack Rhodes & Matthew Ríos & Jacob Williams & Gonzalo Quiñones & Prahalada K Rao & Vladimir Miskovic, 2019. "The role of low-level image features in the affective categorization of rapidly presented scenes," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-17, May.
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