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The spatiotemporal neural dynamics of object location representations in the human brain

Author

Listed:
  • Monika Graumann

    (Freie Universität Berlin
    Humboldt-Universität zu Berlin)

  • Caterina Ciuffi

    (Freie Universität Berlin)

  • Kshitij Dwivedi

    (Freie Universität Berlin
    Goethe Universität)

  • Gemma Roig

    (Goethe Universität)

  • Radoslaw M. Cichy

    (Freie Universität Berlin
    Humboldt-Universität zu Berlin
    Bernstein Center for Computational Neuroscience Berlin)

Abstract

To interact with objects in complex environments, we must know what they are and where they are in spite of challenging viewing conditions. Here, we investigated where, how and when representations of object location and category emerge in the human brain when objects appear on cluttered natural scene images using a combination of functional magnetic resonance imaging, electroencephalography and computational models. We found location representations to emerge along the ventral visual stream towards lateral occipital complex, mirrored by gradual emergence in deep neural networks. Time-resolved analysis suggested that computing object location representations involves recurrent processing in high-level visual cortex. Object category representations also emerged gradually along the ventral visual stream, with evidence for recurrent computations. These results resolve the spatiotemporal dynamics of the ventral visual stream that give rise to representations of where and what objects are present in a scene under challenging viewing conditions.

Suggested Citation

  • Monika Graumann & Caterina Ciuffi & Kshitij Dwivedi & Gemma Roig & Radoslaw M. Cichy, 2022. "The spatiotemporal neural dynamics of object location representations in the human brain," Nature Human Behaviour, Nature, vol. 6(6), pages 796-811, June.
    • Handle: RePEc:nat:nathum:v:6:y:2022:i:6:d:10.1038_s41562-022-01302-0
    DOI: 10.1038/s41562-022-01302-0
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    References listed on IDEAS

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    1. Courtney J Spoerer & Tim C Kietzmann & Johannes Mehrer & Ian Charest & Nikolaus Kriegeskorte, 2020. "Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-27, October.
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