September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
The neural dynamics of natural action understanding
Author Affiliations & Notes
  • Diana C Dima
    Western University
    Vector Institute for Artificial Intelligence
  • Jody C Culham
    Western University
  • Yalda Mohsenzadeh
    Western University
    Vector Institute for Artificial Intelligence
  • Footnotes
    Acknowledgements  This work was supported by BrainsCAN at Western University through the Canada First Research Excellence Fund, a Western Interdisciplinary Development Initiatives Grant, a Vector Institute for Artificial Intelligence Research Grant, and a Western Postdoctoral Fellowship.
Journal of Vision September 2024, Vol.24, 1042. doi:https://doi.org/10.1167/jov.24.10.1042
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      Diana C Dima, Jody C Culham, Yalda Mohsenzadeh; The neural dynamics of natural action understanding. Journal of Vision 2024;24(10):1042. https://doi.org/10.1167/jov.24.10.1042.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Humans rapidly make sense of an ever-changing visual world, extracting information about people’s actions in a wide range of settings. Yet it remains unclear how the brain processes this complex information, from the extraction of perceptual details to the emergence of abstract concepts. To address this, we curated a naturalistic dataset of 95 short videos and sentences depicting everyday human actions. We densely labeled each action with perceptual features like scene setting (indoors/outdoors), action-specific features like tool use, and semantic features categorizing actions at different levels of abstraction, from specific action verbs (e.g. chopping) to broad action classes (e.g. manipulation). To investigate when and where these features are processed in the brain, we leveraged a multimodal approach, collecting EEG and fMRI data while participants viewed the action videos and sentences. We applied temporally and spatially resolved representational similarity analysis and variance partitioning to characterize the neural dynamics of action feature representations. We found that action information is extracted in the brain along a temporal gradient, from early perceptual features to later action-specific and semantic information. We mapped action-specific and semantic features to areas in parietal and lateral occipitotemporal cortices. Using cross-decoding across videos and sentences, we identified a late (~500 ms) modality-invariant neural response. Our results characterize the spatiotemporal dynamics of action understanding in the brain, and highlight the shared neural representations of human actions across vision and language.

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