September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Transformation of event representations along middle temporal gyrus
Author Affiliations & Notes
  • Anna Leshinskaya
    University of Pennsylvania
  • Sharon L Thompson-Schill
    University of Pennsylvania
Journal of Vision September 2019, Vol.19, 91a. doi:https://doi.org/10.1167/19.10.91a
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      Anna Leshinskaya, Sharon L Thompson-Schill; Transformation of event representations along middle temporal gyrus. Journal of Vision 2019;19(10):91a. doi: https://doi.org/10.1167/19.10.91a.

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

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Abstract

When learning about events through visual experience, one must not only identify which events are visually similar, but also retrieve those events’ associates—which may be visually dissimilar—and recognize when different events have similar predictive relations. How are these demands balanced? To address this question, we taught participants the predictive structures among four events, which appeared in four different sequences, each cued by a distinct object. In each, one event (‘cause’) was predictably followed by another (‘effect’). Sequences in the same relational category had similar predictive structure, while across categories, the effect and cause events were reversed. Using fMRI data, we measured associative coding, indicated by correlated responses between effect and cause events; perceptual coding, indicated by correlated responses to visually similar events; and relational category coding, indicated by correlated responses to objects in the same relational category. All three models characterized responses within right middle temporal gyrus (MTG), but in different ways: perceptual and associative coding diverged along the posterior to anterior axis, while relational categories emerged anteriorly in tandem with associative coding. Thus, along the posterior-anterior axis of MTG, the representation of the visual attributes of events is transformed into a representation of both specific and generalizable relational attributes.

Acknowledgement: R01EY021717, R01DC015359, and R01DC009209 
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