December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Neural and computational evidence that category-selective visual regions are facets of a unified object space
Author Affiliations & Notes
  • Jacob S. Prince
    Harvard University
  • Talia Konkle
    Harvard University
  • Footnotes
    Acknowledgements  This research was supported by NSF CAREER BCS-1942438.
Journal of Vision December 2022, Vol.22, 4428. doi:
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      Jacob S. Prince, Talia Konkle; Neural and computational evidence that category-selective visual regions are facets of a unified object space. Journal of Vision 2022;22(14):4428.

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

  • Supplements

How do cortical regions with selectivity for faces, bodies, words, and scenes relate to one another, and to surrounding occipitotemporal cortex? A prominent theory is that they are independent and highly-specialized regions for processing their preferred domains, but the existence of systematic structure in their responses to non-preferred categories challenges this strict domain-specific account. To probe the response structure of these regions at unprecedented scale, we use the newly-released Natural Scenes Dataset, containing high-resolution fMRI responses to thousands of common object images. Considering 12 regions with selectivity for faces, bodies, word forms, and scenes, we find correlated representational geometry between all pairs of ROIs (mean r=0.46 over independent runs, sd=0.17), even for regions with anticorrelated univariate response profiles (e.g. FFA-1 vs. PPA, univariate r=-0.37, RDM r=0.43). These similar representational geometries suggest a shared representational goal unifying these regions, where univariate selectivity profiles highlight different discriminative feature axes of an integrated representational space for objects. Deep neural networks trained on multi-way object recognition directly instantiate this theory, as they operationalize a rich discriminative object space, without any specialized mechanisms for particular domains. We find that (i) there are naturally-emerging subsets of model units with selectivity for each of these domains; (ii) by re-weighting these selective units, we can predict both univariate and multivariate response structure in the corresponding category-selective regions, in some cases approaching the inter-subject noise ceiling (average max-layer univariate predictivity, r=0.46 in 515 held-out images; average RDM predictivity: r=0.39 in >10e5 pairwise comparisons); and that (iii) the unified representational space of the whole layer, considering all units, can predict responses in the macro-scale OTC sector (univariate max r=0.34, RDM r=0.60). These converging results offer strong empirical support at scale for the emerging theoretical view that category-selective regions are facets of a unified map of object space along occipitotemporal cortex.


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