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Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features

View ORCID ProfileMargaret Henderson, View ORCID ProfileMichael J. Tarr, View ORCID ProfileLeila Wehbe
doi: https://doi.org/10.1101/2022.08.04.502850
Margaret Henderson
1Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
2Department of Psychology, Carnegie Mellon University, Pittsburgh, USA
3Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
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  • For correspondence: mmhender@cmu.edu
Michael J. Tarr
1Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
2Department of Psychology, Carnegie Mellon University, Pittsburgh, USA
3Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
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Leila Wehbe
1Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA
2Department of Psychology, Carnegie Mellon University, Pittsburgh, USA
3Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
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Abstract

Representations of visual and semantic information can overlap in human visual cortex, with the same neural populations exhibiting sensitivity to low-level features (orientation, spatial frequency, retinotopic position), and high-level semantic categories (faces, scenes). It has been hypothesized that this relationship between low-level visual and high-level category neural selectivity reflects natural scene statistics, such that neurons in a given category-selective region are tuned for low-level features or spatial positions that are diagnostic of the region’s preferred category. To address the generality of this “natural scene statistics” hypothesis, as well as how well it can account for responses to complex naturalistic images across visual cortex, we performed two complementary analyses. First, across a large set of rich natural scene images, we demonstrated reliable associations between low-level (Gabor) features and high-level semantic dimensions (indoor-outdoor, animacy, real-world size), with these relationships varying spatially across the visual field. Second, we used a large-scale fMRI dataset (the Natural Scenes Dataset) and a voxelwise forward encoding model to estimate the feature and spatial selectivity of neural populations throughout visual cortex. We found that voxels in category-selective visual regions exhibit systematic biases in their feature and spatial selectivity which are consistent with their hypothesized roles in category processing. We further showed that these low-level tuning biases are largely independent of viewed image category. Together, our results are consistent with a framework in which low-level feature selectivity contributes to the computation of high-level semantic category information in the brain.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 06, 2022.
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Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features
Margaret Henderson, Michael J. Tarr, Leila Wehbe
bioRxiv 2022.08.04.502850; doi: https://doi.org/10.1101/2022.08.04.502850
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Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features
Margaret Henderson, Michael J. Tarr, Leila Wehbe
bioRxiv 2022.08.04.502850; doi: https://doi.org/10.1101/2022.08.04.502850

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