RT Journal Article
SR Electronic
T1 Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.08.04.502850
DO 10.1101/2022.08.04.502850
A1 Margaret Henderson
A1 Michael J. Tarr
A1 Leila Wehbe
YR 2022
UL http://biorxiv.org/content/early/2022/08/06/2022.08.04.502850.abstract
AB 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 StatementThe authors have declared no competing interest.