Network predictions sharpen the representation of visual features for categorization

Yan, Y.; Zhan, J.; Ince, R. A. A.; Schyns, P. G.

doi:10.1101/2022.07.01.498431

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bioRxiv · 10.1101/2022.07.01.498431

Network predictions sharpen the representation of visual features for categorization

Yan, Y.·Zhan, J.·Ince, R. A. A.·Schyns, P. G.

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Abstract

Models of visual cognition assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, the specific network mechanisms of this facilitation remain unclear. Here, we studied them in 11 individual participants cued to the spatial location (left vs. right) and contents (Low vs. High Spatial Frequency, LSF vs. HSF) of an upcoming Gabor stimulus that they categorized. Using concurrent MEG recordings, we reconstructed in each participant the network that communicates the predicted contents and the network that represents these contents from the stimulus for categorization. We show that predictions of LSF vs. HSF propagate top-down from temporal to contra-lateral occipital cortex, with modulatory supervision from frontal cortex. In occipital cortex, predictions sharpen bottom-up stimulus LSF vs. HSF representations, leading to faster categorizations. Our results therefore reveal functional networks that predict visual contents to sharpen their representations from the stimulus to facilitate categorization behavior.

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Yan, Y., Zhan, J., Ince, R. A. A., Schyns, P. G.. 2022-07-04. Network predictions sharpen the representation of visual features for categorization. https://doi.org/10.1101/2022.07.01.498431

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