Category-selectivity together with a Normalization Model Predicts the Response to Multi-category Stimuli along the Category-Selective Cortex

Abstract

According to the normalization framework the neural response of a single neuron to multiple stimuli is normalized by the response of its surrounding neurons. High-level visual cortex is composed of clusters of neurons that are selective to the same category. In an fMRI study, we show that the normalization model, together with the profile of category-selectivity of a given cortical area, can predict its response to multi-category stimuli. We measured the response to a face and a body (or a face and an object) presented alone or simultaneously and estimated the contribution of each category to the multicategory representation by fitting a linear model. Results show that the response to multi-category stimuli is a weighted mean of the response to each of its components. The coefficients were correlated with the selectivity profile of the cortical region. These findings suggest that the functional organization of category-selective cortex, i.e., neighboring patches of neurons, each selective to a single category, bias the response to certain categories, for which such clusters of neurons exist, and give them priority in the representation of cluttered visual scenes.