Can Category-selective Cortex Predict Categorization Behavior?

Abstract

Abstract

One of the distinctive features of the human visual system is the presence in occipito-temporal cortex (OTC) of regions that show preferential activation to specific categories of visual objects. To understand how this selectivity relates to categorization behavior, studies have employed a distance-to-bound (DTB) approach, where multivariate brain activity is used to estimate a decision boundary, from which behavioral performance can be predicted. Using this approach, correlations have been found between activity in OTC, and behavioral performance when carrying out certain categorization tasks. However, it remains unclear what determines where in OTC this correlations can be found and with which categorization tasks they can be found. Here, we bridged this gap by relating category-selective regions of OTC, to behavioral performance while participants categorized images as belonging or not to their preferred categories. We adopted a more basic approach and considered simple, univariate activity, rather than relying on decoding to build our DTB. Our results show that activation in regions selective to faces (fusiform face area and occipital face area), bodies (extrastriate body area), and scenes (parahippocampal place area) is sufficient to predict behavioral performance while categorizing images as being faces, bodies, or scenes, respectively. These results are largely consistent across RT and motor movements and generalize to animacy classification. Overall, our data add to evidence that category-selective regions in OTC can serve to guide categorization behavior and underline the validity of the DTB approach to address this relationship.