Can category-selective cortex predict categorisation behaviour?

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 categorisation behaviour, studies have employed a distance-to-bound approach (DTB), where multivariate brain activity is used to estimate a decision boundary, from which behavioural performance can be predicted. Using this approach, correlations have been found between activity in OTC, and behavioural performance when carrying out certain categorisation tasks. However, it remains unclear what determines where in OTC this correlations can be found, and with which categorisation tasks they can be found. Here, we bridged this gap by relating category-selective regions of OTC, to behavioural performance while participants categorised 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 (FFA & OFA), bodies (EBA), and scenes (PPA), is sufficient to predict behavioural performance while categorising images as being faces, bodies, or scenes, respectively. These results are largely consistent across reaction time and motor movements, and generalise to animacy classification. Overall, our data adds to evidence that category-selective regions in OTC can serve to guide categorisation behaviour, and underlines the validity of the DTB approach to address this relationship.