The Normalization Model Captures the Effects of Object-based Attention in the Human Visual Cortex

Abstract

Divisive normalization of the neural responses by the activity of the neighboring neurons has been proposed as a fundamental operation in the nervous system based on its success in predicting neural responses recorded in primate electrophysiology studies. Nevertheless, experimental evidence for the existence of this operation in the human brain is still scant. Here, using functional MRI, we explored the role of normalization across the visual hierarchy in the human visual cortex. Using stimuli form the two categories of human bodies and houses, we presented objects in isolation or in clutter and asked participants to attend or ignore the stimuli.Focusing on the primary visual area V1, the object-selective regions LO and pFs, and the category-selective regions EBA and PPA, we first modeled single-voxel responses using a weighted sum, a weighted average, and a normalization model and demonstrated that the response to multiple stimuli could best be described by a model that takes normalization into account. We then explored the observed effects of attention on cortical responses and demonstrated that these effects were predicted by the normalization model, but not by the weighted sum or the weighted average models. Our results thus provide compelling evidence for normalization as a canonical computation operating in the human brain.