Attention hinges on tuned normalization strength within human visual cortex

Although attention is known to increase the gain of visuocortical responses, its underlying neural computations remain unclear. Here, we used fMRI to test the hypothesis that a neural population’s ability to be modulated by attention is dependent on divisive normalization. To do so, we leveraged the feature-tuned properties of normalization and found that visuocortical responses to stimuli sharing features normalized each other more strongly. Comparing these normalization measures to measures of attentional modulation, we discovered that subpopulations that exhibited stronger normalization also exhibited larger attentional benefits. In a converging experiment, we demonstrated that attentional benefits were greatest when a subpopulation was forced into a state of stronger normalization. We propose a tuned normalization model of attention that parsimoniously accounts for many properties of our results, suggesting that the degree to which a subpopulation exhibits normalization plays a role in dictating its potential for attentional benefits.