Trial-to-trial, uncertainty-based adjustment of decision boundaries in visual categorization

Significance Categorization is an important part of perception and cognition. For example, an animal must successfully categorize a disturbing sound as being due to the wind or to a predator. Computationally, categorization amounts to applying decision boundaries to noisy stimulus measurements. Here, we examine how these decision boundaries change as the quality of the sensory evidence varies unpredictably from trial to trial. We show that both humans and monkeys adjust their decision boundaries from trial to trial, often near-optimally. We further show how a neural network can perform this computation near-optimally. Our results might lead to a better understanding of categorization. Categorization is a cornerstone of perception and cognition. Computationally, categorization amounts to applying decision boundaries in the space of stimulus features. We designed a visual categorization task in which optimal performance requires observers to incorporate trial-to-trial knowledge of the level of sensory uncertainty when setting their decision boundaries. We found that humans and monkeys did adjust their decision boundaries from trial to trial as the level of sensory noise varied, with some subjects performing near optimally. We constructed a neural network that implements uncertainty-based, near-optimal adjustment of decision boundaries. Divisive normalization emerges automatically as a key neural operation in this network. Our results offer an integrated computational and mechanistic framework for categorization under uncertainty.