Topic: perceptual categorization

While navigating the world, we constantly make decisions under uncertainty. In face of noisy perceptions, we build and continuously update our priors and expectations about future events in order to efficiently estimate and map sensory information to actions. In a non-stationary environment, exploratory behaviour may represent an optimal strategy to optimise reward at any given context. Even in stationary environments, animals may perform this innate exploratory behaviour and exhibit unexpected biases. In this work, we investigate how previous experience – sensory priors, as well as choice and

Perception is influenced by past choices. In combined discrimination-estimation experiments a categorical choice leads to two biases: (i) a choice-dependent confirmation bias in the continuous estimate of the stimulus average, and (ii) an overall decrease in the sensitivity to subsequent sensory evidence. It remains unknown what neural mechanisms give rise to these post-decision biases and whether continuous estimates and categorical choices are computed in a common cortical circuit. Here, we develop a neural network model that addresses these questions. We study the integration of continuous