Topic: sensory priors

The view of the world through the lens of our senses is noisy and incomplete. Confronted with this uncertainty, the brain relies on knowledge about the natural world and the current task context to perceive and to act. The framework of Bayesian inference successfully formalizes this knowledge in terms of probabilistic priors, and perception/action as probabilistic inference. However, our understanding of how such priors are obtained, represented and used by the brain remains limited. Here we build upon the recent success in machine learning of “diffusion models” as a means of learning and usin

While moving through the world, such as biking down a bumpy trail, our bodies encounter external forces that cannot be predicted by our own motor output. Delayed sensory feedback is generally too slow to allow accurate state estimation during responses to external forces, so the nervous system may solve this problem by using prior information to make rapid sensory predictions during feedback control. However, it is unknown how flexibly these priors can be set nor how neural activity underlies these fundamental abilities. To answer this, we designed a reaching task in which subjects (human or m

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

A fundamental property of natural intelligence is the capacity to recognise and leverage statistical patterns in the environment to promote adaptive behaviour. This capability, often termed statistical learning (SL), enables us to construct accurate models of the world that aid in perception and decision-making, when faced with noisy and ambiguous sensory input. However, despite extensive data from human experiments and proposals from theoretical neuroscience, the neural implementation of SL remains elusive. To address this, we employed a two-alternative forced-choice (2-AFC) sound categorisat