Predictive eye movements are adjusted in a Bayes-optimal fashion in response to unexpectedly changing environmental probabilities.

This study examined the application of active inference to dynamic visuomotor control. Active inference proposes that actions are dynamically planned according to uncertainty about sensory information, prior expectations, and the environment, with motor adjustments serving to minimise future prediction errors. We investigated whether predictive gaze behaviours are indeed adjusted in this Bayes-optimal fashion during a virtual racquetball task. In this task, participants intercepted bouncing balls with varying levels of elasticity, under conditions of higher or lower environmental volatility. Participants' gaze patterns differed between stable and volatile conditions in a manner consistent with generative models of Bayes-optimal behaviour. Partially observable Markov models also revealed an increased rate of associative learning in response to unpredictable shifts in environmental probabilities, although there was no overall effect of volatility on this parameter. Findings extend active inference frameworks into complex and unconstrained visuomotor tasks and present important implications for a neurocomputational understanding of the visual guidance of action.