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 ...
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.