Hierarchical priors enable neural prediction of perceived biological motion

Biological motion perception is an essential skill that allows us to quickly infer how others move. While this is often cast as inherently predictive process, it remains unclear to what extent different priors shape neural processing of biological motion. We investigated this by comparing the dynamic representational geometry of human magnetoencephalography (MEG) activity and observed biological motion stimuli by means of dynamic representational similarity analysis (dRSA). Under normal viewing conditions, neural representations were indeed predictive and followed an inverse hierarchy: high-level viewpoint-invariant body motion representations were visible before representations of viewpoint-dependent body motion and low-level visual features. Disrupting holistic priors by turning videos upside down selectively reduced high-level predictions. Instead, disrupting kinematics priors by temporal piecewise scrambling eliminated all motion prediction, with neural activity merely reacting to, rather than predicting, visual input. These findings reveal how low- and high-level priors jointly shape predictive neural processing of observed biological motion.