de Vries, I. E. J. | bioRxiv Science

bioRxiv2025

Action prediction relies on high-level holistic and low-level kinematics priors

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. Impact statementDisrupting holistic priors selectively interferes with neural prediction of observed biological motion

neuroscience↗

bioRxiv2022

Predictive neural representations of naturalistic dynamic input

Adaptive behavior such as social interaction requires our brain to predict unfolding external dynamics. While theories assume such dynamic prediction, empirical evidence is limited to static snapshots and indirect consequences of predictions. We present a dynamic extension to representational similarity analysis that uses temporally variable models to capture neural representations of unfolding events. We applied this approach to source-reconstructed magnetoencephalography (MEG) data of healthy human subjects and demonstrate both lagged and predictive neural representations of observed actions. Predictive representations exhibit a hierarchical pattern, such that high-level abstract stimulus features are predicted earlier in time, while low-level visual features are predicted closer in time to the actual sensory input. By quantifying the temporal forecast window of the brain, this approach allows investigating predictive processing of our dynamic world. It can be applied to other naturalistic stimuli (e.g., film, soundscapes, music, motor planning/execution, social interaction) and any biosignal with high temporal resolution.

neuroscience↗