The neural dynamics of hierarchical Bayesian causal inference in multisensory perception

TLDR

Multisensory perception requires resolving whether signals share a common cause, a process thought to follow Bayesian causal inference, yet the underlying neural mechanisms are not well understood. This study aimed to investigate how the brain performs hierarchical Bayesian causal inference during audiovisual integration. Participants viewed audiovisual sequences with varying numbers of flashes and beeps while researchers applied Bayesian modeling and EEG representational similarity analyses to track neural representations. Results showed that the brain first encodes flashes and beeps independently, then computes their combined number via model‑averaged weighted estimates of fusion and segregation, with prestimulus alpha power and phase reflecting prior beliefs that bias arbitration.

Abstract

Transforming the barrage of sensory signals into a coherent multisensory percept relies on solving the binding problem - deciding whether signals come from a common cause and should be integrated or, instead, segregated. Human observers typically arbitrate between integration and segregation consistent with Bayesian Causal Inference, but the neural mechanisms remain poorly understood. Here, we presented people with audiovisual sequences that varied in the number of flashes and beeps, then combined Bayesian modelling and EEG representational similarity analyses. Our data suggest that the brain initially represents the number of flashes and beeps independently. Later, it computes their numbers by averaging the forced-fusion and segregation estimates weighted by the probabilities of common and independent cause models (i.e. model averaging). Crucially, prestimulus oscillatory alpha power and phase correlate with observers' prior beliefs about the world's causal structure that guide their arbitration between sensory integration and segregation.