How relevant is the prior? Bayesian causal inference for dynamic perception in volatile environments

Abstract

Interpreting sensory prediction errors can be challenging in volatile environments because they can be caused by stochastic noise or by outdated predictions. Noisy signals should be integrated with prior beliefs to improve precision, but the two should be segregated when environmental changes render prior beliefs irrelevant. Bayesian causal inference provides a statistically optimal solution to deal with uncertainty about the causes of prediction errors. However, the method quickly becomes memory intensive and computationally intractable when applied sequentially. Here, we systematically evaluate the predictive performance of Bayesian causal inference for perceptual decisions in a spatial prediction task based on noisy audiovisual sequences with occasional changepoints. We elucidate the simplifying assumptions of a previously proposed reduced Bayesian observer model, and we compare it to an extensive set of models based on alternative simplification strategies. Model-free analyses revealed the hallmarks of Bayesian causal inference: participants seem to have integrated sensory evidence with prior beliefs to improve accuracy when prediction errors were small, but prior influence decreased gradually as prediction errors increased, signalling probable irrelevance of the priors due to changepoints. Model comparison results indicated that participants computed probability-weighted averages over the causal options (noise or changepoint), akin to the reduced Bayesian observer model. However, participants reliance on prior beliefs was systematically smaller than expected, and this was best explained by individually fitting lower-than-optimal parameters for the a-priori probability of prior relevance. We conclude that perceptual decision makers utilize priors flexibly to the extent that they are deemed relevant, though also conservatively with a lower tendency to bind than ideal observers. Simplified consecutive Bayesian causal inference predicts key characteristics of belief updating in changepoint environments and forms a suitable foundation for modelling dynamic perception in a changing world.