Topic: prior reliability

Listeners benefit from integrating prior beliefs with auditory sensory signals according to their relative reliability to reduce effects of stochastic noise. In volatile environments, however, listeners should not integrate when a change of signal source renders their prior beliefs irrelevant. Bayesian causal inference could provide the brain with an optimal strategy for adaptively adjusting its reliance on priors. Mechanistically, it has been suggested that the arousal system modulates the weights between prior and likelihoodaccordingly. We tested these hypotheses in an auditory localization

A key challenge in perception is to differentiate random noisy observations from structurally wrong beliefs when confronted with prediction errors. One should integrate out noise to improve precision, but simultaneously recognize when predictions are irrelevant due to environmental change. Bayesian inference prescribes a statistically optimal solution to this problem, but it is memory intensive and computationally complex, thus unlikely to be used continuously by humans.Here, we systematically investigated which hallmarks of Bayesian inference are present in previously published human response