The Influence of Prior Precision on the Inference of Hidden Causes

Perception is widely understood as Bayesian inference, integrating prior expectations with sensory evidence to infer the most probable latent cause of a signal. A common assumption holds that precise priors dominate perception by pulling it strongly toward their mean. However, in hierarchically structured contexts where multiple latent causes compete, Bayesian inference predicts the opposite: imprecise priors can dominate perception under sensory uncertainty. We show that human observers exhibit this counterintuitive bias in an ecologically valid scenario of voice recognition: when classifying ambiguous utterances, observers preferentially attributed them to lower-precision (higher-variance) voice priors. This bias was strongest under high sensory ambiguity and increased with explicit knowledge of prior variance. Computational modeling revealed stable, idiosyncratic prior distributions, suggesting inference operates over hierarchically structured representations of voice identity. These findings identify prior precision as a key determinant of perceptual inference under competing priors.