Sensory reliability shapes perceptual inference via two mechanisms.

To obtain a coherent percept of the environment, the brain should integrate sensory signals from common sources and segregate those from independent sources. Recent research has demonstrated that humans integrate audiovisual information during spatial localization consistent with Bayesian Causal Inference (CI). However, the decision strategies that human observers employ for implicit and explicit CI remain unclear. Further, despite the key role of sensory reliability in multisensory integration, Bayesian CI has never been evaluated across a wide range of sensory reliabilities. This psychophysics study presented participants with spatially congruent and discrepant audiovisual signals at four levels of visual reliability. Participants localized the auditory signals (implicit CI) and judged whether auditory and visual signals came from common or independent sources (explicit CI). Our results demonstrate that humans employ model averaging as a decision strategy for implicit CI; they report an auditory spatial estimate that averages the spatial estimates under the two causal structures weighted by their posterior probabilities. Likewise, they explicitly infer a common source during the common-source judgment when the posterior probability for a common source exceeds a fixed threshold of 0.5. Critically, sensory reliability shapes multisensory integration in Bayesian CI via two distinct mechanisms: First, higher sensory reliability sensitizes humans to spatial disparity and thereby sharpens their multisensory integration window. Second, sensory reliability determines the relative signal weights in multisensory integration under the assumption of a common source. In conclusion, our results demonstrate that Bayesian CI is fundamental for integrating signals of variable reliabilities.