Updating beliefs to maintain coherence with observational evidence is a cornerstone of rationality. This entails the compliance with probabilistic principles which acknowledge that real-world observations are consistent with several possible interpretations. This work presents two novel experimental paradigms and computational analyses of how human participants quantify uncertainty in perceptual inference tasks. Their behavioral responses feature non-trivial patterns of probabilistic inference such as reliability-based belief updating over hierarchical state representations of the environment. Despite characteristic generalization biases, behavior cannot be explained well by alternative heuristic accounts. These results suggest that uncertainty is an integral part of our inferences and that we indeed have the potential to resort to rational inference mechanisms that adhere to probabilistic principles. Furthermore, they appear consistent with ubiquitous representations of uncertainty posited by framework theories such as Bayesian hierarchical modeling and predictive coding.
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