Bayesian inference with incomplete knowledge explains perceptual confidence and its deviations from accuracy.

ABSTRACT: In perceptual decisions, subjects infer hidden states of the environment based on noisy sensory information. Here we show that both choice and its associated confidence are explained by a Bayesian framework based on partially observable Markov decision processes (POMDPs). We test our model on monkeys performing a direction-discrimination task with post-decision wagering, demonstrating that the model explains objective accuracy and predicts subjective confidence. Further, we show that the model replicates well-known discrepancies of confidence and accuracy, including the hard-easy effect, opposing effects of stimulus variability on confidence and accuracy, dependence of confidence ratings on simultaneous or sequential reports of choice and confidence, apparent difference between choice and

SUBMITTER: Khalvati K 

PROVIDER: S-EPMC8481237 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature