Bayesian confidence in optimal decisions.

ABSTRACT: The optimal way to make decisions in many circumstances is to track the difference in evidence collected in favor of the options. The drift diffusion model (DDM) implements this approach and provides an excellent account of decisions and response times. However, existing DDM-based models of confidence exhibit certain deficits, and many theories of confidence have used alternative, nonoptimal models of decisions. Motivated by the historical success of the DDM, we ask whether simple extensions to this framework might allow it to better account for confidence. Motivated by the idea that the brain will not duplicate representations of evidence, in all model variants decisions and confidence are based on the same evidence accumulation process. We compare the models to benchmark results, and suc

SUBMITTER: Calder-Travis J 

PROVIDER: S-EPMC7617410 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature