Expressions for Bayesian confidence of drift diffusion observers in fluctuating stimuli tasks.

ABSTRACT: We introduce a new approach to modelling decision confidence, with the aim of enabling computationally cheap predictions while taking into account, and thereby exploiting, trial-by-trial variability in stochastically fluctuating stimuli. Using the framework of the drift diffusion model of decision making, along with time-dependent thresholds and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of "pipeline" evidence that has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli that change over the course of a trial with normally-

SUBMITTER: Calder-Travis J 

PROVIDER: S-EPMC7615478 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature