Using artificial neural networks to reveal the human confidence computation.

ABSTRACT: Humans can evaluate the accuracy of their decisions by providing confidence judgements. Traditional cognitive models have tried to capture the mechanisms underlying this process but remain mostly limited to two-choice tasks with simple stimuli. How confidence is given for naturalistic stimuli with multiple choice alternatives is not well understood. We recently developed a convolutional neural network (CNN) model - RTNet - that exhibits several important signatures of human decision making and outperforms existing alternatives in predicting human responses. Here, we use RTNet's image computability to test seven different confidence strategies on a task involving digit discrimination with eight alternatives (N = 60). Specifically, we compared confidence strategies that consider the entire e

SUBMITTER: Shekhar M 

PROVIDER: S-EPMC12799183 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature