Confidence Is Influenced by Evidence Accumulation Time in Dynamical Decision Models

• Published in: Computational brain & behavior 2024-09, Vol.7 (3), p.287-313
• Main Authors: Hellmann, Sebastian, Zehetleitner, Michael, Rausch, Manuel
• Format: Article
• Language: English
• Subjects: Behavioral Science and Psychology; Cognitive Psychology; Mathematical Models of Cognitive Processes and Neural Networks; Original Paper; Psychological Methods/Evaluation; Psychology
• ISSN: 2522-0861; 2522-087X
• DOI: 10.1007/s42113-024-00205-9

Confidence judgments are closely correlated with response times across a wide range of decision tasks. Sequential sampling models offer two competing explanations for the relationship between confidence and response time: According to some models, decision time directly influences confidence. Other models explain the correlation by linking subjective confidence computation to the decision process dynamics. In previous model comparisons, drift diffusion-based confidence models that do not explicitly consider decision time in the computation of confidence provided superior model fits compared to race models that directly included decision time in the internal computation of confidence. In the present study, we present support for the assumption that confidence explicitly takes decision time and post-decisional accumulation time into account. We propose the dynamical visibility, time, and evidence (dynaViTE) model, an extension of the dynamical weighted evidence and visibility (dynWEV) model. DynaViTE assumes that confidence is not solely based on the final amount of accumulated evidence but explicitly includes time in the computation of confidence. Model comparisons using four previously published data sets with different perceptual decision tasks showed a good model fit of dynaViTE, indicating that the relationship between confidence and response time is not only due to the close link in the accumulation process but also to an explicit inclusion of time in the computation of confidence.