Confidence Is Influenced by Evidence Accumulation Time in Dynamical Decision Models

Area of Science:

• Cognitive psychology
• Computational neuroscience
• Decision science

Background:

• Confidence judgments correlate with response times in decision tasks.
• Sequential sampling models offer competing explanations for this relationship.
• Previous models often favored those not explicitly including decision time in confidence computation.

Purpose of the Study:

• To investigate if confidence judgments explicitly incorporate decision and post-decisional accumulation time.
• To propose and validate a novel model that includes time in confidence computation.

Main Methods:

• Introduction of the dynamical visibility, time, and evidence (dynaViTE) model, an extension of the dynamical weighted evidence and visibility (dynWEV) model.
• Model comparison using four established datasets from diverse perceptual decision tasks.
• Evaluation of model fit to assess the role of time in confidence computation.

Main Results:

• The dynaViTE model demonstrated a strong fit across multiple datasets.
• Findings support the hypothesis that confidence explicitly accounts for decision time and post-decisional accumulation.
• The correlation between confidence and response time is influenced by an explicit inclusion of time in confidence calculation.

Conclusions:

• Confidence judgments are not solely based on accumulated evidence but also explicitly integrate temporal factors.
• The dynaViTE model provides a better account of the confidence-response time relationship than models excluding explicit time components.
• This highlights the critical role of time in the computation of subjective confidence during decision-making.