Douglas G. Lee

This paper extends the drift-diffusion model to account for choice-induced preference change by incorporating attribute variability during decision-making.

This paper proposes a confidence-driven decision-making model that reinterprets confidence as the primary monitored variable during decisions, offering a more parsimonious explanation of existing findings.

The paper tests if incorporating individual attribute information into computational models improves their ability to predict value-based decision-making patterns.

The paper extends the drift-diffusion model to account for choice-induced preference change by allowing drift rates to vary during decision-making based on attended attributes.

The paper shows that evaluating options shifts their perceived value and attributes to favor the chosen option, increasing confidence, regardless of whether the choice is explicit or not.

This paper introduces a computational model of metacognitive control in value-based decision-making, linking mental effort, confidence, and preference reversals through an effort-confidence tradeoff.

This study tests whether value certainty, not just value difference, affects decision-making accuracy, confidence, and response time.

The paper extends the drift-diffusion model to include value certainty, showing it better explains choice consistency and response time in empirical data.

This paper investigates choice-induced preference change (CIPC) and challenges the cognitive dissonance theory by proposing it results from value representation refinement during decision-making, supported by behavioral experiments.