The three most basic performance measures used in cognitive research are choice, decision time, and confidence. We present a diffusion model that accounts for all three variables using a common underlying process. The model uses a standard drift diffusion process to account for choice and decision time. To make a confidence judgment, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The fully specified model qualitatively accounts for the known relationships between all three variables. Besides the speed/accuracy trade-off, the model also correctly predicts that confidence increases with accuracy. Finally, it captures the two-fold relationship between confidence and decision time. On the one hand, during optional stopping tasks (where the respondent determines when to stop and decide), there is an inverse relationship between the time taken and the degree of confidence expressed in the choice (Henmon, 1911). On the other hand, during externally controlled stopping tasks (where the experimenter determines when to stop and decide) then the longer people are given to make a decision the more confident they become (Irwin et al., 1956). Quantitatively we will evaluate both the ability of the diffusion model and the Poisson model using Vicker's (1979) balance-of-evidence hypothesis to capture accuracy, response time distributions, and confidence rating distributions from a statement verification task. Theoretical implications and applications of the model to a variety of basic and applied tasks will be discussed. |