Memory

model as a form of data analysis for speeded binary de-cisions. The diffusion model assumes that binary deci-sions are based on a continuous process that fluctuates between two possible outcomes (Figure 1). As soon as the process reaches a critical upper or lower value, a de-cision is made, and the corresponding response is exe-cuted. One main advantage of the diffusion model is that different components of the decision process (rate of in-formation uptake, bias, conservatism, and motor compo-nent) are represented by different parameters of the model. Theoretically, distinct aspects of the decision process can be separated statistically. In a sense, the model allows in-ferences regarding hidden cognitive processes. Another advantage of the diffusion model data analysis is the high degree of information utilization. In contrast to conven-tional forms of data analysis, the diffusion model incor-porates response times (RTs) for correct responses and er-rors, as well as the ratio of correct and erroneous responses. The diffusion model thus provides a powerful statisti-cal tool that allows a very fine-grained analysis of the cognitive processes underlying simple response tasks. Previous applications of the diffusion model have in-cluded, for example, the analysis of retrieval processes (Ratcliff, 1978, 1988) and the identification of the fac-tors underlying age-related slowing (Ratcliff, Spieler, &amp