Continuous versus discrete information processing: Modeling accumulation of partial information.

David Meyer and colleagues have recently developed a new technique for examining the time course of information processing. The technique is a variant of the response signal procedure: On some trials subjects are presented with a signal that requires them to respond, whereas on other trials they respond normally. These two types of trials are randomly intermixed so subjects are unable to anticipate which kind of trial is to be presented next. For data analysis, it is assumed that on the signal trials observed reaction times are a probability mixture of regular responses and guesses based on partial information. The accuracy of guesses based on partial information can be determined by using the data from the regular trials and a simple race model to remove the contribution of fastfinishing regular trials from signal trial data. This analysis shows that the accuracy of guesses is relatively low and is either approximately constant or grows slowly over the time course of retrieval. Meyer and colleagues have argued that this pattern of results rules out most continuous models of information processing. But the analyses presented in this article show that this pattern is consistent with several stochastic reaction time models: the simple random walk, the runs, and the continuous diffusion models. The diffusion model is assessed with data from a new experiment using the studytest recognition memory procedure. Fitting the diffusion model to the data from regular trials fixes