Putting noise into neurophysiological models of simple decision making

letters to the editor TO THE EDITOR—In neurophysiology and cognitive psychology, considerable progress has been made in understanding the decision processes involved in simple cognitive tasks. Reddi and Carpenter's LATER model 1 assumes that decision time reflects the gradual accumulation of evidence from a stimulus, with a response generated only when the total amount of evidence exceeds some criterial amount. The model has received support from single-cell recording data showing that populations of monkey frontal eye field cells exhibit buildup activity before a decision requiring an eye movement, and that decision time is predictable from the buildup activity. The model was applied to response time data collected from a two-choice task with human subjects, predicting the empirically observed shapes of response time distributions. However, the model has no mechanism for producing errors. This means it cannot the straggling left tail for the speed condition. Third, the model produces accuracy values that match data to within 1 percent while also producing the linear functions and convergence at infinite time. Fourth, the effects of speed–accuracy instructions are well modeled with changes only in response boundaries. Stochastic decision models, like the diffusion model, developed in psychology can account for all the dependent measures collected in two-choice experiments. The models also suggest ways of interpreting neurophysiological data in terms of variability in processing (neur-al noise 8), competition between responses , and differences in difficulty of choices as indexed by accuracy and response time. The results from LATER and the diffusion model show the possibility of convergence and useful cross-fertilization. account for accuracy rates or error response times, both of which are measured in two-choice experiments. In cognitive psychology, there has been a history over the last 40 years of modeling simple two-choice decision processes 2. Successful models such as the diffusion model 3,4 assume the same gradual accumulation of evidence as the LATER model, but also include an additional crucial feature: the accumulation of information within a trial is assumed to be noisy. This allows the models to explain how error responses come about and also to predict accuracy, error response times, the shapes of response time distributions, and the effects of speed versus accuracy instructions. To support LATER, the distribution of an inverse transformation of response times was shown to be normal , so that if cumulative frequency on a z-scale (cumulative normal scale) is plotted against 1/RT, then the result is a …