A theory of memory retrieval is developed and is shown to apply over a range of experimental paradigms. Access to memory traces is viewed in terms of a resonance metaphor. The probe item evokes the search set on the basis of probe-memory item relatedness, just as a ringing tuning fork evokes sympathetic vibrations in other tuning forks. Evidence is accumulated in parallel from each probe-memory item comparison, and each comparison is modeled by a continuous random walk process. In item recognition, the decision process is self-terminating on matching comparisons and exhaustive on nonmatching comparisons. The mathematical model produces predictions about accuracy, mean reaction time, error latency, and reaction time distributions that are in good accord with experimental data. The theory is applied to four item recognition paradigms (Sternberg, prememorized list, study-test, and continuous) and to speed-accuracy paradigms; results are found to provide a basis for comparison of these paradigms. It is noted that neural network models can be interfaced to the retrieval theory with little difficulty and that semantic memory models may benefit from such a retrieval scheme. At the present time, one of the major deficiencies in cognitive psychology is the lack of explicit theories that encompass more than a single experimental paradigm. The lack of such theories and some of the unfortunate consequences have been discussed recently by Allport (1975) and Newell (1973). Two important points are made by Newell: First, research in cognitive psychology is motivated This research was supported by Research Grants APA 146 from the National Research Council of Canada and OMHF 164 from the Ontario Mental Health Foundation to Bennet B. Murdock, Jr. I would like to thank Ben Murdock for his support, help, and criticism and the use of data from his published and unpublished experiments. I would also like to thank the following: Ron Okada and David Burrows for use of their data; Peter Liepa for programming Experiment 2; David Andrews for help with the mathematical development; Gail McKoon, Bill Hockley, and Bob Lockhart for useful discussion; and Howard Kaplan for general programming assistance.

The diffusion decision model allows detailed explanations of behavior in two-choice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral data-accuracy, mean response times, and response time distributions-into components of cognitive processing. Three experiments are used to illustrate experimental manipulations of three components: stimulus difficulty affects the quality of information on which a decision is based; instructions emphasizing either speed or accuracy affect the criterial amounts of information that a subject requires before initiating a response; and the relative proportions of the two stimuli affect biases in drift rate and starting point. The experiments also illustrate the strong constraints that ensure the model is empirically testable and potentially falsifiable. The broad range of applications of the model is also reviewed, including research in the domains of aging and neurophysiology.

The diffusion model for two-choice real-time decisions is applied to four psychophysical tasks. The model reveals how stimulus information guides decisions and shows how the information is processed through time to yield sometimes correct and sometimes incorrect decisions. Rapid two-choice decisions yield multiple empirical measures: response times for correct and error responses, the probabilities of correct and error responses, and a variety of interactions between accuracy and response time that depend on instructions and task difficulty. The diffusion model can explain all these aspects of the data for the four experiments we present. The model correctly accounts for error response times, something previous models have failed to do. Variability within the decision process explains how errors are made, and variability across trials correctly predicts when errors are faster than correct responses and when they are slower.Making decisions is a ubiquitous part of everyday life. In psychology, besides being an object of study in its own right, decision making plays a central role in the tasks used to study basic cognitive functions such as memory, perception, and language comprehension. Frequently, the decisions required in these tasks are rapid two-choice decisions, decisions that are based on information that can be described as varying along a single dimension. Two key features of these decisions are that they occur over time-decisions are never reached instantaneously-and that they are error prone. In this article, we present a model to explain this class of decision processes. The goal is to understand what information drives the decision and how the decision process evolves over time to reach correct and incorrect decisions. The problem is difficult because potential models are constrained to explain multiple empirical measures that interact in complex ways. The measures include mean response times for correct and error responses, the shapes of the distributions of the response times, and the probabilities of correct and error responses. The relation between response time and accuracy is not fixed; it varies according to whether speed or accuracy of performance is emphasized and according to whether one or the other of the responses is more probable or weighted more heavily. In addition, the relation between probability of an error and error response time is not fixed but varies across levels of overall accuracy. Because of these complexities, no previous model has been completely successful. Often, models have dealt with only one measure-accuracy but not response time, or response time but not accuracy. Models that have dealt with response time have usually tried to explain only mean response times for correct responses, not the shapes of response time distributions or response times for errors. Modeling speed-accuracy relationships has usually not been attempted.In this article, we show how the diffusion model (Ratcliff, 1978(Ratcliff, , 1981(Ratcliff, , 1985(Ratcliff, , 1988 Ratcliff, Van Zandt, & McKoon,...

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this article, we relate the models to both earlier and more recent research in psychology.