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.

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.

The diffusion model for 2-choice decisions (R. Ratcliff, 1978) was applied to data from lexical decision experiments in which word frequency, proportion of high-versus low-frequency words, and type of nonword were manipulated. The model gave a good account of all of the dependent variables -accuracy, correct and error response times, and their distributions-and provided a description of how the component processes involved in the lexical decision task were affected by experimental variables. All of the variables investigated affected the rate at which information was accumulated from the stimuli-called drift rate in the model. The different drift rates observed for the various classes of stimuli can all be explained by a 2-dimensional signal-detection representation of stimulus information. The authors discuss how this representation and the diffusion model's decision process might be integrated with current models of lexical access.The lexical decision task is one of the most widely used paradigms in psychology. The goal of the research described in this article was to account for lexical decision performance with the diffusion model (Ratcliff, 1978), a model that allows components of cognitive processing to be examined in two-choice decision tasks. Nine lexical decision experiments, manipulating a number of factors known to affect lexical decision performance, are presented. The diffusion model gives good fits to the data from all of the experiments, including mean response times for correct and error responses, the relative speeds of correct and error responses, the distributions of response times, and accuracy rates.In the diffusion model, the mechanism underlying two-choice decisions is the accumulation of noisy information from a stimulus over time. Information accumulates toward one or the other of two decision criteria until one of the criteria is reached; then the response associated with that criterion is initiated. In the lexical decision task, one of the criteria is associated with a word response, the other with a nonword response. The rate with which information is accumulated is called drift rate, and it depends on the quality of information from the stimulus. In lexical decision, some stimuli are more wordlike than others, and so their rate of accumulation of information toward the word criterion is faster; other stimuli, such as random letter strings, are so un-wordlike that information accumulates quickly toward the nonword criterion. For the nine experiments presented below, the drift rates can be summarized quite simply. First, the ordering of the drift rates from largest to smallest is as follows: high-frequency words, low-frequency words, very low-frequency words, pseudowords, and random letter NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript strings. Second, the differences among the drift rates are larger when the nonwords in an experiment are pseudowords than when they are random letter strings.For our framework, Figure 1 outlines the relationships among...

Two connectionist frameworks, GRAIN (J. L. McClelland, 1993) and brain-state-in-a-box (J. A. Anderson, 1991), and R. Ratcliff's (1978) diffusion model were evaluated using data from a signal detection task. Dependent variables included response probabilities, reaction times for correct and error responses, and shapes of reaction-time distributions. The diffusion model accounted for all aspects of the data, including error reaction times that had previously been a problem for all response-time models. The connectionist models accounted for many aspects of the data adequately, but each failed to a greater or lesser degree in important ways except for one model that was similar to the diffusion model. The findings advance the development of the diffusion model and show that the long tradition of reaction-time research and theory is a fertile domain for development and testing of connectionist assumptions about how decisions are generated over time.

Most current theories of text processing assume a constructionist view of inference processing. In this article, an alternative view is proposed, labeled the minimalist hypothesis. According to this hypothesis, the only inferences that are encoded automatically during reading are those that are based on easily available information, either from explicit statements in the text or from general knowledge, and those that are required to make statements in the text locally coherent. The minimalist hypothesis is shown to be supported by previous research and by the results of several new experiments. It is also argued that automatically encoded minimalist inferences provide the basic representation of textual information from which more goal-directed, purposeful inferences are constructed.