The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks Roger Ratcliff and Gail McKoon The diffusion decision model provides detailed explanations of behavior in two-choice discrimination tasks. This article reviews the model to show how it translates behavioral data—accuracy, mean response times, and response time distributions—into components of cognitive processing. Three experiments illustrate how the model accounts for the effects of experimental manipulations on accuracy, mean response times, and response time distributions. The experiments also demonstrate the model's empirical testability and potential falsifiability. The model has broad applications in research domains such as aging and neurophysiology. The diffusion model is a model of cognitive processes involved in simple two-choice decisions. It separates the quality of evidence entering the decision from decision criteria and nondecision processes such as stimulus encoding and response execution. The model is applied to relatively fast two-choice decisions (mean RTs less than about 1000 to 1500 ms) and decisions that are a single-stage process. The diffusion model assumes that decisions are made by a noisy process that accumulates information over time from a starting point toward one of two response criteria or boundaries. The rate of accumulation of information is called the drift rate, determined by the quality of information extracted from the stimulus. The model predicts that increases in stimulus difficulty lead to increases in mean RT and decreases in accuracy. The model also predicts changes in RT distributions, with little change in shape. The diffusion model extracts theoretically relevant components of processing from accuracy and RT data of two-choice tasks. The model provides a decomposition of data that isolates components so they can be individually studied. The model can provide a meeting point between a model for stimulus encoding and decision processes. It can also extract decision criterion settings from data to explain how they are determined by instructions, payoffs, reward contingencies, etc. The diffusion model has been applied to various research domains, including the effects of age and aphasia on memory and decision criteria, and the effects of depression on information processing. Recent studies have mapped the model's components of processing onto neural firing rate data. The diffusion model is used to explain how information is accrued from a stimulus. The model can predict accuracy and RT distributions when stimulus information is fed through the diffusion model. The model can also explain how decision criteria are determined by instructions, payoffs, reward contingencies, etc. The diffusion model has been used to study the effects of age and aphasia on memory and decision criteria, and the effects of depression on information processing. Recent studies have mapped the model's components of processing onto neural firing rate data. The diffusion model is used to explain how information is accrued from a stimulus. The model can predict accuracy and RT distributions when stimulus information is fed through the diffusion model. The model can also explain how decision criteria are determined by instructions, payoffs, reward contingencies, etc.The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks Roger Ratcliff and Gail McKoon The diffusion decision model provides detailed explanations of behavior in two-choice discrimination tasks. This article reviews the model to show how it translates behavioral data—accuracy, mean response times, and response time distributions—into components of cognitive processing. Three experiments illustrate how the model accounts for the effects of experimental manipulations on accuracy, mean response times, and response time distributions. The experiments also demonstrate the model's empirical testability and potential falsifiability. The model has broad applications in research domains such as aging and neurophysiology. The diffusion model is a model of cognitive processes involved in simple two-choice decisions. It separates the quality of evidence entering the decision from decision criteria and nondecision processes such as stimulus encoding and response execution. The model is applied to relatively fast two-choice decisions (mean RTs less than about 1000 to 1500 ms) and decisions that are a single-stage process. The diffusion model assumes that decisions are made by a noisy process that accumulates information over time from a starting point toward one of two response criteria or boundaries. The rate of accumulation of information is called the drift rate, determined by the quality of information extracted from the stimulus. The model predicts that increases in stimulus difficulty lead to increases in mean RT and decreases in accuracy. The model also predicts changes in RT distributions, with little change in shape. The diffusion model extracts theoretically relevant components of processing from accuracy and RT data of two-choice tasks. The model provides a decomposition of data that isolates components so they can be individually studied. The model can provide a meeting point between a model for stimulus encoding and decision processes. It can also extract decision criterion settings from data to explain how they are determined by instructions, payoffs, reward contingencies, etc. The diffusion model has been applied to various research domains, including the effects of age and aphasia on memory and decision criteria, and the effects of depression on information processing. Recent studies have mapped the model's components of processing onto neural firing rate data. The diffusion model is used to explain how information is accrued from a stimulus. The model can predict accuracy and RT distributions when stimulus information is fed through the diffusion model. The model can also explain how decision criteria are determined by instructions, payoffs, reward contingencies, etc. The diffusion model has been used to study the effects of age and aphasia on memory and decision criteria, and the effects of depression on information processing. Recent studies have mapped the model's components of processing onto neural firing rate data. The diffusion model is used to explain how information is accrued from a stimulus. The model can predict accuracy and RT distributions when stimulus information is fed through the diffusion model. The model can also explain how decision criteria are determined by instructions, payoffs, reward contingencies, etc.