The diffusion decision model (DDM) is a sequential-sampling model used to represent cognitive and neural processes in speeded decision making. It assumes that evidence accumulates noisily over time until a decision threshold is reached. The model has been widely applied in cognitive tasks and clinical research to examine individual differences in decision-making. The standard DDM, developed by Ratcliff in the 1970s, includes components such as drift rate (rate of evidence accumulation), boundary settings (decision thresholds), and nondecision time (time for encoding and response execution). The model has been linked to neural processing and has successfully explained decision-making across various domains, including aging, child development, and clinical populations. Recent research has focused on the effects of drift rate, boundary settings, and nondecision time on response times (RTs) and decision accuracy. The model has been used to explain phenomena such as speed-accuracy trade-offs, response biases, and sequential effects. It has also been applied to tasks involving nonstationarity, where decision-making processes change over time. The DDM has been shown to predict RT distributions accurately and has been used to examine the effects of external and internal noise on decision-making. The model has been extended to multialternative decision-making and confidence tasks. It has also been used to analyze conflict paradigms, such as the Eriksen flanker task, where decision boundaries change over time. The DDM has been compared to other models, including those based on collapsing boundaries and urgency signals, and has been shown to provide a better fit to data in many cases. The DDM has been used to examine the effects of stimulus presentation duration, attention, and contrast on decision-making. It has also been applied to tasks involving brief stimulus presentation, where the model's assumptions about drift rate and boundary settings have been tested. The model has been shown to account for phenomena such as fast and slow errors, where incorrect responses are either faster or slower than correct responses depending on the decision context. The DDM has been used to examine the relationship between internal and external noise in decision-making and has been applied to tasks involving dynamic noise. It has also been used to study the effects of stimulus presentation duration on decision-making, with findings suggesting that perceptual integration processes may be involved in the computation of drift rate rather than evidence accumulation. Overall, the DDM has been widely used to model decision-making processes and has provided insights into the cognitive and neural mechanisms underlying decision-making. It has been applied to a wide range of tasks and has been shown to provide accurate predictions of RT distributions and decision accuracy. The model continues to be a valuable tool in cognitive and neural research.The diffusion decision model (DDM) is a sequential-sampling model used to represent cognitive and neural processes in speeded decision making. It assumes that evidence accumulates noisily over time until a decision threshold is reached. The model has been widely applied in cognitive tasks and clinical research to examine individual differences in decision-making. The standard DDM, developed by Ratcliff in the 1970s, includes components such as drift rate (rate of evidence accumulation), boundary settings (decision thresholds), and nondecision time (time for encoding and response execution). The model has been linked to neural processing and has successfully explained decision-making across various domains, including aging, child development, and clinical populations. Recent research has focused on the effects of drift rate, boundary settings, and nondecision time on response times (RTs) and decision accuracy. The model has been used to explain phenomena such as speed-accuracy trade-offs, response biases, and sequential effects. It has also been applied to tasks involving nonstationarity, where decision-making processes change over time. The DDM has been shown to predict RT distributions accurately and has been used to examine the effects of external and internal noise on decision-making. The model has been extended to multialternative decision-making and confidence tasks. It has also been used to analyze conflict paradigms, such as the Eriksen flanker task, where decision boundaries change over time. The DDM has been compared to other models, including those based on collapsing boundaries and urgency signals, and has been shown to provide a better fit to data in many cases. The DDM has been used to examine the effects of stimulus presentation duration, attention, and contrast on decision-making. It has also been applied to tasks involving brief stimulus presentation, where the model's assumptions about drift rate and boundary settings have been tested. The model has been shown to account for phenomena such as fast and slow errors, where incorrect responses are either faster or slower than correct responses depending on the decision context. The DDM has been used to examine the relationship between internal and external noise in decision-making and has been applied to tasks involving dynamic noise. It has also been used to study the effects of stimulus presentation duration on decision-making, with findings suggesting that perceptual integration processes may be involved in the computation of drift rate rather than evidence accumulation. Overall, the DDM has been widely used to model decision-making processes and has provided insights into the cognitive and neural mechanisms underlying decision-making. It has been applied to a wide range of tasks and has been shown to provide accurate predictions of RT distributions and decision accuracy. The model continues to be a valuable tool in cognitive and neural research.