The article "Diffusion Decision Model: Current Issues and History" by Ratcliff, Smith, Brown, and McKoon reviews the development and application of diffusion models in psychology. These models, which have a long history, are used to represent the cognitive and neural processes involved in speeded decision-making. The standard diffusion model assumes that evidence accumulates at a constant rate over a short period to reach a decision boundary. This model has been successfully applied to various cognitive tasks and has been linked to neural processes and clinical research. The authors discuss the model's components, including the drift rate, starting point, and nondecision time, and how these parameters affect response times (RTs) and choice probabilities. They highlight the model's ability to account for behavioral data, its links to neural processing, and its success in explaining decision-making across different domains of psychology. The article also addresses current issues and unresolved questions in the field, such as the assumption of across-trial variability in model parameters, the interpretation of response signal and go/no-go tasks, and the relationship between internal and external noise in decision-making. It emphasizes the importance of examining RT distributions and the need to consider both accuracy and RT when analyzing experimental data. Additionally, the authors explore expanded judgment tasks, brief stimulus presentation, and nonstationarity in processing, providing insights into the complexities of decision-making processes. They conclude by discussing the ongoing research and future directions in the field, emphasizing the importance of integrating older and newer findings to advance the understanding of decision-making.The article "Diffusion Decision Model: Current Issues and History" by Ratcliff, Smith, Brown, and McKoon reviews the development and application of diffusion models in psychology. These models, which have a long history, are used to represent the cognitive and neural processes involved in speeded decision-making. The standard diffusion model assumes that evidence accumulates at a constant rate over a short period to reach a decision boundary. This model has been successfully applied to various cognitive tasks and has been linked to neural processes and clinical research. The authors discuss the model's components, including the drift rate, starting point, and nondecision time, and how these parameters affect response times (RTs) and choice probabilities. They highlight the model's ability to account for behavioral data, its links to neural processing, and its success in explaining decision-making across different domains of psychology. The article also addresses current issues and unresolved questions in the field, such as the assumption of across-trial variability in model parameters, the interpretation of response signal and go/no-go tasks, and the relationship between internal and external noise in decision-making. It emphasizes the importance of examining RT distributions and the need to consider both accuracy and RT when analyzing experimental data. Additionally, the authors explore expanded judgment tasks, brief stimulus presentation, and nonstationarity in processing, providing insights into the complexities of decision-making processes. They conclude by discussing the ongoing research and future directions in the field, emphasizing the importance of integrating older and newer findings to advance the understanding of decision-making.