The diffusion decision model (DDM) is a mathematical framework that jointly describes choice behavior and response time distributions, offering a process-level account of decision-making. Conceptualizing decisions as the accumulation of noisy evidence, the DDM has provided insights into the cognitive mechanisms underlying perception, attention, memory, and higher-order decision-making. Its flexibility and explanatory power have made it one of the most widely used tools in experimental psychology, bridging cognitive theory, mathematical modeling, and empirical research.
The increasing prominence of DDMs has spurred both conceptual and methodological developments. This symposium focuses on recent theoretical and computational advancements in the modeling of DDMs, including advances in estimation techniques, alternative stochastic dynamics to the Wiener process, and integrations with other modeling frameworks. Together, we aim to highlight new directions for enhancing theoretical and conceptual precision, modeling flexibility, and computational efficiency.
This symposium is the first part of a two-part series on DDMs at TeaP. While Part I emphasizes model development, theoretical extensions, and computational innovation, Part II turns to applied research, demonstrating how DDMs can help us better understand cognitive processes across different populations and domains. By being open to scholars from all areas of experimental psychology, the series offers a forum for presenting new ideas, establishing collaborations, and identifying future directions in the modeling of human cognition.