Toward a Mechanistic Account of Cognitive Inhibition: Integrating Computational Modeling and Psychometrics

09:00 - 10:30

Part I: Advancing Diffusion Decision Modeling: From Theoretical Extensions to Computational Innovations

Room: HSZ - N4

Chair/s:

Nicola Schneider

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.

Submission 190

Toward a Mechanistic Account of Cognitive Inhibition: Integrating Computational Modeling and Psychometrics

SymposiumTalk-03

Presented by: Jiashun Wang

Jiashun Wang ¹, Lasse Elsemüller ², Mischa von Krause ², Tuo Liu ³, Wanke Pan ⁴, Christopher Donkin ¹

¹ LMU Munich, Germany

² Heidelberg University, Germany

³ Goethe-Universität Frankfurt am Main, Germany

⁴ Nanjing Normal University, China

Inhibition plays a central role in explaining how individuals resolve conflict during decision making. Traditionally, research has estimated inhibition using reaction time (RT) differences between congruent and incongruent conditions in conflict tasks (e.g., Simon, Flanker, and Stroop tasks). However, this approach overlooks accuracy information and conflates inhibition with other processes such as processing speed and non-decision time. To address these limitations, we adapted the Competing Accumulators (CA) model to quantify cognitive inhibition—the ability to suppress task-irrelevant or conflicting information—within a formal decision-making framework. Our new model provides a cognitively grounded and psychometrically coherent method that disentangles inhibition from other cognitive processes.

Combining computational modeling and structural equation modeling (SEM) across multiple conflict tasks, we tried to identify a coherent latent inhibition factor. We also applied the same modeling framework to large-scale behavioral data from over 100,000 participants in the Implicit Association Task, revealing distinct lifespan trajectories of top-down inhibition. Together, these findings demonstrate how computational modeling can uncover latent cognitive mechanisms and shed some light on a mechanistic understanding of individual differences in cognitive inhibition.

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