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11:00 - 12:30
Parallel sessions 5
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11:00 - 12:30
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 how people make decisions. 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 explanatory power and versatility of the DDM have made it indispensable for testing psychological theories. By illustrating how the model bridges the gap between quantitative modeling and psychological theory, we aim to highlight the value of DDMs for understanding individual and group differences, clinical dysfunctions, and social-cognitive processes. Together, these studies illustrate the breadth of DDM applications across experimental psychology and highlight how cognitive modeling can inform theoretical and applied research alike.
This symposium is the second 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 272
Applying the Diffusion Model to Implicit Weight Bias: No Credible Evidence for BMI-Based Differences
SymposiumTalk-01
Presented by: Katja Pollak
Katja Pollak, Hanna Wachten, Julius Fenn, Raphael Hartmann, Constantin Meyer-Grant, Jana Strahler, Andrea Kiesel
University of Freiburg, Germany
Implicit weight bias, defined as an automatic, unconscious negative attitude toward individuals with overweight or obesity, can be assessed with reaction time tasks such as the Affective Priming Task. Previous research using the diffusion model (DM) to analyze data from this task has shown that implicit weight bias manifests as a starting point bias, i.e., participants tended to expect a negative word more often after an overweight than after a normalweight prime. To test whether this a priori expectation is moderated by participants’ own Body-Mass Index (BMI), we recruited two different groups via Prolific: a low-BMI group (self-reported BMI < 25; N = 50) and a high-BMI group (self-reported BMI > 30, N = 45). Both groups completed the Affective Priming Task with images depicting four gender-matched body types (including obese) and one neutral rectangle. Comparing responses to targets after obese versus neutral primes, neither group showed an implicit weight bias. Bayesian hierarchical diffusion modeling mirrored this finding: in neither group did starting points or other parameters of the DM differ credibly between obese and neutral primes. Descriptively, however, starting points followed the expected direction – closer to the positive boundary after obese compared to neutral primes in the high-BMI group and closer to the negative boundary in the low-BMI group – potentially indicating a weak but non-credible moderation by BMI. We discuss possible explanations for this null finding related to sample and task characteristics.