Course Description
Computational cognitive modeling is an approach to explaining behavioral data, by statistically breaking it down into latent and theoretically meaningful parameters. These parameters describe how people make decisions (e.g., how well or how hesitantly they make choices, or how much they value different choice options, etc.). One conceptual strength of “cognitive models” is that their parameters can often simultaneously explain multiple types of data that stem from the same decision; for example: which choice a participant made this trial and how fast they made it (evidence accumulation models) or: which choice a participant made this trial and which choices they made before (reinforcement-learning models). A cost of applying them is that they make strong assumptions about the structure of the to-be-explained data (e.g., that task conditions are relatively stationary, or that observations are interchangeable). Luckily, many experiments fulfill such criteria and cognitive models are flexible (i.e., you can relax some assumptions in exchange for others), meaning that cognitive models can be applied in a wide variety of research domains.
In this workshop, we will provide a conceptual introduction to (computational) cognitive modelling with a particular emphasis on one class of cognitive models: evidence accumulation models. We will discuss what cognitive models are, when and why they are useful and how they are used in practice. In a practical, we will subsequently allow you to get hands-on experience with fitting such a model to data and trying to draw inferences from the results.
Prerequisites
Participants should be proficient (e.g., at least capable of preprocessing and simple analyses) in R.
Reading Materials
Optional
Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. elife, 8, e49547. https://doi.org/10.7554/eLife.49547
Skip:
- pages 5 starting at “design good models” until page 17 (restart reading at “Run the experiment and analyze the actual data”)
- skip the blue boxes
Capacity
This course has a maximum capacity of 35 participants.
Time and Location
This workshop will be held on-site only at Eindhoven University of Technology on April 20, 2026. Details will be provided to all attendees over email after registration for the workshop.
Workshops start from 9:30 to 16:30 with a lunch break from 12:30 to 13:30. Lunch will not be provided but can be purchased at the university canteen or the on-campus supermarket.
Registration
To register for this workshop, please complete the following form by April 2. Note that your registration will be considered finalized only after receiving a confirmation email. The registration link will remain open after this date if spots are still available.
Instructor
Dr. Leendert van Maanen
Leendert van Maanen is Associate Professor of Cognition and AI at Utrecht University. He is an expert on formal modeling of response times, with a focus on method development and application to understand the neural architecture underlying decisions. He has developed novel methodologies to test for multiple strategies in behavioral data which have been applied to provide new insights in perceptual decision-making behavior under time pressure, economic judgements, and causal reasoning. In his current work, he aims to integrate the modeling of neural and behavioral data to understand more complex tasks, including tasks that involve multiple cognitive strategies. Additionally, he uses cognitive models to study how people use AI systems to make decisions.
Dominik Bachmann
Dominik Bachmann is PhD student at the Institute for Logic, Language and Computation (University of Amsterdam) as well as guest researcher at the Department of Experimental Psychology at Utrecht University. Under the supervision of Leendert, he works on ways to (covertly) assess humans and language models.