This workshop is developed by Stephen Rhodes (@stephenrho) and Julia Haaf (@JuliaHaaf) at the Department of Psychological Sciences, University of Missouri.
Cognitive process models provide a powerful tool to disentangle different cognitive processes contributing to the same observable responses. These models are successfully applied in many fields of psychology (e.g., memory, decision making, and social cognition). This two day workshop covers the essentials of cognitive modeling using the programming language R. Attendees will be introduced to several commonly used models in cognitive psychology and how to fit them using both maximum likelihood and hierarchical Bayesian methods. While the workshop is specifically aimed at graduate students in cognitive psychology, this workshop will be of interest to anyone looking to build their R modeling skills.
These are not absolutely required but would be useful:
- Passing familiarity with the
Rprogramming language. You can find a free online introduction here. - Familiarity with statistical concepts such as likelihood.
| Day | Topic |
|---|---|
| 1. Morning | Introduction to R; Introduction to Maximum Likelihood |
| 1. Afternoon | Modeling groups: Signal-detection theory and Multinomial Processing Tree Models I |
| 2. Morning | Modeling individuals: SDT and MPT II |
| 2. Afternoon | Bayesian hierarchical process modeling |