Bayesian Cognitive Modeling: A Practical Course
Abstract
Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.
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The Bayes factors for Hypotheses 1 and 2 fell in the range of 1-3 and can be regarded as rather small.
“However, it should be noted that both the Bayes factors for H1 and H2 fell in the range of 1 -3 and, as such, can be regarded as rather small (Lee & Wagenmaker, 2014).”
From A Three-Body Problem: The effects of foreign language anxiety, enjoyment, and boredom on academic achievement (2023) · cites this paper as Lee & Wagenmaker, 2014
Bayes factors for target emotions (BF01=4.307), non-target emotions (BF01=369.726), and no-emotions (BF01=195.5) all indicate substantial evidence for the null hypothesis of absence of a gender difference.
“Here, the Bayes factors were respectively BF01 = 4.307 (target emotions), BF01 = 369.726 (non-target emotions), and BF01 = 195.5 (no-emotions), all indicating substantial evidence for the null hypothesis, in this case, the absence of a gender difference [45].”
From Gender differences in emotion perception and self-reported emotional intelligence: A test of the emotion sensitivity hypothesis (2018) · cites this paper as [45]
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Cited by 1,795
- Authors
- Lee, M. D., & Wagenmakers, E.-J.
- Affiliations
- University of California, Irvine (US)
- University of Amsterdam (NL)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2014-04-03
- OpenAlex
- W4231324690
- Cited by
- 1,795
- References
- 0
- Citations by year
- 2026: 184 · 2025: 253 · 2024: 190 · 2023: 200 · 2022: 233 · 2021: 214 · 2020: 178 · 2019: 122 · 2018: 98 · 2017: 61 · 2016: 33 · 2015: 13 · 2014: 13 · 2013: 1
- FWCI
- 8.89
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- Bayesian inference, parameter estimation, model selection, WinBUGS, JAGS, cognitive science, experimental psychology, Bayesian analysis
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