Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- Part II Human Behaviour
- 4 Behavioural Data Science for Understanding Human Decisions, Choices and Judgement
- 5 Psychological Theories of Decision-Making under Risk
- 6 Prediction-Oriented Behavioural Research and Its Relationship to Classical Decision Research
- 7 The ABCs of Behavioural Influence
- 8 Word and Sentence Embedding Methods for Studying Human Behaviour
- 9 Predictive Bayesian Modelling in Cognitive Sciences
- 10 Human Aspects of AI-Related Risks
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
9 - Predictive Bayesian Modelling in Cognitive Sciences
from Part II - Human Behaviour
Published online by Cambridge University Press: 29 May 2026
Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- Part II Human Behaviour
- 4 Behavioural Data Science for Understanding Human Decisions, Choices and Judgement
- 5 Psychological Theories of Decision-Making under Risk
- 6 Prediction-Oriented Behavioural Research and Its Relationship to Classical Decision Research
- 7 The ABCs of Behavioural Influence
- 8 Word and Sentence Embedding Methods for Studying Human Behaviour
- 9 Predictive Bayesian Modelling in Cognitive Sciences
- 10 Human Aspects of AI-Related Risks
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
Summary
The use of Bayesian modelling has been growing in the cognitive sciences, as it provides a flexible framework for modelling complex and heterogeneous data, as well as incorporating prior knowledge and uncertainty into the modelling process. In this chapter, we focus on predictive Bayesian modelling, which allows for the construction of models that can make accurate and meaningful predictions about future events, behaviours or outcomes. We begin by providing a brief overview of Bayesian statistics and the key concepts necessary for understanding predictive Bayesian modelling. We then discuss the benefits and limitations of using predictive Bayesian modelling in cognitive science research. Next, we explore several examples of how predictive Bayesian modelling has been applied to different areas of cognitive science, including perception, memory, decision-making and language. We discuss how these models have contributed to our understanding of cognitive processes and how they can be used to make predictions about human behaviour. Finally, we outline some of the challenges and future directions for predictive Bayesian modelling in cognitive science research. We discuss the importance of evaluating model fit and model comparison, the need for more accurate and informative prior distributions and the potential for combining predictive Bayesian modelling with other methods, such as machine learning.
Keywords
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- Information
- The Cambridge Handbook of Behavioural Data Science , pp. 157 - 170Publisher: Cambridge University PressPrint publication year: 2026
References
Baker, C. L., Saxe, R. & Tenenbaum, J. B. (2009). Action understanding as inverse planning. Cognition, 113(3), 329–349.10.1016/j.cognition.2009.07.005CrossRefGoogle ScholarPubMed
van den Berg, R., Shin, H., Chou, W.-C., George, R. & Ma, W. J. (2012). Variability in encoding precision accounts for visual short-term memory limitations. Proceedings of the National Academy of Sciences, 109(22), 8780–8785.10.1073/pnas.1117465109CrossRefGoogle ScholarPubMed
Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859–877.10.1080/01621459.2017.1285773CrossRefGoogle Scholar
Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.Google Scholar
Blundell, C., Cornebise, J., Kavukcuoglu, K. & Wierstra, D. (2015). Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424.Google Scholar
Boldt, A. & Yeung, N. (2015). Shared neural markers of decision confidence and error detection. Journal of Neuroscience, 35(8), 3478–3484.10.1523/JNEUROSCI.0797-14.2015CrossRefGoogle ScholarPubMed
Drugowitsch, J., Moreno-Bote, R. & Pouget, A. (2012). The cost of accumulating evidence in perceptual decision making. Journal of Neuroscience, 32(11), 3612–3628.CrossRefGoogle ScholarPubMed
Ernst, M. O. & Banks, M. S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415(6870), 429–433.10.1038/415429aCrossRefGoogle Scholar
Frank, S. L., Otten, L. J., Galli, G. & Vigliocco, G. (2015). The ERP response to the amount of information conveyed by words in sentences. Brain and Language, 140, 1–11.10.1016/j.bandl.2014.10.006CrossRefGoogle Scholar
Friston, K., Parr, T. & de Vries, B. (2017). The graphical brain: Belief propagation and active inference. Network Neuroscience, 1(4), 381–414.10.1162/NETN_a_00018CrossRefGoogle ScholarPubMed
Futrell, R., Gibson, E. & Levy, R. (2019). Lossy-context surprisal: An information-theoretic model of memory effects in sentence processing. Cognitive Science, 44(3), e12814.10.1111/cogs.12814CrossRefGoogle Scholar
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayesian data analysis (3rd ed.). Chapman and Hall/CRC.Google Scholar
Gelman, A. & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.10.1017/CBO9780511790942CrossRefGoogle Scholar
Gelman, A., Hwang, J. & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24(6), 997–1016.10.1007/s11222-013-9416-2CrossRefGoogle Scholar
Gershman, S. J. (2016). Empirical priors for reinforcement learning models. Journal of Mathematical Psychology, 71, 1–6.10.1016/j.jmp.2016.01.006CrossRefGoogle Scholar
Ghavamzadeh, M., Mannor, S., Pineau, J. & Tamar, A. (2015). Bayesian reinforcement learning: A survey. Foundations and Trends in Machine Learning, 8(5–6), 359–483.10.1561/2200000049CrossRefGoogle Scholar
Goodman, N. D. & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences, 20(11), 818–829.10.1016/j.tics.2016.08.005CrossRefGoogle ScholarPubMed
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A. & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8), 357–364.10.1016/j.tics.2010.05.004CrossRefGoogle ScholarPubMed
Griffiths, T. L. & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9), 767–773.10.1111/j.1467-9280.2006.01780.xCrossRefGoogle ScholarPubMed
Haefner, R. M., Beck, J., Savin, C., Salmasi, M. & Pitkow, X. (2024). How does the brain compute with probabilities? arXiv preprint arXiv:2409.02709.Google Scholar
Hemmer, P. & Steyvers, M. (2009). A Bayesian account of reconstructive memory. Topics in Cognitive Science, 1(1), 189–202.10.1111/j.1756-8765.2008.01010.xCrossRefGoogle ScholarPubMed
Jones, M. & Love, B. C. (2011). Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34(4), 169–188.10.1017/S0140525X10003134CrossRefGoogle ScholarPubMed
Kahneman, D. & Tversky, A. (1982). On the study of statistical intuitions. Cognition, 11(2), 123–141.10.1016/0010-0277(82)90022-1CrossRefGoogle Scholar
Knill, D. C. (2007). Bayesian models of sensory cue integration, in Bayesian brain: Probabilistic approaches to neural coding, edited by Doya, K., Ishii, S., Pouget, A. & Rao, R. P. N.. MIT Press, 198–206.Google Scholar
Körding, K. P. & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nature, 427(6971), 244–247.10.1038/nature02169CrossRefGoogle ScholarPubMed
Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2nd ed.). Academic Press.Google Scholar
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338.10.1126/science.aab3050CrossRefGoogle ScholarPubMed
Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126–1177.10.1016/j.cognition.2007.05.006CrossRefGoogle ScholarPubMed
Lieder, F. & Griffiths, T. L. (2020). Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, e1.10.1017/S0140525X1900061XCrossRefGoogle Scholar
Ma, W. J., Beck, J. M., Latham, P. E. & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9(11), 1432–1438.10.1038/nn1790CrossRefGoogle ScholarPubMed
Papamakarios, G., Sterratt, D. C. & Murray, I. (2019). Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows. Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, Naha, Okinawa, Japan, 16–18 April, 837–848.Google Scholar
Ratcliff, R. & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922.10.1162/neco.2008.12-06-420CrossRefGoogle ScholarPubMed
Robert, C. P. & Casella, G. (2004). Monte Carlo statistical methods (2nd ed.). Springer.10.1007/978-1-4757-4145-2CrossRefGoogle Scholar
Sanborn, A. N. & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883–893.10.1016/j.tics.2016.10.003CrossRefGoogle ScholarPubMed
Shiffrin, R. M., Lee, M. D., Kim, W. & Wagenmakers, E. J. (2008). A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32(8), 1248–1284.10.1080/03640210802414826CrossRefGoogle ScholarPubMed
Shiffrin, R. M. & Steyvers, M. (1997). A model for recognition memory: REM – retrieving effectively from memory. Psychonomic Bulletin & Review, 4(2), 145–166.10.3758/BF03209391CrossRefGoogle Scholar
Tenenbaum, J. B., Griffiths, T. L. & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10(7), 309–318.10.1016/j.tics.2006.05.009CrossRefGoogle ScholarPubMed
Turner, B. M., Dennis, S. & Van Zandt, T. (2013). Likelihood-free Bayesian analysis of memory models. Psychological Review, 120(3), 667–678.10.1037/a0032458CrossRefGoogle ScholarPubMed
Van der Maas, H. L. J., Kan, K.-J., Marsman, M. & Stevenson, C. E. (2020). Cognitive psychology meets psychometrics: The mutual benefits of two disciplines. Frontiers in Psychology, 11, 1960.Google Scholar
Vehtari, A., Gelman, A. & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413–1432.10.1007/s11222-016-9696-4CrossRefGoogle Scholar
Weiss, Y., Simoncelli, E. P. & Adelson, E. H. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5(6), 598–604.10.1038/nn0602-858CrossRefGoogle ScholarPubMed
Wolpert, D. M. (2007). Probabilistic models in human sensorimotor control. Human Movement Science, 26(4), 511–524.10.1016/j.humov.2007.05.005CrossRefGoogle ScholarPubMed
Xu, F. & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological Review, 114(2), 245–272.10.1037/0033-295X.114.2.245CrossRefGoogle ScholarPubMed
Zhang, W. & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453(7192), 233–235.10.1038/nature06860CrossRefGoogle ScholarPubMed
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