Introductory description

Bayesian approaches have made important contributions to Behavioural Science, both as statistical models for empirical data, and as cognitive models of how people perform tasks. As statistical models, Bayesian methods are particularly important for establishing the absence of an effect, which is difficult for standard statistical methods to do. As cognitive models, Bayesian methods prescribe what an agent should do in a task, as as such provide important benchmarks against which to compare human behaviour. In both domains, approximations play an important role: allowing the practical use of complex statistical models, and providing a route to explain deviations of human and animal behaviour from the Bayesian ideal.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module will introduce Bayesian statistics and Bayesian models of behaviour. In the lectures on Bayesian statistics, topics such as mixed models, estimation vs. testing, approximation, and choosing priors will be covered. In the lectures on Bayesian models of behaviour, topics such as the evidence for and against these models, the role of approximations, evolution and animal behaviour will be covered.

Learning outcomes

By the end of the module, students should be able to:

• Apply common Bayesian statistical methods to analyse empirical data in behavioural science.
• Gain familiarity with Bayesian approaches to modelling cognition and behaviour.
• Understand the evidence against Bayesian approaches to modelling cognition and behaviour, and the extent to which approximations can account for this evidence.
• Understand the logic of Bayesian statistics and the necessity of approximation methods.

Indicative reading list

Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9), 767-773.

Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.

Lambert, B. (2018). A student’s guide to Bayesian statistics. SAGE.

Lee, M. D., & Wagenmakers, E. J. (2014). Bayesian cognitive modeling: A practical course. Cambridge University Press.

Ma, W. J. (2012). Organizing probabilistic models of perception. Trends in Cognitive Sciences, 16(10), 511-518.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed.). Taylor and Francis, CRC Press.

O’Hagan, A. (2019). Expert knowledge elicitation: subjective but scientific. The American Statistician, 73(sup1), 69-81.

Sanborn, A. N., & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883-893.

Sanborn, A. N., & Griffiths, T. L. (2015). Exploring the structure of mental representations by implementing computer algorithms with people. In Cognitive Modeling in Perception and Memory: A Festschrift for Richard M. Shiffrin (pp. 212-228). Taylor and Francis Inc.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.

Vincent, B. T. (2015). A tutorial on Bayesian models of perception. Journal of Mathematical Psychology, 66, 103-114.

Zhu, J., Sanborn, A. N. and Chater, N. (2020). The Bayesian sampler: generic Bayesian inference causes incoherence in human probability judgments. Psychological Review.

Interdisciplinary

Incorporates elements of Psychology, Economics, Business, and Statistics

Subject specific skills

Understand the logic of Bayesian statistics and the necessity of approximation methods.
Apply common Bayesian statistical methods to analyse empirical data in behavioural science.

Transferable skills

Understand the logic of Bayesian statistics and the necessity of approximation methods.
Applying common Bayesian statistical methods to analyse empirical data generally.