Module Code:

PS931

Module Name:

Bayesian Approaches in Behavioural Science

Module Credits (CATS):

15

Module Convener

Adam Sanborn

Module Teachers

Adam Sanborn, Pete Trimmer

Module Aims

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.

The purpose of the module is to introduce Bayesian approaches to statistics and modelling of behaviour, and the approximations that make them work in practice.

Learning Outcomes

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

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

Assessed by:

• Class tests and essay
  
  
• Assignment and class tests
  
  
• Class tests, assignment, and essay
  
  
• Class tests and essay

Module Work Load

Module Length

9 weeks

Lectures

9 lectures of 2 hours each

Seminars

4 seminars of 2 hours each

Attendance

Attendance at lectures and seminars is compulsory

Module Assessment

Assessed work:

Bayesian data analysis assignment – involves performing a Bayesian data analysis and writing up the results

Essay – 2000 words, based on discussing Bayesian models of cognition

Weighting:

42%


42%

Exams:

8 Class Tests - multiple-choice questions, each test counts for 2% of final grade

Weighting:

16%

Module Programme

Lectures

1. Introduction to Bayesian statistics
2. Approximations in Bayesian statistics
3. Estimation vs. testing and more complex models
4. Choosing priors
5. Rationality and evolution
6. Animal behaviour I
7. Animal behaviour II
8. Evidence for and against Bayesian models of cognition and behaviour
9. Approximations in Bayesian models of cognition and behaviour

Seminars

- Following Lecture 2: Hands-on Bayesian data analysis in R

- Following Lecture 4: Hands-on Bayesian data analysis in R

- Following Lecture 7: Discussion of Bayesian models of behaviour and cognition

- Following Lecture 9: Discussion of Bayesian models of behaviour and cognition

Module Reading List

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

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

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

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.

Shiffrin, R. M. (2016). Cognitive modelling in perception and memory. Routledge, Taylor & Francis Group.

McNamara, J., & Houston, A. The application of statistical decision theory to animal behaviour. Journal of Theoretical Biology, 85(4), 673-690

Trimmer, P. C., et al. Decision-making under uncertainty: biases and Bayesians, Animal Cognition, 14(4), 465-476

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

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

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

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

Zhu, J., Sanborn, A. N, & Chater, N. The Bayesian sampler: generic Bayesian inference causes incoherence in human probability judgement. Psychological Review, 127(5), 719-748

Griffiths, T. L., & Tenenbaum, J. B. Optimal Predictions in Everyday Cognition. Psychological Science, 17(9), 767-773

Marcus, G. F., & Davis, E. How Robust Are Probabilistic Models of Higher-Level Cognition? Psychological Science, 24(12), 2351-2360