Online script for the methods course (neuroscience minor)

Ellis, Andrew (2021). Online script for the methods course (neuroscience minor). [Software & Other Digital Items] 10.5281/zenodo.6403207

Archive neuroscicomplab-1.0.zip - Draft Version Available under License BORIS Standard License. Download (299MB)

In recent years, the fields of neuroscience and mathematical modelling of cognition have been combined to create the new field of model-based cognitive neuroscience. Mathematical modelling of behaviour can be used to inform the analysis of brain data, and, in turn, brain data can provide constraints for mathematical models. This course will offer a hands-on introduction to advanced data analysis techniques and mathematical models of behaviour, with a particular focus on

- Introduction to Bayesian data analysis
- Bayesian multilevel regression models
- Evidence accumulation models of decision making
- Bayesian models of cognition

We will start with an introduction Bayesian data analysis, and then progress to multilevel regression models for the analysis of behavioural data (binary choices and response times). This will be followed by an introduction to the diffusion decision model (DDM), a model of evidence accumulation for decision making, which is often combined with brain imaging data. We will then learn how fit a basic DDM in a Bayesian multilevel framework. After this, we look at Bayesian models of cognition, which are often used to investigate how human behaviour deviates from optimal, rational behaviour.

In this course, students will work with the R and Stan programming languages perform data analysis and simulations, and will be required to complete homework assignments on a weekly basis. Evaluation will be based on the assignments.

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