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Modeling and Methods in Human Behavioural Neuroscience
Last Updated: 2026-02-05 15:40:35
Abstract
The course presents models in human behavioral neuroscience and methods to:1) Adapt the models to embed hypotheses;2) Make model-based predictions;3) Use models when designing data collections that verify/disprove predictions
Objective
At the end of this module students should know: • different types of models used in human behavioral neuroscience, their features and their limits • how to use models to estimate expected human behavioural outcomes or to interpret behavioural data • how to implement models and methods via software (Matlab)
Content
1. Linear time-invariant model and their practical applications on neuroscience systems (e.g. sensory input, motor control). From equations to block diagram representation. 2. Psychophysical methods to test human perceptual response and statistical models of behaviour (e.g. Bayesian model). Examples from tasks probing perceptual responses. 3. How the brain controls our body through internal models (feedforward and feedback). Examples from motor and balance tasks. The optimal observer as a model of how the human brain interprets inputs, plans and compares actions and finally executes them. The course will combine theoretical and practical knowledge on how to implement models and techniques via software on datasets (Matlab)
General Information
- Language
- English
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 120 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Modeling and Methods in Human Behavioural Neuroscience |
|
2 h weekly |