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Thesis: Exploring if EEG can be used to constrain cognitive models.

Bierens de Haan, D.J. (2017) Thesis: Exploring if EEG can be used to constrain cognitive models. Master's Thesis / Essay, Human-Machine Communication.

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Abstract

Cognitive architectures such as ACT-R can be used to get a better understanding of the underlying processes in human cognition, but they can be hard to evaluate based on behavioral measures alone. Therefore, neural data can be used as additional information to inform models. There is a good connection between ACT-R and fMRI, but the connection between EEG is a lot less elaborated. EEG can be especially useful to get a better understanding of fast paced tasks because of the excellent temporal resolution of EEG. In this project we examined whether EEG could be used to constrain cognitive models. This is examined by evaluating the results from a model-based EEG analysis of an algebra solving task. Model-based analysis is a method to investigate new connections between model constructs and brain areas or to evaluate existing mappings between model constructs and brain areas. Model-based analysis uses the predictions from a cognitive model as regressors in a general linear model to examine the connections between EEG data and the model constructs. The model-based EEG analysis in this project showed promising results; the visual ACT-R module showed correlation between EEG data channels around the fusiform gyrus in the occipital lobe and the manual ACT-R module showed correlations between EEG channels around the motor cortex. These results show the connections between EEG data and model constructs and suggests that EEG data could be used to constrain cognitive models. However, the imaginal and declarative ACT-R modules did not show correlations between the module predictions and the EEG data channels around areas where we expected them. Therefore we suggest that our cognitive model of the algebra task needs refinement.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Human-Machine Communication
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:29
Last Modified: 15 Feb 2018 08:29
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/15359

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