McDonagh, Eoghan (2023) Tracking pupil size to determine mental processing in associative recognition. Bachelor's Thesis, Artificial Intelligence.
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Abstract
In the past number of decades, several models have been put forth to explain the cognitive mechanisms behind associative recognition. These models have grown more intricate over time, with brain imaging techniques such as EEG and MEG providing a clearer picture of the nature and duration of the individual stages that comprise the associative recognition process. Our study aimed to identify those same stages using pupillary data, which we collected and then analysed through a combination of hidden semi-Markov models and generalized additive mixed models. We also hoped to investigate the usefulness of pupillary data as a cognitive analysis tool, particularly in terms of its ability to identify discrete processing stages. The resulting models were broadly comparable to those produced by earlier studies, thereby supporting the applicability of pupillary data in this and similar fields.
| Item Type: | Thesis (Bachelor's Thesis) |
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| Supervisor name: | Borst, J.P. and Krause, J. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 31 Jul 2023 07:31 |
| Last Modified: | 31 Jul 2023 07:31 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/30967 |
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