Model Updating and Model Revision in Volatile Environments
| dc.contributor.advisor | Rutar, D. | |
| dc.contributor.author | Burgos, A.I.T. | |
| dc.date.issued | 2020-02-06 | |
| dc.description.abstract | The theoretical framework of predictive processing describes fundamental concepts of human cogni- tion. Although the framework has increased in popularity in recent years, it remains to account for tasks requiring higher-level cognition, such as how humans deal with unstable environments. This thesis aims to be a point of guidance and a primary step in research on how human agents adapt their generative model when exposed to uncertain environments. The exploratory research was carried out by the use of a constructed computer simulation. The goal was to determine if subjective experience of volatility had an impact on whether performing model updating or model revision was preferred to minimise the prediction error of the generative model. It was found that revision was preferred for both low and high volatility. However, it is argued that further research, including modi cations to the simulation, experimental setup, or both, is needed to better understand how the bene ts of performing model updating and model revision are in uenced by the degree of volatility. | en_US |
| dc.embargo.lift | 10000-01-01 | |
| dc.embargo.type | Permanent embargo | en_US |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/12597 | |
| dc.language.iso | en | en_US |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | en_US |
| dc.thesis.specialisation | Bachelor Artificial Intelligence | en_US |
| dc.thesis.studyprogramme | Artificial Intelligence | en_US |
| dc.thesis.type | Bachelor | en_US |
| dc.title | Model Updating and Model Revision in Volatile Environments | en_US |
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