Learning to Predict with Contextual Variables: The Importance of Salience
| dc.contributor.advisor | Kwisthout, J.H.P. | |
| dc.contributor.author | Oetringer, D.A. | |
| dc.date.issued | 2017-06-29 | |
| dc.description.abstract | The Predictive Processing account offers a possible explanation for how the human brain works. Various aspects of this account have been re- searched a lot, but not so much on the computational mechanisms by which generative models are learnt and adapted. In this Bachelor thesis we offer a candidate explanation for how contextual variables could be learnt and processed in the computational explanation of Predictive Processing, as proposed by Kwisthout et al (2017). This proposed explanation provides a mechanism for keeping track of the salience of combinations of phenomena. The proposed explanation leads to generative models that lead to overall lower (yet not minimal) Prediction Errors than more naive methods. However, how to deal with more complex environments has only been discussed theoretically. Thus, more experiments are needed with more complex environments. | en_US |
| dc.identifier.uri | http://theses.ubn.ru.nl/handle/123456789/5217 | |
| 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 | Learning to Predict with Contextual Variables: The Importance of Salience | en_US |
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