The E ect of Detail on Predictive Models A search for the optimal granularity

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dc.contributor.advisorRutar, D.
dc.contributor.advisorKwisthout, J.H.P.
dc.contributor.authorGeertjes, Lennart
dc.date.issued2020-03-01
dc.description.abstractPredictive processing is a theory in Cognitive Neuroscience which states that informa- tion processing in the brain is performed via predicting future inputs and comparing them to the actual observations. Recently it has been proposed that knowing what determines the level of detail of the predictions is important for understanding the predictive models in our brain. For this research project, a simulation was built in which multiple agents made predictions at di erent levels of detail. The results of this simulation were analysed to see if an optimal level of detail could be found, where the prediction error is low while predictions are su ciently informative. In the end the optimum could not be determined, and it remains an open question how exactly the brain balances expected prediction error with informativeness of predictions.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/12650
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleThe E ect of Detail on Predictive Models A search for the optimal granularityen_US
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