Bursting with Error: Dealing with unexpected prediction error in Babybots

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dc.contributor.advisorKwisthout, J.H.P.
dc.contributor.authorWolff, E.J. de
dc.date.issued2017-01-30
dc.description.abstractOur research builds on and adds to the prediction processing theory, specifically to model updating and model revision. We propose a hyperparameter approach to model updating at the computational level, designed to add precision to beliefs, which we tested via computer simulations. We also introduce the concept of unexpected high prediction error, which can be used as a signal to revise the generative model of agents. The latter links two of the proposed methods the brain uses to reduce future prediction error.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/4375
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.titleBursting with Error: Dealing with unexpected prediction error in Babybotsen_US
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