Predictors of Individual Differences in Infants’ Learning Performance: A Neural Networks Conceptualization

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dc.contributor.advisorKwisthout, Johan
dc.contributor.advisorRutar, Danaja
dc.contributor.authorMannes, Julius
dc.date.issued2019-01-28
dc.description.abstractPredictive Processing theory, according to which the brain constantly generates and tests hypotheses about the environment, is still unable to fully account for individual learning differences. In the theory, models are modified based upon a prediction error. A neurophysiological proxy for the prediction error in infants might be the ERP component Nc. In an attempt to extend the Predictive Processing theory on its account for individual learning differences, the current study investigated neurophysiological correlates of the onset latency of Nc in infants. A convolutional neural network was trained on EEG data to classify the data as either an early or late onset latency of Nc. Subsequently, the LRP technique was applied to gain insight into the data points that were relevant in the classification process. Conclusive results were not obtained, due to the network showing indications of overfitting and no evidence in favor or against the hypothesis was found.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/10864
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.titlePredictors of Individual Differences in Infants’ Learning Performance: A Neural Networks Conceptualizationen_US
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