Predictors of Individual Differences in Infants’ Learning Performance: A Neural Networks Conceptualization
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2019-01-28
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en
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Abstract
Predictive 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.
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Faculteit der Sociale Wetenschappen