Learning to Predict with Contextual Variables: The Importance of Salience
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2017-06-29
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en
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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.
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Faculteit der Sociale Wetenschappen