Model Updating and Model Revision in Volatile Environments
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2020-02-06
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en
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Abstract
The theoretical framework of predictive processing describes fundamental concepts of human cogni-
tion. Although the framework has increased in popularity in recent years, it remains to account for
tasks requiring higher-level cognition, such as how humans deal with unstable environments. This
thesis aims to be a point of guidance and a primary step in research on how human agents adapt their
generative model when exposed to uncertain environments. The exploratory research was carried out
by the use of a constructed computer simulation. The goal was to determine if subjective experience
of volatility had an impact on whether performing model updating or model revision was preferred
to minimise the prediction error of the generative model. It was found that revision was preferred
for both low and high volatility. However, it is argued that further research, including modi cations
to the simulation, experimental setup, or both, is needed to better understand how the bene ts of
performing model updating and model revision are in
uenced by the degree of volatility.
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Faculteit der Sociale Wetenschappen