The Development of Bayesian generative models
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2020-01-31
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en
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Abstract
The predictive processing (PP) framework is one of the leading theories to explain cognition.
According to PP, the brain continuously predicts sensory inputs given its generative model of the
world. An interesting question is how such a generative model of the world is developed. Two
approaches for developing a generative model are model updating and model revision. Model
updating refers to updating the probabilities over the hypothesis in the model. Compared to
that, model revision can take place by constructing or reducing a generative model. While
most research focuses on the model updating, in this Bachelor thesis, we will investigate the
development of a Bayesian model by model revision. Particularly interesting is the question,
how development of a generative model compares in terms of accuracy and causal relations
between the two existing model revision processes. Model reduction and model construction are
compared by using a computer simulation. In general, neither of the approaches converge to the
`true' model of the environment. However, both approaches developed a model that captures
the association rules of the environment. Despite showing that model development can underlie
the process of model reduction, as well as model construction, more research in complex areas
is necessary to generalize these fi ndings.
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Faculteit der Sociale Wetenschappen