The Effect of Structural (mis)alignment of Temporal Generative Models on Prediction Error
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2021-06-01
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en
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Abstract
It is often assumed that predictive processing agents can e ectively learn the dynam-
ics of an environmental variable (e.g. temperature) that changes over time at multiple
timescales using an internal hierarchical generative model, where higher layers represent
slower, more abstract, processes than lower layers. In this research paper, we investigate
how accurately a perceptive inference agent is able to learn and predict these dynamic
environment variables using such a model. More speci cally, we answer the follow-
ing question: What is the e ect of structural (mis)alignment of hierarchical temporal
generative models on prediction error?
To do this, we used three scenarios. In the rst scenario, a generative model was
structurally aligned with the generative process. In the second scenario, we created
a generative model that had one or more layers removed or added with respect to the
structurally aligned model. In the third scenario, the generative model had a layer which
was smaller or larger in comparison to the structurally aligned agent. In particular, we
looked at how well the agents managed to learn these dynamics in terms of prediction
error minimization.
We found that generally models with less layers learn a more accurate prediction of
the environment, but only if the number of unique state combinations remains the same
as with the structurally aligned agent. If a layer has less states than the structurally
aligned equivalent, this layer and the layers above it are not able to learn a represen-
tation of the process at all. If a layer has more states, however, this layer can learn a
representation but only if the layer size is an integer multiple of the structurally aligned
equivalent.
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Faculteit der Sociale Wetenschappen