Improving Parameter Recovery for a Bayesian Model of Verticality Perception
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2022-06-17
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en
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Abstract
Alberts et al. 2016 proposed a Bayesian model designed to decompose
verticality perception into constituent components whose contribution
on the integrated percept can then be quantified using a psychometric
experiment called the rod-and-frame task. Potentially, the model
can be utilized as a diagnostic instrument in a clinical setting to determine
the effectiveness of treatments related to vestibular disorders.
As of now, the number of trials that is needed to estimate the model’s
parameters using the rod-and-frame task deems this intention infeasible.
This thesis examined the ability of ReLU based fully connected
feed forward neural networks to approximate the Bayesian model in
the upright condition and replace it in an adaptive stimulus selection
procedure to investigate whether the model’s parameters could be recovered
using an online evaluation of stimuli while at the same time
remaining computationally cheap. It could be shown that the networks
performed well on training data but significantly worse on unfamiliar
data. Nevertheless, parameters could be partially recovered using a
parameter setting familiar to the network. The average trial time of
2.14s that was measured during the procedure suggests the feasibility
of a dynamical stimulus evaluation approach within adaptive stimulus
selection.
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Faculteit der Sociale Wetenschappen