Using Bayesian Adaptive Stimulus Selection to Estimate Generalization Curves
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2020-01-31
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en
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Abstract
In my research, I implement the Bayesian Adaptive Stimulus Selection
algorithm proposed by Kontsevich and Tyler (Kontsevich & Tyler, 1999) and
adapt it in order to estimate the parameters of generalization curves of motor
learning. Using this algorithm could lead to faster and more e cient
computations and, as a result, more relevant ndings. In order to test the
algorithm's performance, I run it against an algorithm which selects stimuli
randomly. Eyeballing the resulting plots shows a considerable di erence in the
performance, although for 2 out of 3 of the curve parameters, the results are
not statistically signi cant. Future research could further build on this model
to improve its performance, or use it in a model comparison study between
symmetric and asymmetric models of a generalization curve.
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Faculteit der Sociale Wetenschappen