Recovering Generalization Curves using Adaptive Stimulus Selection
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2020-01-31
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en
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Abstract
Adaptive Stimulus Selection has been around for a while now, and is usable for a
range of stimulus selection problems. It is expected to use the stimulus that creates
the largest information gain. As motor learning experiments require many trials in
order to get reliable results, this is a field where using adaptive stimulus selection
might be especially advantageous. In particular, with a properly working stimulus
selection, it is possible to more quickly generate the generalization curve in a motor
learning experiment. To test this the question is thus whether it is possible to correctly
recover a generalization curve of a motor learning model, whilst performing
faster than a random stimulus selection.
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Faculteit der Sociale Wetenschappen