Does the Infant Motor System Predict Actions Based on Their Transitional Probability?
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2021-07-19
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en
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Predicting upcoming actions is a fundamental ability that shapes our social world. Therefore,
we need to be able to detect patterns in continuous streams of human actions and learn from
it; an ability called statistical learning. An important learning mechanism in statistical
learning is the computation of transitional probabilities of adjacent stimuli. However, it is not
yet clear how infants learn about their environment and to what degree they use statistical
learning. Therefore, it is now researched whether the infant motor system predicts actions
based on their transitional probability. Current study made use of electroencephalography
with 18-month-old infants. They watched the three subsequent days before they came to the
lab a video each day in which they saw unfamiliar action sequences with different transitional
probabilities. After these three days, they came to the lab and watched a similar video while
their motor activity was being measured using EEG. Findings revealed no significant effect of
(different) transitional probabilities on their motor activity. More research should be realised
to obtain more insight in statistical learning.
Keywords: statistical learning, transitional probability, infants,
electroencephalography, mu suppression, motor activity
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Faculteit der Sociale Wetenschappen