Measuring statistical learning by eye-tracking

Statistical learning—the skill to pick up probability-based regularities of the environment—plays a crucial role in adapting to the environment and learning perceptual, motor, and language skills in healthy and clinical populations. Here, we developed a new method to measure statistical learning wit...

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Bibliographic Details
Published in:	Experimental Results Vol. 3
Main Authors:	Zolnai, Tamás, Dávid, Dominika Réka, Pesthy, Orsolya, Nemeth, Marton, Kiss, Mariann, Nagy, Márton, Nemeth, Dezso
Format:	Journal Article
Language:	English
Published:	Cambridge University Press 15-08-2022
Subjects:	Alternating Serial Reaction Time task Cognitive science eye-tracking Humanities and Social Sciences Neuroscience procedural learning Psychology statistical learning procedural learning statistical learning eye-tracking statistical learning eye-tracking Alternating Serial Reaction Time task procedural learning Alternating Serial Reaction Time task
Online Access:	Get full text
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Summary:	Statistical learning—the skill to pick up probability-based regularities of the environment—plays a crucial role in adapting to the environment and learning perceptual, motor, and language skills in healthy and clinical populations. Here, we developed a new method to measure statistical learning without any manual responses. We used the Alternating Serial Reaction Time (ASRT) task, adapted to eye-tracker, which, besides measuring reaction times (RTs), enabled us to track learning-dependent anticipatory eye movements. We found robust, interference-resistant learning on RT; moreover, learning-dependent anticipatory eye movements were even more sensitive measures of statistical learning on this task. Our method provides a way to apply the widely used ASRT task to operationalize statistical learning in clinical populations where the use of manual tasks is hindered, such as in Parkinson’s disease. Furthermore, it also enables future basic research to use a more sensitive version of this task to measure predictive processing.
ISSN:	2516-712X 2516-712X
DOI:	10.1017/exp.2022.8