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Measuring statistical learning by eye-tracking
Tamás Zolnai, Dominika Réka Dávid, Orsolya Pesthy, Marton Nemeth, Mariann Kiss, Márton Nagy, Dezső Németh

Measuring statistical learning by eye-tracking

Science & Mathematics Published 2022 Verification Pending

Overview & Synopsis

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.

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Document Information

Document Title Measuring statistical learning by eye-tracking
Author / Organization Tamás Zolnai, Dominika Réka Dávid, Orsolya Pesthy, Marton Nemeth, Mariann Kiss, Márton Nagy, Dezső Németh
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2022
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-196161
Original Source Link Visit Source Page on hal.science »