On-line Measures of Prediction in a Self-Paced Statistical Learning Task

Elisabeth A. KaruzaUniversity of Rochester
Thomas A. FarmerUniversity of Iowa
Alex B. FineUniversity of Illinois at Urbana Champaign
Francis X. SmithUniversity of Iowa
T. Florian JaegerUniversity of Rochester

Abstract

As lifelong statistical learners, humans are remarkably sensitive to the unfolding of elements and events in their surroundings. In the present work, we examined the time-course of non-local dependency learning using a self-paced moving window display. We exposed participants to an artificial grammar of shape sequences and extracted processing times, or how long they viewed each shape, over the course of the experiment. On-line learning was quantified as the growing difference in viewing duration between predictable and predictive items. In other words, as participants learned, they processed predictable items increasingly faster. Our results indicate that participants who make implicit predictions as they learn, and have their expectations met, achieve higher learning outcomes on an off-line post-test. Potential links between these findings, obtained with novel stimuli in an experimental context, and the role of prediction in natural language comprehension are considered.

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