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

As lifelong statistical learners, humans are remarkably sensitive to the unfolding of elements and events in their surroundings. In the present work, we examine the timecourse of non-local dependency learning using a self-paced moving window display. We expose participants to an artificial grammar of shape sequences and extract processing times, or how long they viewed each shape, over the course of the experiment. On-line learning is quantified as the growing difference in viewing duration between predictable and predictive items. In other words, as participants learn, they process 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.