Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

First published: 07 October 2017From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible “statistical” properties that are the object of learning. Much less attention has been given to defining what “learning” is in the context of “statistical learning.” One major difficulty is that SL research has been monitoring participants’ performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative- forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.This paper was supported by the ERC Advanced grant awarded to Ram Frost (project 692502), the Israel Science Foundation (Grant 217/14 awarded to Ram Frost), by the National Institute of Child Health and Human Development (RO1 HD 067364 awarded to Ken Pugh and Ram Frost, and PO1 HD 01994 awarded to Haskins Laboratories)