Implicit Statistical Learning: A Tale of Two Literatures

Abstract

Implicit learning and statistical learning are two contemporary approaches to the long‐standing question in psychology and cognitive science of how organisms pick up on patterned regularities in their environment. Although both approaches focus on the learner's ability to use distributional properties to discover patterns in the input, the relevant research has largely been published in separate literatures and with surprisingly little cross‐pollination between them. This has resulted in apparently opposing perspectives on the computations involved in learning, pitting chunk‐based learning against probabilistic learning. In this paper, I trace the nearly century‐long historical pedigree of the two approaches to learning and argue for their integration under the heading of “implicit statistical learning.” Building on basic insights from the memory literature, I sketch a framework for statistically based chunking that aims to provide a unified basis for understanding implicit statistical learning.