Implicit Statistical Learning: A Tale of Two Literatures

• Published in: Topics in cognitive science Vol. 11; no. 3; pp. 468 - 481
• Main Author: Christiansen, Morten H.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2019
• Subjects: Bibliographies as Topic; Chunking; Cognitive Science - history; History, 20th Century; History, 21st Century; Humans; Implicit learning; Learning; Memory; Nonword repetition; Probability Learning; Serial recall; Statistical learning; Text editing; Chunking; Implicit learning; Nonword repetition; Statistical learning; Memory; Serial recall
• ISSN: 1756-8757; 1756-8765; 1756-8765
• DOI: 10.1111/tops.12332

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. In this review article, Christiansen provides a historical perspective on the two research traditions, implicit learning and statistical learning, thus nicely setting the scene for this special issue of Topics in Cognitive Science. In this “tale of two literatures”, he first traces the history of both literatures before sketching a framework that provides a basis for understanding implicit learning and statistical learning as a unified phenomenon.