Verbal Working Memory, Long-Term Knowledge, and Statistical Learning

Abstract

Evidence supporting the idea that serial-order verbal working memory is underpinned by long-term knowledge has accumulated over more than half a century. Recent studies using natural-language statistics, artificial statistical-learning techniques, and the Hebb repetition paradigm have revealed multiple types of long-term knowledge underlying serial-order verbal working memory performance. These include (a) element-to-element association knowledge, which slowly accumulates through extensive exposure to an exemplar; (b) position–element knowledge, which is acquired through several encounters with an exemplar; and (c) whole-sequence knowledge, which is captured by the Hebb repetition paradigm and acquired rapidly with a few repetitions. Arguably, the first two are a basis for fluent and efficient language usage, and the third is a basis for vocabulary learning. Thus, statistical-learning mechanisms (and possibly episodic-learning mechanisms) may form the foundation of language acquisition and language processing, which characterize linguistic long-term knowledge for verbal working memory.