Distributional Language Learning: Mechanisms and Models of Category Formation. Issue 2 (September 2014)

• Record Type: Journal Article
• Authors: Aslin, Richard N.; Newport, Elissa L.; Molnar, Monika; Sebastian‐Galles, Nuria
• Is Part Of: Language learning. Volume 64:Issue 2(2014)
• Page Start: 86
• Page End: 105
• Publication Date: 2014-09
• Subjects: Language and languages -- Periodicals; Language and languages -- Study and teaching -- Periodicals; Linguistics; 407
• DOI: 10.1111/lang.12074
• Languages: English
• ISSNs: 0023-8333

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes—acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars—actually represent a single mechanism. Evidence in support of this single‐mechanism hypothesis comes from a series of artificial grammar‐learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic‐category learning are summarized.</p> </abstract>