Linguistic generalization on the basis of function and constraints on the basis of statistical preemption

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • Princeton University

Abstract

A key question in language learning is what encourages and what constraints
generalization beyond what is witnessed in the input. Experiment 1 exposes
participants to two novel word order constructions that differ in terms of their
semantics: One construction exclusively describes actions that have a strong
effect; the other construction describes actions with a weaker but otherwise
similar affect. One group of participants witnessed novel verbs only
appearing in one construction or the other, while another group witnessed a
minority of verbs alternating between constructions. Subsequent production
and judgment results demonstrate that participants in both conditions
extended and accepted verbs in whichever construction best described the
intended message. Unlike related previous work, this finding is not naturally
attributable to prior knowledge of the likely division of labor between verbs
and constructions. A second experiment included one verb (out of six) that
was witnessed in a single construction to describe both strong and weak
effects, essentially preempting the use of the other construction. In this case,
participants were much more lexically conservative with this verb and other
verbs, while they nonetheless displayed an appreciation of the distinct
semantics of the constructions with new novel verbs. Results indicate that the
need to better express an intended message encourages generalization, while
statistical preemption constrains generalization by providing evidence that
verbs are restricted in their distribution.

Details

Original languageEnglish
Pages (from-to)276-293
JournalCognition
Volume168
Early online date27 Jul 2017
Publication statusPublished - Nov 2017

Keywords

  • Language acquisition, Artificial language learning, Novel construction learning, Statistical learning, Argument structure constructions, Generalization