Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Beyond associations: Sensitivity to structure in pre-schoolers' linguistic predictions

Gambi, Chiara, Pickering, Martin J. and Rabagliati, Hugh 2016. Beyond associations: Sensitivity to structure in pre-schoolers' linguistic predictions. Cognition 157 , pp. 340-351. 10.1016/j.cognition.2016.10.003

[img]
Preview
PDF - Accepted Post-Print Version
Download (753kB) | Preview

Abstract

One influential view of language acquisition is that children master structural generalizations by making and learning from structure-informed predictions. Previous work has shown that from 3 years of age children can use semantic associations to generate predictions. However, it is unknown whether they can generate predictions by combining these associations with knowledge of linguistic structure. We recorded the eye movements of pre-schoolers while they listened to sentences such as Pingu will ride the horse. Upon hearing ride, children predictively looked at a horse (a strongly associated and plausible patient of ride), and mostly ignored a cowboy (equally strongly associated, but an implausible patient). In a separate experiment, children did not rapidly look at the horse when they heard You can show Pingu … “riding”, showing that they do not quickly activate strongly associated patients when there are no structural constraints. Our findings demonstrate that young children’s predictions are sensitive to structure, providing support for predictive-learning models of language acquisition.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Publisher: Elsevier
ISSN: 0010-0277
Date of First Compliant Deposit: 12 December 2017
Date of Acceptance: 5 October 2016
Last Modified: 14 Nov 2019 12:07
URI: http://orca.cf.ac.uk/id/eprint/106645

Citation Data

Cited 8 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics