Expectation Violation Enhances the Development of New Abstract Syntactic Representations: Evidence from an Artificial Language Learning Study

Research output: Contribution to journalArticlepeer-review

Full text download(s)

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

JournalLanguage Development Research
DateAccepted/In press - 20 Sep 2021
DatePublished (current) - 13 Oct 2021
Number of pages51
Original languageEnglish

Abstract

Prediction error is known to enhance priming effects for familiar syntactic structures; it also strengthens the formation of new declarative memories. Here, we investigate whether violating expectations may aid the acquisition of new abstract syntactic structures, too, by enhancing memory for individual instances which can then form the basis for abstraction. In a cross-situational artificial language learning paradigm, participants were exposed to novel syntactic structures in ways that either violated their expectations (Surprisal group) or that conformed to them (Control group). First, we established a potential expectation to hear feedback that simply repeated the same structure as that just experienced. We then manipulated feedback so that the Surprisal group unexpectedly heard passive structures in feedback following active sentences, while the Control group only heard passive structures following passive sentences. Delayed post-tests examined participants’ structural knowledge both by means of structure test trials (focusing on the active / passive distinction, with both familiar and novel verbs), and by a grammaticality judgment task. The Surprisal group was significantly more accurate than the Control group on the structure test trials with novel verbs and on the grammaticality judgment task, suggesting participants had developed stronger abstract structural knowledge and were better at generalising it to novel instances. Tentative evidence suggested the Surprisal group was not significantly more likely to become aware of the functional distinction between the two structures.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations