Adaptation of Gap Predictions in Filler-Gap Dependency Processing during Reading

Author:

Atkinson Emily1ORCID,Omaki Akira2

Affiliation:

1. Department of Language Studies, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada

2. Department of Linguistics, University of Washington, Seattle, WA 98195, USA

Abstract

Syntactic adaptation effects have been demonstrated for an expanding list of structure types, but the mechanism underlying this effect is still being explored. In the current work on filler-gap dependency processing, we examined whether exposing participants to a less common gap location—prepositional object (PO) gaps—altered their gap predictions, and whether these effects would transfer across tasks when this input was presented in a quasi-naturalistic way (i.e., by reading stories). In Experiment 1, we demonstrated that comprehenders dampened their direct object (DO) gap predictions following exposure to PO gaps. However, Experiments 2A and 2B suggest that these adaptation effects did not transfer when the quasi-naturalistic exposure phase was presented as a separate task (Experiment 2A) and when they also needed to generalize from a syntactic to a semantic measure of direct object gap predictions (i.e., filled gap vs. plausibility mismatch sentences; Experiment 2B). Overall, these experiments add filler-gap dependency processing, as well as the gap predictions associated with it, to the growing list of structures demonstrating adaptation effects, while also suggesting that this effect may be specific to a singular experimental task environment.

Funder

NSF

Publisher

MDPI AG

Subject

Linguistics and Language,Language and Linguistics

Reference77 articles.

1. Atkinson, Emily (2016). Active Dependency Completion in Adults and Children: Representations and Adaptation. [Doctoral dissertation, Johns Hopkins University].

2. Developing incrementality in filler-gap dependency processing;Atkinson;Cognition,2018

3. Mixed-effects modeling with crossed random effects for subjects and items;Baayen;Journal of Memory and Language,2008

4. Random effects structure for confirmatory hypothesis testing: Keep it maximal;Barr;Journal of Memory and Language,2013

5. Bates, Douglas M., Maechler, Martin, Bolker, Benjamin M., and Walker, Steven (2022, October 31). Lme4: Linear Mixed-Effects Models Using Eigen and S4. R Package Version 1.1-9. Available online: https://CRAN.R-project.org/package=lme4/.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3