Evidence map›Paper›PMID 35884663›Full record

ArticleBrain sciences2022

Agents Strongly Preferred: ERP Evidence from Natives and Non-Natives Processing Intransitive Sentences in Spanish.

Adam Zawiszewski, Gillen Martinez de la Hidalga, Itziar Laka

Abstract read
In one paragraph

Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Adam ZawiszewskiDepartment of Linguistics and Basque Studies, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain.ORCID 0000-0002-5613-8880
Gillen Martinez de la HidalgaEPA-HHI Arrasateko Helduen Eskola, 20500 Arrasate, Spain.
Itziar LakaDepartment of Linguistics and Basque Studies, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain.ORCID 0000-0003-2402-2661

Funding

Basque Government IT1169-19Ministerio de Ciencia e Innovación (Spain) FPI-2017-BES-2016-076456 (GM)Ministerio de Ciencia, Innovación y Universidades (Spain) PID2019-104016GB-I00
6 · The paper itself

Abstract

Are non-native speakers able to process their second language in a native-like way? The present study used the Event-Related Potentials' (ERPs) method to address this issue by focusing (1) on agent vs. agentless intransitive sentences and (2) on person vs. number agreement morphology. For that purpose, native and high proficiency and early non-native speakers of Spanish were tested while processing intransitive sentences containing grammatical and ungrammatical subject-verb agreement. Results reveal greater accuracy in the agent (unergative) condition as compared with the agentless (unaccusative) condition and different ERP patterns for both types of verbs in all participants, suggesting a larger processing cost for the agentless sentences than for the agentive ones. These effects were more pronounced in the native group as compared with the non-native one in the early time window (300-500 ms). Differences between person and number agreement processing were also found at both behavioral and electrophysiological levels, indicating that those morphological features are distinctively processed. Importantly, this pattern of results held for both native and non-native speakers, thus suggesting that native-like competence is attainable given early Age of Acquisition (AoA), frequent use and high proficiency.

Indexed as

agentsERPsintransitive sentence processingnon-native language processingpatients

Identifiers

PMID35884663
PMCPMC9312802

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.