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Lookup NU author(s): Holly Jenkins, Dr Faye Smith, Dr Nick Riches
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 Jenkins et al. Implicit statistical learning, whereby predictable relationships between stimuli are detected without conscious awareness, is important for language acquisition. However, while this process is putatively implicit, it is often assessed using measures that require explicit reflection and conscious decision making. Here, we conducted three experiments combining an artificial grammar learning paradigm with a serial reaction time (SRT-AGL) task, to measure statistical learning of adjacent and nonadjacent dependencies implicitly, without conscious decision making. Participants viewed an array of six visual stimuli and were presented with a sequence of three auditory (nonsense words, Expt. 1; names of familiar objects, Expt. 2) or visual (abstract shapes, Expt. 3) cues and were asked to click on the corresponding visual stimulus as quickly as possible. In each experiment, the final stimulus in the sequence was predictable based on items earlier in the sequence. Faster responses to this predictable final stimulus compared to unpredictable stimuli would provide evidence of implicit statistical learning, without requiring explicit decision making or conscious reflection. Despite previous positive results (Christiansen et al. 2009 and Misyak et al. 2010) we saw little evidence of implicit statistical learning in any of the experiments, suggesting that in this case, these SRT-AGL tasks were not an effective measure implicit statistical learning.
Author(s): Jenkins HE, Leung P, Smith F, Riches N, Wilson B
Publication type: Article
Publication status: Published
Journal: PLoS ONE
Year: 2024
Volume: 19
Issue: 9
Online publication date: 20/09/2024
Acceptance date: 27/07/2024
Date deposited: 30/09/2024
ISSN (electronic): 1932-6203
Publisher: Public Library of Science
URL: https://doi.org/10.1371/journal.pone.0308653
DOI: 10.1371/journal.pone.0308653
Data Access Statement: The data and analysis are available on OSF (DOI https://doi.org/10.17605/OSF.IO/DETP3).
PubMed id: 39302892
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