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Lookup NU author(s): Dr Faye Smith, Dr Nick Riches
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2024 Jenkins, de Graaf, Smith, Riches and Wilson.Introduction: Implicit statistical learning is, by definition, learning that occurs without conscious awareness. However, measures that putatively assess implicit statistical learning often require explicit reflection, for example, deciding if a sequence is ‘grammatical’ or ‘ungrammatical’. By contrast, ‘processing-based’ tasks can measure learning without requiring conscious reflection, by measuring processes that are facilitated by implicit statistical learning. For example, when multiple stimuli consistently co-occur, it is efficient to ‘chunk’ them into a single cognitive unit, thus reducing working memory demands. Previous research has shown that when sequences of phonemes can be chunked into ‘words’, participants are better able to recall these sequences than random ones. Here, in two experiments, we investigated whether serial visual recall could be used to effectively measure the learning of a more complex artificial grammar that is designed to emulate the between-word relationships found in language. Methods: We adapted the design of a previous Artificial Grammar Learning (AGL) study to use a visual serial recall task, as well as more traditional reflection-based grammaticality judgement and sequence completion tasks. After exposure to “grammatical” sequences of visual symbols generated by the artificial grammar, the participants were presented with novel testing sequences. After a brief pause, participants were asked to recall the sequence by clicking on the visual symbols on the screen in order. Results: In both experiments, we found no evidence of artificial grammar learning in the Visual Serial Recall task. However, we did replicate previously reported learning effects in the reflection-based measures. Discussion: In light of the success of serial recall tasks in previous experiments, we discuss several methodological factors that influence the extent to which implicit statistical learning can be measured using these tasks.
Author(s): Jenkins HE, de Graaf Y, Smith F, Riches N, Wilson B
Publication type: Article
Publication status: Published
Journal: Frontiers in Psychology
Year: 2024
Volume: 15
Online publication date: 18/12/2024
Acceptance date: 02/12/2024
Date deposited: 13/01/2025
ISSN (electronic): 1664-1078
Publisher: Frontiers Media SA
URL: https://doi.org/10.3389/fpsyg.2024.1497201
DOI: 10.3389/fpsyg.2024.1497201
Data Access Statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://osf.io/dpeu8/.
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