de Vries, M.H., Monaghan, P., Knecht, S. et al. (1 more author) (2008) Syntactic structure and artificial grammar learning:The learnability of embedded hierarchical structures. Cognition. pp. 763-774. ISSN 0010-0277
Abstract
Embedded hierarchical structures, such as ‘‘the rat the cat ate was brown’’, constitute a core generative property of a natural language theory. Several recent studies have reported learning of hierarchical embeddings in artificial grammar learning (AGL) tasks, and described the functional specificity of Broca’s area for processing such structures. In two experiments, we investigated whether alternative strategies can explain the learning success in these studies. We trained participants on hierarchical sequences, and found no evidence for the learning of hierarchical embeddings in test situations identical to those from other studies in the literature. Instead, participants appeared to solve the task by exploiting surface distinctions between legal and illegal sequences, and applying strategies such as counting or repetition detection. We suggest alternative interpretations for the observed activation of Broca’s area, in terms of the application of calculation rules or of a differential role of working memory. We claim that the learnability of hierarchical embeddings in AGL tasks remains to be demonstrated.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2008 Elsevier B.V. This is an author produced version of a paper published in Cognition. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Psychology (York) |
Depositing User: | Sherpa Assistant |
Date Deposited: | 05 Jun 2008 11:34 |
Last Modified: | 22 Oct 2024 23:49 |
Published Version: | https://doi.org/10.1016/j.cognition.2007.09.002 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.cognition.2007.09.002 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:3948 |