Syntactic structure and artificial grammar learning: The learnability of embedded hierarchical structures

Psychology

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https://doi.org/10.1016/j.cognition.2007.09.002
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Keywords

artificial grammar learning, syntax, context free grammar, finite-state grammar, centre embeddings, hierarchical structure learning, WORKING-MEMORY, LANGUAGE, COMPREHENSION, FACULTY

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Syntactic structure and artificial grammar learning: The learnability of embedded hierarchical structures. / de Vries, Meinou H.; Monaghan, Padraic; Knecht, Stefan et al.
In: Cognition, Vol. 107, No. 2, 05.2008, p. 763-774.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{97fa7444eea14e00a2fe2919b77d1d5a,

title = "Syntactic structure and artificial grammar learning: The learnability of embedded hierarchical structures",

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. (C) 2007 Elsevier B.V. All rights reserved.",

keywords = "artificial grammar learning, syntax, context free grammar, finite-state grammar, centre embeddings, hierarchical structure learning, WORKING-MEMORY, LANGUAGE, COMPREHENSION, FACULTY",

author = "{de Vries}, {Meinou H.} and Padraic Monaghan and Stefan Knecht and Pienie Zwitserlood",

year = "2008",

month = may,

doi = "10.1016/j.cognition.2007.09.002",

language = "English",

volume = "107",

pages = "763--774",

journal = "Cognition",

issn = "0010-0277",

publisher = "Elsevier",

number = "2",

}

RIS

TY - JOUR

T1 - Syntactic structure and artificial grammar learning: The learnability of embedded hierarchical structures

AU - de Vries, Meinou H.

AU - Monaghan, Padraic

AU - Knecht, Stefan

AU - Zwitserlood, Pienie

PY - 2008/5

Y1 - 2008/5

N2 - 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. (C) 2007 Elsevier B.V. All rights reserved.

AB - 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. (C) 2007 Elsevier B.V. All rights reserved.

KW - artificial grammar learning

KW - syntax

KW - context free grammar

KW - finite-state grammar

KW - centre embeddings

KW - hierarchical structure learning

KW - WORKING-MEMORY

KW - LANGUAGE

KW - COMPREHENSION

KW - FACULTY

U2 - 10.1016/j.cognition.2007.09.002

DO - 10.1016/j.cognition.2007.09.002

M3 - Journal article

VL - 107

SP - 763

EP - 774

JO - Cognition

JF - Cognition

SN - 0010-0277

IS - 2

ER -

Research

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