Implicit acquisition of grammars with crossed and nested non-adjacent dependencies: Investigating the push-down stack model
Publication year
2012Number of pages
24 p.
Source
Cognitive Science, 36, 6, (2012), pp. 1078-1101ISSN
Publication type
Article / Letter to editor
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Organization
SW OZ DCC PL
Journal title
Cognitive Science
Volume
vol. 36
Issue
iss. 6
Languages used
English (eng)
Page start
p. 1078
Page end
p. 1101
Subject
DI-BCB_DCC_Theme 1: Language and Communication; PsycholinguisticsAbstract
A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finite-state and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central to the processing of non-adjacent dependencies as information has to be maintained across intervening material. One proposal is that an external memory device in the form of a limited push-down stack is used to process non-adjacent dependencies. We tested this hypothesis in an artificial grammar learning paradigm where subjects acquired non-adjacent dependencies implicitly. Generally, we found no qualitative differences between the acquisition of non-adjacent dependencies and adjacent dependencies. This suggests that although the acquisition of non-adjacent dependencies requires more exposure to the acquisition material, it utilizes the same mechanisms used for acquiring adjacent dependencies. We challenge the push-down stack model further by testing its processing predictions for nested and crossed multiple non-adjacent dependencies. The push-down stack model is partly supported by the results, and we suggest that stack-like properties are some among many natural properties characterizing the underlying neurophysiological mechanisms that implement the online memory resources used in language and structured sequence processing.
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- Academic publications [238441]
- Faculty of Social Sciences [29483]
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