Impaired artificial grammar learning in agrammatism

It is often assumed that language is supported by domain-specific neural mechanisms, in part based on neuropsychological data from aphasia. If, however, language relies on domain-general mechanisms, it would be expected that deficits in non-linguistic cognitive processing should co-occur with aphasi...

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Bibliographic Details
Published in:Cognition 2010-09, Vol.116 (3), p.382-393
Main Authors: Christiansen, Morten H., Louise Kelly, M., Shillcock, Richard C., Greenfield, Katie
Format: Article
Language:English
Subjects:
Adult and adolescent clinical studies
Aged
Aged, 80 and over
Agrammatism
Aphasia
Artificial grammar learning
Biological and medical sciences
Case-Control Studies
Cognition
Cognitive ability
Cognitive Processes
Cognitive psychology
Control Groups
Disorders of higher nervous function. Focal brain diseases. Central vestibular syndrome and deafness. Brain stem syndromes
Domain-specificity
Economic Status
Experiments
Female
Grammar
Humans
Language
Language acquisition
Language and communication disorders
Language disorder
Language Tests
Learning
Learning - physiology
Linguistics
Male
Medical sciences
Middle Aged
Modularity
Nervous system (semeiology, syndromes)
Neurology
Organic mental disorders. Neuropsychology
Patients
Psychology. Psychoanalysis. Psychiatry
Psychopathology. Psychiatry
Sequential Learning
Test Items
Vocabulary
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Description
Summary:It is often assumed that language is supported by domain-specific neural mechanisms, in part based on neuropsychological data from aphasia. If, however, language relies on domain-general mechanisms, it would be expected that deficits in non-linguistic cognitive processing should co-occur with aphasia. In this paper, we report a study of sequential learning by agrammatic aphasic patients and control participants matched for age, socio-economic status and non-verbal intelligence. Participants were first exposed to strings derived from an artificial grammar after which they were asked to classify a set of new strings, some of which were generated by the same grammar whereas others were not. Although both groups of participants performed well in the training phase of the experiment, only the control participants were able to classify novel test items better than chance. The results show that breakdown of language in agrammatic aphasia is associated with an impairment in artificial grammar learning, indicating damage to domain-general neural mechanisms subserving both language and sequential learning.
ISSN:0010-0277
1873-7838
DOI:10.1016/j.cognition.2010.05.015