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Sign language and the brain: a review.
Ruth Campbell1, Mairéad MacSweeney, Dafydd Waters
1ESRC Deafness, Cognition and Language Research Centre, Department of Human Communication Science, University College London, 49 Gordon Square, London WC1H 0PD. r.campbell@ucl.ac.uk
This review explores how the brain processes signed languages, comparing it to spoken language. It investigates whether sign language is primarily processed in the left hemisphere, like spoken language, or involves the right hemisphere more.
Area of Science:
- Neuroscience
- Linguistics
- Cognitive Science
Background:
- Spoken language processing is predominantly lateralized to the left hemisphere (LH) in native speakers.
- Understanding the neural basis of signed language is crucial for a comprehensive theory of human language.
- Investigating sign language processing offers insights into the brain's capacity for language regardless of modality.
Purpose of the Study:
- To review current research on brain processing of signed languages.
- To compare neural mechanisms of sign and speech processing.
- To examine the lateralization of signed language in the brain.
Main Methods:
- Review of neuroimaging studies (e.g., fMRI, EEG) on sign language processing.
- Comparison of findings across studies investigating signers and speakers.
- Analysis of methodologies used to study brain activity during language tasks.
Main Results:
- Evidence suggests that signed language processing is also largely lateralized to the left hemisphere in native signers.
- Some studies indicate potential right hemisphere involvement in sign language processing, possibly exceeding that in speech processing.
- Methodological challenges exist in definitively answering the lateralization question for signed languages.
Conclusions:
- Signed language processing shares similarities with spoken language processing, particularly regarding left hemisphere dominance.
- The extent of right hemisphere involvement in sign language processing warrants further investigation.
- Future research should focus on refining methodologies to clarify the precise neural networks underlying signed language comprehension and production.
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