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Cortical Encoding of Manual Articulatory and Linguistic Features in American Sign Language.

ABSTRACT: The fluent production of a signed language requires exquisite coordination of sensory, motor, and cognitive processes. Similar to speech production, language produced with the hands by fluent signers appears effortless but reflects the precise coordination of both large-scale and local cortical networks. The organization and representational structure of sensorimotor features underlying sign language phonology in these networks remains unknown. Here, we present a unique case study of high-density electrocorticography (ECoG) recordings from the cortical surface of profoundly deaf signer during awake craniotomy. While neural activity was recorded from sensorimotor cortex, the participant produced a large variety of movements in linguistic and transitional movement contexts. We found that at both single electrode and neural population levels, high-gamma activity reflected tuning for particular hand, arm, and face movements, which were organized along dimensions that are relevant for phonology in sign language. Decoding of manual articulatory features revealed a clear functional organization and population dynamics for these highly practiced movements. Furthermore, neural activity clearly differentiated linguistic and transitional movements, demonstrating encoding of language-relevant articulatory features. These results provide a novel and unique view of the fine-scale dynamics of complex and meaningful sensorimotor actions.

SUBMITTER: Leonard MK

PROVIDER: S-EPMC7674262 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The Health Information National Trends Survey (HINTS) collects nationally representative data about the American's public use of health-related information. This survey is available in English and Spanish, but not in American Sign Language (ASL). Thus, the exclusion of ASL users from these national health information survey studies has led to a significant gap in knowledge of Internet usage for health information access in this underserved and understudied population.The objectives of this study are (1) to culturally adapt and linguistically translate the HINTS items to ASL (HINTS-ASL); and (2) to gather information about deaf people's health information seeking behaviors across technology-mediated platforms.We modified the standard procedures developed at the US National Center for Health Statistics Cognitive Survey Laboratory to culturally adapt and translate HINTS items to ASL. Cognitive interviews were conducted to assess clarity and delivery of these HINTS-ASL items. Final ASL video items were uploaded to a protected online survey website. The HINTS-ASL online survey has been administered to over 1350 deaf adults (ages 18 to 90 and up) who use ASL. Data collection is ongoing and includes deaf adult signers across the United States.Some items from HINTS item bank required cultural adaptation for use with deaf people who use accessible services or technology. A separate item bank for deaf-related experiences was created, reflecting deaf-specific technology such as sharing health-related ASL videos through social network sites and using video remote interpreting services in health settings. After data collection is complete, we will conduct a series of analyses on deaf people's health information seeking behaviors across technology-mediated platforms.HINTS-ASL is an accessible health information national trends survey, which includes a culturally appropriate set of items that are relevant to the experiences of deaf people who use ASL. The final HINTS-ASL product will be available for public use upon completion of this study.

Dataset Information

Cortical Encoding of Manual Articulatory and Linguistic Features in American Sign Language.

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