How do we study and model the structure and processing of sign languages?
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Studying and Modeling the Structure and Processing of Sign Languages
Introduction to Sign Language Processing
Sign languages are fully developed human languages that use visual-manual modality for communication. They possess all the fundamental linguistic properties of spoken languages, including phonology, syntax, and semantics . Understanding how sign languages are processed and structured involves exploring their neurobiological bases, linguistic complexity, and predictive processing mechanisms.
Neurobiological Bases of Sign Language
Neural Systems and Lateralization
Research indicates that the neural systems supporting sign languages are similar to those supporting spoken languages, predominantly involving a left-lateralized perisylvian network . This suggests that the brain regions responsible for language processing are largely modality-independent, although some neural regions are specific to sign language. Studies have shown that both deaf and hearing individuals process sign languages using similar neural mechanisms, highlighting the amodal nature of language processing .
Sensorimotor and Cognitive Neural Components
The study of sign languages allows researchers to dissociate sensorimotor and cognitive neural components of the language signal. For instance, monitoring different phonological parameters such as handshape and location engages the same cortical language network but distinct perceptual ones. This indicates that while the linguistic processing of sign languages is independent of their sensorimotor characteristics, the phonological structure significantly influences neural processing.
Linguistic Complexity of Sign Languages
High Dimensionality and Visual Domain
Sign languages use a dynamic visual signal for communication, which complicates the application of traditional linguistic metrics developed for linear, symbolic representations like written forms of spoken languages. The higher dimensionality (spatial and temporal) of sign language signals requires specialized approaches to operationalize their complexity. Researchers have identified methods that capture linguistically relevant features of the sign language signal while maintaining high fidelity modeling in the visual domain.
Iconicity and Language Acquisition
Iconicity, the resemblance between a sign and its meaning, is pervasive in sign language lexicons and plays a significant role in language acquisition and processing. This feature distinguishes sign languages from spoken languages and provides unique insights into how language modality affects language structure and use.
Predictive Processing in Sign Languages
Evidence for Predictive Processing
Predictive processing (PP) in sign languages involves anticipating upcoming linguistic information based on context. Studies have shown that Deaf native signers predict linguistic information during sign language processing, suggesting that PP is an amodal property of language processing. However, the underlying mechanisms in the visual modality remain unclear, and there is limited evidence for frequency-based, phonetic, and syntactic prediction in sign languages.
Forward Models and Lexical Prediction
Forward models, which draw upon the language production system to set up expectations during comprehension, provide a promising approach to understanding prediction in sign languages. Event-related potential (ERP) studies on German Sign Language (DGS) have shown that unexpected signs elicit specific neural responses, indicating that the comprehension system anticipates modality-specific information about the realization of predicted semantic items. This supports the application of forward models in language comprehension.
Challenges and Future Directions
Inclusion in Natural Language Processing
Despite their linguistic richness, sign languages are often underrepresented in Natural Language Processing (NLP) research. There is a need for the development of linguistically informed models, efficient tokenization methods, and the collection of real-world signed language data. Engaging local signed language communities as active participants in research can also enhance the social and scientific impact of these studies.
Understanding Language Modality
Studying sign languages provides unique insights into human language that cannot be obtained by studying spoken languages alone. By considering both signed and spoken languages, researchers can gain a better understanding of how language is represented in the human brain and the relationship between different language modalities.
Conclusion
The study and modeling of sign languages involve exploring their neurobiological bases, linguistic complexity, and predictive processing mechanisms. While significant progress has been made, challenges remain in fully integrating sign languages into NLP research and understanding the unique aspects of their modality. Continued research in this field promises to deepen our understanding of human language and its neural underpinnings.
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