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| Funder | Economic and Social Research Council |
|---|---|
| Recipient Organization | Queen Mary University of London |
| Country | United Kingdom |
| Start Date | Sep 30, 2024 |
| End Date | Mar 30, 2028 |
| Duration | 1,277 days |
| Number of Grantees | 2 |
| Roles | Student; Supervisor |
| Data Source | UKRI Gateway to Research |
| Grant ID | 2930005 |
Sign languages are linguistic systems that are as rich and complex as spoken languages, and with as much potential to illuminate our understanding of the nature of human language. Yet, only recently have they started to be studied systematically.
This project will contribute to filling in this gap by carrying out the first comprehensive description and analysis of the syntax and semantics of British Sign Language (BSL) connectives. Connectives link two or more statements together.
E.g., if both 'Susie is a doctor' and 'Anna is a linguist' are true, in English you can say 'Susie is a doctor and Anna is a linguist'. If only one is, you say 'Susie is a doctor or Anna is a linguist'.
Importantly, while natural languages typically have words such as 'and' or 'or', no natural language has a simple, single connective to indicate that two statements are false. Instead, speakers use more complex expressions (e.g., 'neither...nor').
Cross-linguistically, only a small number of the many possible ways of combining two statements can be expressed with a simple, one-word connective. Why, and what does that tell us about human language?
For spoken language connectives, such questions have been addressed using optimal trade-off models (Uegaki 2022, Enguehard & Spector 2021, Carcassi & Sbardolini 2023).
A language will maximise the communicative efficiency of its lexicon (the repository of all of its words) by balancing the competing pressures of informativity, on the one hand, and simplicity, on the other.
In this view, a lexicon is an optimal trade-off between the benefits of an accurate vocabulary, and the cognitive overload of an entire lexicon of highly specific words.
Sign languages, however, have multiple points of articulation: as well as the manual ones (hands), there are non-manual markers (facial features, head, torso), and the space in front of the signer. Information integral to meaning is regularly conveyed through non-manual markers in addition to lexical signs.
E.g., the difference between 'and' and 'or' in Catalan Sign Language rests solely on the use of non-lexical articulators (body lean, head tilt, signing space ('and') vs. strong head thrust ('or')); otherwise, all lexical signs stay the same (Navarrete-González 2021). Current trade-off models have not, to date, managed to incorporate sources of information external to the lexicon.
How do non-lexical articulators figure in computations of complexity? Are optimal trade off models adequate for the analysis of BSL? Aims: 1.
To provide a description of the syntax and semantics of BSL connectives, with a focus on non-manual markers and signing space, 2.
To critically assess the ways in which sign languages like BSL challenge the current understanding of communicative efficiency, 3. To develop an accurate optimal trade-off model of the BSL connective lexicon, and 4. To explore the implications of (2) and (3) for our understanding of the human linguistic capacity.
Additionally, the project will: 5.
Collect qualitative data that supports the social goals of the partner organisation, RNID, a national charity that works to improve the lives of Deaf people with a strong focus on social research.
This project will contribute to their mission by recording personal narratives from participants about their experience as a Deaf person in the UK today.
Methodology: Research preparation.Via RNID, the student will have access to expertise on the best practices of conducting research with the Deaf community, and the opportunity to observe data collection by its social research team.
Participants and project assistants for the project will be recruited with the support of these established connections.
The student will attend a 6-month placement at SignLab, University of Amsterdam, at the end of the first year to be trained in the innovative sign language research techniques being developed there, including new hardware (depth sensing cameras, motion capt
Queen Mary University of London
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