Sign Language Handshapes, Similarly to Speech Sounds, Exploit Biomechanical Endpoints
Bibliographic record
Abstract
Keywords: sign language, articulation, biomechanicsSpeech motor control approaches have argued that the dimensionality problem can be handled by exploiting endpoints, a type of biomechanical quantal region (Moisik & Gick, 2017, Gick et al. 2020), where a stable output can be obtained regardless of the starting position or variable muscle activation of the articulators (Moisik & Gick, 2013). Endpoints involve a contact between two articulators or an active articulator and a passive articulator. One advantage endpoints offer is in preventing overshoot. In spoken languages, endpoints are maximally exploited in plosive sounds, the most frequent type of consonants occurring in all known spoken languages (Maddieson, 1984: 25). In signed languages, endpoints are exploited in signs where two hands make contact with each other, or where a hand(s) makes contact with the signer’s body (Tkachman, 2022). For example, in signs produced with body contact, variable arm muscle activation does not lead to variable place of articulation (Goyal, Venkata, Tkachman & Gick, 2019). In this study, we extend research on sign-language endpoints by investigating handshape-internal endpoints, which is a state of a finger/joint where it is maximally extended or flexed or where the fingers and/or thumb make a contact with each other and/or the palm. We annotated handshape inventories from five genetically distinct sign languages to determine the extent to which phonemic handshapes in the inventory exploit handshape-internal endpoints. These results support the view that the biomechanical quantal regions previously observed for speech are general to communicative control systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".