Production of real signs but not pseudosigns affected by age of acquisition in American Sign Language
Bibliographic record
Abstract
Research shows that insufficient language access in early childhood significantly affects language processing. While the majority of this work focuses on syntax, phonology also appears to be affected, though it is unclear exactly how. Here we investigated phonological production across age of acquisition of American Sign Language (ASL). Participants were deaf adult signers who first learned ASL at ages ranging from birth to 14 years and they performed both lexical decisions and repetitions of ASL signs and pseudosigns. Because phonological production has been understudied across age of acquisition, we were particularly interested in production accuracy for the sublexical phonological parameters of handshape, movement, and location. Lexical decision responses were slower and more accurate for impossible pseudosigns compared with possible pseudosigns, indicating participants were sensitive to ASL phonological structure regardless of age of acquisition. Despite this, age of acquisition affected repetition accuracy. Handshape errors were highest for those with earlier ages of acquisition, but movement errors were highest for those with later ages of acquisition, though this effect of age of acquisition was only seen for real ASL signs and not pseudosigns. The parameter error pattern for pseudosigns was not affected by age of acquisition. These results indicate that later age of acquisition does not inhibit the ability to produce ASL phonology but ultimately alters the processing of the phonological parameters when meaning and phonology are integrated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".