Impact of ASL exposure on spoken phonemic discrimination in CI users
Bibliographic record
Abstract
We examined neural activation patterns underlying phonemic discrimination in a spoken language in deaf CI users (N=18, age=18-24 years) who were exposed to a signed language at different ages and in hearing individuals (N=18, age=18-21 years). In deaf CI users, early-life language exposure, irrespective of modality, was associated with greater neural activation of language areas that are critically involved in phonological processing. For deaf CI users with later age of implantation, early age of exposure to a signed language was associated with increased activation in the left hemisphere’s classic language regions for native language (English) versus non-native language (Hindi) phonemic contrasts. For deaf CI users with earlier age of implantation, no significant change related to the age of exposure to a signed language was observed. These findings lend support to the hypothesis that early sign exposure does not negatively impact language processing in a spoken language in deaf CI users but may potentially offset the negative effects of language deprivation that children without any sign language exposure experience prior to implantation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 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".