Visual-aerotactile perception and congenital hearing loss
Bibliographic record
Abstract
Previous research on multimodal speech perception with hearing-impaired individuals focused on audiovisual integration with mixed results. Cochlear-implant users integrate audiovisual cues better than perceivers with normal hearing when perceiving congruent [Rouger et al. 2007, PNAS, 104(17), 7295–7300] but not incongruent cross-modal cues [Rouger et al. 2008, Brain Research 1188, 87–99), leading to the suggestion that early auditory exposure is required for typical speech integration processes to develop (Schorr 2005, PNAS, 102(51), 18748–18750). If a deficit of one modality does indeed lead to a deficit in multimodal processing, then hard of hearing perceivers should show different patterns of integration in other modality pairings. The current study builds on research showing that gentle puffs of air on the skin can push individuals with normal hearing to perceive silent bilabial articulations as aspirated. We report on a visual-aerotactile perception task comparing individuals with congenital hearing loss to those with normal hearing. Results indicate that aerotactile information facilitated identification of /pa/ for all participants (p < 0.001) and we found no significant difference between the two groups (normal hearing and congenital hearing loss). This suggests that typical multi-modal speech perception does not require access to all modalities from birth. [Funded by NIH.]
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".