Audiovisual Segregation in Cochlear Implant Users
Bibliographic record
Abstract
It has traditionally been assumed that cochlear implant users de facto perform atypically in audiovisual tasks. However, a recent study that combined an auditory task with visual distractors suggests that only those cochlear implant users that are not proficient at recognizing speech sounds might show abnormal audiovisual interactions. The present study aims at reinforcing this notion by investigating the audiovisual segregation abilities of cochlear implant users in a visual task with auditory distractors. Speechreading was assessed in two groups of cochlear implant users (proficient and non-proficient at sound recognition), as well as in normal controls. A visual speech recognition task (i.e. speechreading) was administered either in silence or in combination with three types of auditory distractors: i) noise ii) reverse speech sound and iii) non-altered speech sound. Cochlear implant users proficient at speech recognition performed like normal controls in all conditions, whereas non-proficient users showed significantly different audiovisual segregation patterns in both speech conditions. These results confirm that normal-like audiovisual segregation is possible in highly skilled cochlear implant users and, consequently, that proficient and non-proficient CI users cannot be lumped into a single group. This important feature must be taken into account in further studies of audiovisual interactions in cochlear implant users.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".