Multisensory Integration in Cochlear Implant Recipients
Bibliographic record
Abstract
Speech perception is inherently a multisensory process involving integration of auditory and visual cues. Multisensory integration in cochlear implant (CI) recipients is a unique circumstance in that the integration occurs after auditory deprivation and the provision of hearing via the CI. Despite the clear importance of multisensory cues for perception, in general, and for speech intelligibility, specifically, the topic of multisensory perceptual benefits in CI users has only recently begun to emerge as an area of inquiry. We review the research that has been conducted on multisensory integration in CI users to date and suggest a number of areas needing further research. The overall pattern of results indicates that many CI recipients show at least some perceptual gain that can be attributable to multisensory integration. The extent of this gain, however, varies based on a number of factors, including age of implantation and specific task being assessed (e.g., stimulus detection, phoneme perception, word recognition). Although both children and adults with CIs obtain audiovisual benefits for phoneme, word, and sentence stimuli, neither group shows demonstrable gain for suprasegmental feature perception. Additionally, only early-implanted children and the highest performing adults obtain audiovisual integration benefits similar to individuals with normal hearing. Increasing age of implantation in children is associated with poorer gains resultant from audiovisual integration, suggesting a sensitive period in development for the brain networks that subserve these integrative functions, as well as length of auditory experience. This finding highlights the need for early detection of and intervention for hearing loss, not only in terms of auditory perception, but also in terms of the behavioral and perceptual benefits of audiovisual processing. Importantly, patterns of auditory, visual, and audiovisual responses suggest that underlying integrative processes may be fundamentally different between CI users and typical-hearing listeners. Future research, particularly in low-level processing tasks such as signal detection will help to further assess mechanisms of multisensory integration for individuals with hearing loss, both with and without CIs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".