Audiovisual fusion and cochlear implant proficiency
Bibliographic record
Abstract
PURPOSE: Recent studies suggest that cochlear implant (CI) users have a typical, and perhaps improved, ability to fuse congruent multisensory information. The ability to fuse incongruent auditory and visual inputs, however, remains to be fully investigated. METHODS: Here, performance on a classical audiovisual task (the McGurk effect) was assessed in seventeen cochlear-implanted, postlingually deaf individuals with varied degrees of auditory competency. RESULTS: In line with previous studies, our results revealed audiovisual fusion abilities that were within normal limits in CI users compared to normally-hearing (NH) participants. A different pattern of response emerged, however, when participants' responses were analyzed according to the degree of auditory proficiency with the CI. Although proficient CI users (pCI) and NH participants favoured auditory input when multisensory signals were not fused, only the non-proficient CI users (npCI) relied predominantly on visual cues to resolve audiovisual conflict. This pattern was found despite a similar percentage of fused percepts between pCI users, npCI users and NH participants. CONCLUSION: These data show a remarkable level of similarity between pCI users and NH individuals in the perception of incongruent audiovisual information, suggesting that optimal auditory performance with the CI is associated with normal fusion of conflicting audiovisual input.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".