Visually biased Perception in Cochlear Implant Users: A Study of the McGurk and Sound-Induced Flash Illusions
Bibliographic record
Abstract
The reduction in spectral resolution by cochlear implants oftentimes requires complementary visual speech cues to facilitate understanding. Despite substantial clinical characterization of auditory-only speech measures, relatively little is known about the audiovisual (AV) integrative abilities that most cochlear implant (CI) users rely on for daily speech comprehension. In this study, we tested AV integration in 63 CI users and 69 normal-hearing (NH) controls using the McGurk and sound-induced flash illusions. To our knowledge, this study is the largest to-date measuring the McGurk effect in this population and the first that tests the sound-induced flash illusion (SIFI). When presented with conflicting AV speech stimuli (i.e., the phoneme "ba" dubbed onto the viseme "ga"), we found that 55 CI users (87%) reported a fused percept of "da" or "tha" on at least one trial. After applying an error correction based on unisensory responses, we found that among those susceptible to the illusion, CI users experienced lower fusion than controls-a result that was concordant with results from the SIFI where the pairing of a single circle flashing on the screen with multiple beeps resulted in fewer illusory flashes for CI users. While illusion perception in these two tasks appears to be uncorrelated among CI users, we identified a negative correlation in the NH group. Because neither illusion appears to provide further explanation of variability in CI outcome measures, further research is needed to determine how these findings relate to CI users' speech understanding, particularly in ecological listening conditions that are naturally multisensory.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".