Audiovisual Temporal Processing in Postlingually Deafened Adults with Cochlear Implants
Bibliographic record
Abstract
For many cochlear implant (CI) users, visual cues are vitally important for interpreting the impoverished auditory speech information that an implant conveys. Although the temporal relationship between auditory and visual stimuli is crucial for how this information is integrated, audiovisual temporal processing in CI users is poorly understood. In this study, we tested unisensory (auditory alone, visual alone) and multisensory (audiovisual) temporal processing in postlingually deafened CI users (n = 48) and normal-hearing controls (n = 54) using simultaneity judgment (SJ) and temporal order judgment (TOJ) tasks. We varied the timing onsets between the auditory and visual components of either a syllable/viseme or a simple flash/beep pairing, and participants indicated either which stimulus appeared first (TOJ) or if the pair occurred simultaneously (SJ). Results indicate that temporal binding windows-the interval within which stimuli are likely to be perceptually 'bound'-are not significantly different between groups for either speech or non-speech stimuli. However, the point of subjective simultaneity for speech was less visually leading in CI users, who interestingly, also had improved visual-only TOJ thresholds. Further signal detection analysis suggests that this SJ shift may be due to greater visual bias within the CI group, perhaps reflecting heightened attentional allocation to visual cues.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".