Multimodal sensorimotor investigation of audio-visual integration in cochlear implant users
Bibliographic record
Abstract
Rhythm is an omnipresent element of many daily activities. Numerous studies in cognitive sciences have highlighted that humans exhibit greater precision in synchronizing their movements with auditory rhythmic stimuli compared to visual ones. Deaf individuals were shown to excel in synchronizing with visual cues, surpassing those with normal hearing (NH). Furthermore, it was demonstrated that cochlear implant (CI) users were able to move in time to the beat of music, although not as well as NH controls. This study aims to investigate whether CI users retain a visual synchronization advantage from their pre-implant deafness, while maintaining auditory synchronization skills comparable to those of NH individuals, or if the neural reorganization post-implantation negates the visual synchronization advantage acquired pre-implantation. Specifically, we assessed both unimodal and multimodal auditory and visual abilities in CI users compared to NH controls using a standard sensorimotor synchronization paradigm. Results revealed that CI users exhibit comparable auditory rhythmic synchronization abilities to NH individuals, which is consistent with existing research, while not displaying a superior ability in synchronizing with visual rhythms, likely due to neural reorganization following implantation. This shift in audio-visual integration among CI users suggests that the post-implant reorganization of their auditory cortex might hinder the effective integration of temporal auditory input from the implant with visual information.
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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 | 0.001 |
| 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".