Towards a better understanding of multimodal integration and sensorimotor adaptation to audiovisual environmental incongruence using Virtual Reality
Bibliographic record
Abstract
People use previous knowledge and in situ judgment to produce speech with a vocal effort appropriate to a given environment’s acoustics. To test how people integrate auditory and visual cues in speech production, we employed a three-by-three cross-conditional audiovisual match-mismatch paradigm. Three visually distinct environments with three different room acoustics were selected: a gymnasium, a classroom, and a hemi-anechoic room. The visual environment was presented with a Virtual Reality (VR) headset and the auditory environment was a diffuse room impression, playing back the participants’ speech through loudspeakers in the room with different reverberation times. Participants were prompted to speak in all nine combinations of the audiovisual conditions, with three being congruent and six incongruent. Linear mixed-effects regression modeling was used to evaluate the effect of the audiovisual manipulations and time course on mean intensity. Preliminary results indicate that participants initially spoke at a level that matched the visual expectation and then adapted to the audio condition; detailed analysis of the time course of adaptation is ongoing and will be presented. This study furthers our understanding of multimodal integration and the sensorimotor adaptation of speech production, which finds applications in fields including communication in noise and VR soundscape design.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".