Visual Perception of Gravity: Effect on Speech Tongue Posture
Bibliographic record
Abstract
Visual perception of gravity change may impact tongue posture during speech production. Previous studies [Chander et al., 2019, Behav. Sci., 9(11)] report anticipatory visual disturbances inducing compensatory postural behavior in the human body [Philips et al., 2022, Theor. Iss. Ergon. Sci., 23, 25] and, furthermore, showed the magnitude of such postural responses is dependent on velocity and direction of visual perception. Assländer et al. [2023, Sci. Rep., 13, 2594] investigated the visual component impact on body posture balance in virtual reality (VR). An earlier VR study on tongue posture [Chernets et al., 2024, JASA 156] found that posture significantly differed between visually perceived falling vs. rising, and levelled vs. rising gravitational conditions; however, there was inter-participant variation in the direction of tongue posture change with no generalizable consistent directional change across participants. The current study investigates tongue posture in speech production during perceived gravity changes in an immersive VR environment. Ultrasound imaging is used to measure tongue posture changes during speech production compared to a non-speech condition while experiencing a VR plank-walk in gravitationally stable and falling conditions. Furthermore, within the falling condition posture is separated into weightless falling (initial and midpoint of fall) vs. force of fall (endpoint of fall). Acoustic analyses compare the production of an elongated vowel, nonsense CVC sequences, and CVC word sequences across conditions. We predict that the falling condition will induce postural adjustment for vowels, resulting in different tongue postures between the falling and level conditions. Results will be presented for a post-trial immersion questionnaire investigating a possible correlation between reported level of VR immersion and degree of tongue posture compensation, as well as for acoustic and articulatory analyses. Implications for postural adaptation in VR and real environments will be discussed.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".