Postural and emotional changes following repeated exposure to standing at a virtual height
Bibliographic record
Abstract
Postural threat influences standing balance control, yet only some of these changes are attenuated with repeated exposure to the threat (Johnson et al. 2019; Zaback et al. 2019). This study used virtual reality to repeatedly expose subjects to a height-induced postural threat to determine if similar emotional and postural changes are observed. We hypothesized that repeated exposure to a virtual-height would minimize the emotional response, however, most threat-related balance control changes would remain unchanged. Seven healthy young adults (6 females) completed a series of 60-s quiet standing trials while wearing a head-mounted display and immersed in a virtual lab standing on a Low height ( 0.8m above ground, away from the platform edge; 2 trials) and High platform (3.2m above ground, at platform edge; 15 trials). Each trial was followed by a seated rest. Fear of falling, perceived anxiety and confidence, and attentional focus were measured using questionnaires. Electrodermal activity (arousal) and forceplate-derived centre of pressure (COP) measures were recorded for each trial. Fear, anxiety, confidence, arousal, and attentional focus all significantly changed with initial exposure to a virtual-height and were subsequently attenuated following repeated exposure. In contrast, most height-related changes in balance control did not adapt with repeated exposure. These results suggest that some threat-related postural changes are context dependent, and repeated exposure to virtual heights can be used to examine relationships between emotional state, attentional focus, and postural control.Acknowledgments: Funded by NSERC
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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 | 0.000 | 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) | 0.002 | 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".