Modulation of Visually Evoked Movement Responses in Moving Virtual Environments
Bibliographic record
Abstract
Virtual-reality technology is being increasingly used to understand how humans perceive and act in the moving world around them. What is currently not clear is how virtual reality technology is perceived by human participants and what virtual scenes are effective in evoking movement responses to visual stimuli. We investigated the effect of virtual-scene context on human responses to a virtual visual perturbation. We hypothesised that exposure to a natural scene that matched the visual expectancies of the natural world would create a perceptual set towards presence, and thus visual guidance of body movement in a subsequently presented virtual scene. Results supported this hypothesis; responses to a virtual visual perturbation presented in an ambiguous virtual scene were increased when participants first viewed a scene that consisted of natural landmarks which provided 'real-world' visual motion cues. Further research in this area will provide a basis of knowledge for the effective use of this technology in the study of human movement responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".