Differences in perceptions of aperture crossing during a virtual reality choice reaction task according to the temporality of visual stimuli
Bibliographic record
Abstract
The purpose of this study was to determine whether the amount of viewing time prior to crossing a converging aperture would affect individuals' perceptions about passability. It was hypothesized that shorter durations of viewing would negatively affect response time (RT) and passability accuracy of a converging aperture. Eleven adults (x=20.77+/-0.83years) walked along a 7.5m pathway towards a goal in virtual reality while two avatars moved at various rates along 45° converging angles towards a theoretical crossing area. Aperture widths at the theoretical crossing area ranged from 0.8 to 1.8x shoulder width, at increments of 0.2. Visual information was removed at 0.5, 1.0, 1.5, or 2.0s prior to theoretical crossing and participants were instructed to indicate whether they were able to successfully pass through the approaching avatars without rotating their shoulders. Results revealed that there was a main effect of disappearance time on RT, such that removing the scene 2.0s prior to crossing resulted in slower RTs (M=1.157,) compared 0.5s, 1.0, or 1.5 (M=0.608; M=.845; M=1.059; respectively). There was also a main effect of disappearance time on accuracy, such that participants' estimation of passability became less accurate as the disappearance time increased from 0.5s to 2.0s (p
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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 | 0.008 |
| 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) | 0.001 | 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".