Target interception in virtual reality is better for natural versus unnatural trajectory shapes and orientations
Bibliographic record
Abstract
Human performance in perceptual and visuomotor tasks is enhanced when stimulus motion follows the laws of gravitational physics, including acceleration consistent with Earth's gravity, g. Here we used a manual interception task in virtual reality to investigate the effects of trajectory shape and orientation on interception timing and accuracy. Participants punched to intercept a ball moving along one of four trajectories that varied in shape (parabola or tent) and orientation (upright or inverted). We also varied the location of visual fixation such that trajectories fell entirely within the lower or upper visual field. Reaction times were faster for more natural shapes and orientations, regardless of visual field. Overall accuracy was poorer and movement time was longer for the inverted tent condition than the other three conditions, perhaps because it was imperfectly reminiscent of a bouncing ball. A detailed analysis of spatial errors revealed that interception endpoints were more likely to fall along the path of the final trajectory in upright vs. inverted conditions, suggesting stronger expectations regarding the final trajectory direction for these conditions. Taken together, these results suggest that the naturalness of the shape and orientation of a trajectory contributes to performance in a virtual interception task.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".