Effect of History Trail Display on Human Spatial Performance under Normal and Rotated Spatial Mappings
Bibliographic record
Abstract
In a rotated visual-motor mapping environment, human spatial performance is seriously affected by misaligned visual and motor reference axes, resulting in elevated spatial errors. In this paper, we propose a history trail display in an augmented reality setting. We investigate the effectiveness of the display in consecutive aiming tasks, under normal visual-motor mapping, as well as mappings with 90°, 135°, and 180° rotations. Spatial movement error and the smoothness of the trajectories were measured and compared between the history trail display and the regular video display. Our results show that, under normal mapping condition, the history trail appears to help reduce the spatial movement error and improve the smoothness of the movement trajectories. With rotated mappings, the benefit of the history trail becomes significant only after a certain degree of adaptation to the rotated mappings has been attained. The history trail appears to enhance the perception of errors, movement direction, and speed information for error-correcting processes during the aiming movements.
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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 | 0.004 |
| 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 | 0.001 |
| 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".