NaviChair
Bibliographic record
Abstract
This research aims to investigate if using a more embodied interface that includes motion cueing can facilitate spatial updating compared to a more traditional non-embodied interface. The ultimate goal is to create a simple, elegant, and effective self-motion control interface. Using a pointing task, we quantify spatial updating in terms of mean pointing error to determine how two modes of locomotion compare: user powered motion cueing (use your body to swivel and tilt a joystick-like interface) and no-motion cueing (traditional joystick). Because the user-powered chair is a more embodied interface providing some minimal motion cueing, we hypothesized it should more effectively support spatial updating and, thus, increase task performance. Results showed, however, the user-powered chair did not significantly improve mean pointing performance in a virtual spatial orientation task (i.e., knowing where users are looking in the VE). Exit interviews revealed the control mechanism for the user-powered chair was not as accurate or easy to use as the joystick, although many felt more immersed. We discuss how user feedback can guide the design of more effective user-powered motion cueing to overcome usability issues and realize benefits of motion cueing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.333 | 0.177 |
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".