On Your Feet!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When navigating larger virtual environments and computer games, natural walking is often unfeasible. Here, we investigate how alternatives such as joystick- or leaning-based locomotion interfaces ("human joystick") can be enhanced by adding walking-related cues following a sensory substitution approach. Using a custom-designed foot haptics system and evaluating it in a multi-part study, we show that adding walking related auditory cues (footstep sounds), visual cues (simulating bobbing head-motions from walking), and vibrotactile cues (via vibrotactile transducers and bass-shakers under participants' feet) could all enhance participants' sensation of self-motion (vection) and involement/presence. These benefits occurred similarly for seated joystick and standing leaning locomotion. Footstep sounds and vibrotactile cues also enhanced participants' self-reported ability to judge self-motion velocities and distances traveled. Compared to seated joystick control, standing leaning enhanced self-motion sensations. Combining standing leaning with a minimal walking-in-place procedure showed no benefits and reduced usability, though. Together, results highlight the potential of incorporating walking-related auditory, visual, and vibrotactile cues for improving user experience and self-motion perception in applications such as virtual reality, gaming, and tele-presence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.007 |
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 it