Bibliographic record
Abstract
Humans rely on both visual and kinesthetic cues to register self-motion and navigate through the world. Normally, these sources of information are consistent with the perceived motion through the environment. Virtual Reality (VR) often introduces visual/kinesthetic inconsistency due to scale differences between the physical space and the simulated virtual space (motion gain). Large amounts of left-right self-motion gain has been found to compress apparent distance and monocular depth. In the present study, we asked whether observers adapt to exposure to extended periods of high or low motion gain. In the adaptation phase, observers played a VR game. They moved laterally to intersect targets with their body; their virtual motion was scaled to be either 0.67, 1 or 2 times their physical motion. These three adaptation conditions were presented in separate sessions, each starting with 5 minutes of initial adaptation, followed by testing interleaved with three 2-minute top-up adaptation periods. During the test phase, observers swayed left-right over 20 cm to the beat of a 0.5 Hz metronome and indicated if the virtual environment moved ‘more or less than they did’. Using a method of constant stimuli, we measured the PSE for each gain in separate blocks. Results from 18 observers showed that there were no consistent differences between the PSEs obtained in the three gain conditions. Neither increasing the adaptation duration nor testing monocularly affected this pattern of results. These data suggest that while observers are sensitive to differences between their intended movement and that rendered in VR, they do not appear to adapt to a constant mismatch in the current setup. Ongoing experiments are evaluating if this is also true for forward-backward motion. Lack of adaptation to mismatches between self and world motion may be an important evolutionary strategy as such distortions could signal a hazardous situation (e.g. poisoning).
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.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".