Body Pitch Together With Translational Body Motion Biases the Subjective Haptic Vertical
Bibliographic record
Abstract
Accurate perception of verticality is critical for postural maintenance and successful physical interaction with the world. Although previous research has examined the independent influences of body orientation and self-motion under well-controlled laboratory conditions, these factors are constantly changing and interacting in the real world. In this study, we examine the subjective haptic vertical in a real-world scenario. Here, we report a bias of verticality perception in a field experiment on the Hong Kong Peak Tram as participants traveled on a slope ranging from 6° to 26°. Mean subjective haptic vertical (SHV) increased with slope by as much as 15°, regardless of whether the eyes were open (Experiment 1) or closed (Experiment 2). Shifting the body pitch by a fixed degree in an effort to compensate for the mountain slope failed to reduce the verticality bias (Experiment 3). These manipulations separately rule out visual and vestibular inputs about absolute body pitch as contributors to our observed bias. Observations collected on a tram traveling on level ground (Experiment 4A) or in a static dental chair with a range of inclinations similar to those encountered on the mountain tram (Experiment 4B) showed no significant deviation of the subjective vertical from gravity. We conclude that the SHV error is due to a combination of large, dynamic body pitch and translational motion. These observations made in a real-world scenario represent an incentive to neuroscientists and aviation experts alike for studying perceived verticality under field conditions and raising awareness of dangerous misperceptions of verticality when body pitch and translational self-motion come together.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".