Contributions of the body and head to perceived vertical: Cross-modal differences
Bibliographic record
Abstract
At whole-body tilts of 45°, a bias in the subjective visual vertical (SVV) towards the direction of tilt has been reported (the "A" or Aubert effect). This bias has been attributed to a tendency for the perceived direction of gravity to shift towards the longitudinal body axis (MacNeilage et al., 2007). However, it is unclear whether this bias exists in non-visual measures of gravity perception (e.g., Bortolami et al., 2006). Here we directly compared haptic (SHV) and visual (SVV) judgments of a rod's verticality relative to gravity. To assess the relative contributions of the head and body axes on verticality perception we varied body and head tilt independently. When the body was tilted 45° with the head upright, the SVV and SHV were both biased towards the direction of body tilt. When the body was upright with the head tilted 45°, the SVV bias was towards the head and increased in magnitude, but the SHV did not significantly differ from the gravity and body axes. Our findings agree with previous reports that SVV is biased primarily towards head position, but is also influenced to a lesser extent by body tilt. A novel finding is that biases of the SHV appear to be largely related to body orientation and not head orientation, potentially explaining some of the inconsistencies in the SHV literature. Clemens and colleagues (2011) have proposed two systems for estimating the direction of gravity, one using the head's position as a reference and the other using the body's position. Our results suggest the body system may play a stronger role in SHV than SVV, possibly because it is more computationally efficient to compare hand position to the body rather than the head. Meeting abstract presented at VSS 2014
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".