Asymmetrical representation of body orientation
Bibliographic record
Abstract
The perceived orientation of objects, gravity, and the body are biased to the left. Whether this leftward bias is attributable to biases in sensing or processing vestibular, visual, and body sense cues has never been assessed directly. The orientation in which characters are most easily recognized--the perceived upright (PU)--can be well predicted from a weighted vector sum of these sensory cues. A simple form of this model assumes that the directions of the contributing inputs are coded accurately and as a consequence participants tilted left- or right-side-down relative to gravity should exhibit mirror symmetric patterns of responses. If a left/right asymmetry were present then varying these sensory cues could be used to assess in which sensory modality or modalities a PU bias may have arisen. Participants completed the Oriented Character Recognition Test (OCHART) while manipulating body posture and visual orientation cues relative to gravity. The response patterns showed systematic differences depending on which side they were tilted. An asymmetry of the PU was found to be best modeled by adding a leftward bias of 5.6° to the perceived orientation of the body relative to its actual orientation relative to the head. The asymmetry in the effect of body orientation is reminiscent of the body-defined left-leaning asymmetry in the perceived direction of light coming from above and reports that people tend to adopt a right-leaning posture.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 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".