Left/right asymmetries in the contribution of body orientation to the perceptual upright
Bibliographic record
Abstract
INTRODUCTION The direction of the orientation at which objects and characters are most easily recognized, the perceived upright has been modelled as a weighted vector sum of the directions defined by the body's long axis (egocentric), gravity, and visible cues (Dyde et al. 2006, Exp. Brain Res.). This model predicts symmetrical responses such that subjects lying left or right side down relative to gravity should exhibit mirror symmetric patterns of responses. Such symmetry is also expected if torsional eye orientation dependent upon body orientation relative to gravity or visual orientation relative to the body is included in the model. METHODS Nineteen subjects drawn from researchers and students at York University participated. The Oriented Character Recognition Test (OCHART - described in Dyde et al. 2006) was administered while subjects viewed several orientations of visual background while either upright, left side down, or right side down relative to gravity. OCHART identifies the perceptual upright using the perceived identity of letters. RESULTS Responses revealed a systematic difference between the response pattern when lying left side down and lying right side down. This asymmetry can be modelled by a leftwise bias in the perceived orientation of the body relative to its actual orientation. DISCUSSION The asymmetry in the effect of body orientation is reminiscent of the left-leaning asymmetry in determining the direction of light coming from above (Mamassian & Goutcher 2001 Cognition 81:B1). The asymmetry might reflect a similar tendency to perceive the body as tilted.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".