Contribution of Bodily and Gravitational Orientation Cues to Face and Letter Recognition
Bibliographic record
Abstract
Sensory information provided by the vestibular system is crucial in cognitive processes such as the ability to recognize objects. The orientation at which objects are most easily recognized--the perceptual upright (PU)--is influenced by body orientation with respect to gravity as detected from the somatosensory and vestibular systems. To date, the influence of these sensory cues on the PU has been measured using a letter recognition task. Here we assessed whether gravitational influences on letter recognition also extend to human face recognition. 13 right-handed observers were positioned in four body orientations (upright, left-side-down, right-side-down, supine) and visually discriminated ambiguous characters ('p'-from-'d'; 'i'-from-'!') and ambiguous faces used in popular visual illusions ('young woman'-from-'old woman'; 'grinning man'-from-'frowning man') in a forced-choice paradigm. The two transition points (e.g., 'p-to-d' and 'd-to-p'; 'young woman-to-old woman' and 'old woman-to-young woman') were fit with a sigmoidal psychometric function and the average of these transitions was taken as the PU for each stimulus category. The results show that both faces and letters are more influenced by body orientation than gravity. However, faces are more optimally recognized when closer in alignment with body orientation than letters--which are more influenced by gravity. Our results indicate that the brain does not utilize a common representation of upright that governs recognition of all object categories. Distinct areas of ventro-temporal cortex that represent faces and letters may weight bodily and gravitational cues differently--possibly to facilitate the specific demands of face and letter recognition.
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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 | 0.003 |
| 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) | 0.002 | 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".