Representing Human Hands Haptically or Visually from First-Person versus Third-Person Perspectives
Why this work is in the frame
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Bibliographic record
Abstract
Humans can recognise human body parts haptically as well as visually. We employed a mental-rotation task to determine whether participants could adopt a third-person perspective when judging the laterality of life-like human hands. Female participants adopted either a first-person or a third-person perspective using vision (experiment 1) or haptics (experiment 2), with hands presented at various orientations within a horizontal plane. In the first-person perspective task, most participants responded more slowly as hand orientation increasingly deviated from the participant's upright orientation, regardless of modality. In the visual third-person perspective task, most participants responded more slowly as hand orientation increasingly deviated from the experimenter's upright orientation; in contrast, less than half of the participants produced this same inverted U-shaped response-time function haptically. In experiment 3, participants were explicitly instructed to adopt a third-person perspective haptically by mentally rotating the rubber hand to the experimenter's upright orientation. Most participants produced an inverted U-shaped function. Collectively, these results suggest that humans can accurately assume a third-person perspective when hands are explored haptically or visually. With less explicit instructions, however, the canonical orientation for hand representation may be more strongly influenced haptically than visually by body-based heuristics, and less easily modified by perspective instructions.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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 it