Representing Human Hands Haptically or Visually from First-Person versus Third-Person Perspectives
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".