Why Peter Pan might never empathize with Captain Hook: The sensitivity of the human body schema to hidden, missing, and prosthetic limbs
Bibliographic record
Abstract
It has been proposed that our brain stores a representative map of our dynamic body shape in space, known as the body schema that we can use in social cognitive processes such as self-other matching and empathy. One method used to study the body schema and self-other matching involves the body part compatibility task (Bach, Peatfield & Tipper, 2007). This research has shown that when target stimuli are presented on the limbs (e.g., hand) of an observed body, response initiation times are shorter when the response is made with the same limb (e.g., hand) in the observer than when it is made with a different limb (e.g., foot). These data suggest that we identify with the observed body and respond to the stimuli with our own compatible limbs. The purpose of the present experiments was to use the body-part compatibility effect task to assess the coding of observed limbs when observing altered body forms. In Experiment 1, when the target limb (hand) was hidden behind an object (backpack) we observed a body part compatibility effect consistent to a full-limb control suggesting that the observer was able to intuit the limb behind the backpack and match the response compatibility. In Experiment 2, when the observers viewed stimuli on a non-existent limb (limb amputee) there was a disruption in the body part compatibility effect. Finally, in Experiment 3, when the non-existent amputated limb was extended via a prosthetic limb (that matched the size attributes of a control full-limb body) the body part compatibility effect did not consistently re-emerge. In sum, the present data suggest that the human body schema is sensitive to modulation depending on the integrity of the human body being observed. Acknowledgments: This research was supported by grants from NSERC and the Ontario Ministry of Research and Innovation.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".