Do Changes in the Body-Part Compatibility Effect Index Tool-Embodiment?
Bibliographic record
Abstract
Tool-embodiment is said to occur when the representation of the body extends to incorporate the representation of a tool following goal-directed tool-use. The present study was designed to determine if tool-embodiment-like phenomenon emerges following different interventions. Participants completed body-part compatibility task in which they responded with foot or hand presses to colored targets presented on the foot or hand of a model, or on a rake held by the model. This response time (RT) task was performed before and after one of four interventions. In the Virtual-Tangible and the Virtual-Keyboard interventions, participants used customized controllers or keyboards, respectively, to move a virtual rake and ball around a course. Participants in the Tool-Perception intervention manually pointed to targets presented on static images of the virtual tool-use task. Participants in the Tool-Absent group completed math problems and were not exposed to a tool task. Results revealed that all four interventions lead to a pattern of pre-/post-intervention changes in RT thought to indicate the emergence of tool-embodiment. Overall, the study indicated that tool-embodiment can occur through repeated exposure to the body-part compatibility paradigm in the absence of any active tool-use, and that the paradigm may tap into more than just body schema.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".