Rake-it-ball: Trying to measure tool-embodiment through a body-part compatibility task
Bibliographic record
Abstract
Tool embodiment refers to the effects of tool-use on the coding of space and our body schema such that the tool becomes extension of our physical effectors. One task that was thought to index this phenomenon was the body-part compatibility task because it provided an index of what is or is not being coded as a body part in the body schema. Previous employment of a body-part compatibility task has revealed a modification of the body schema when a tool was manipulated physically or virtually. The present study investigated the validity of the body-part compatibility task to measure tool-embodiment by assessing two types of interactions: 1) perception of the physical tool-interaction task, and 2) completion of math questions without any tool-interaction. Before and after the tool-interaction task, participant completed a body-part compatibility task in which they responded to targets presented on the image of a model holding a rake. Targets were presented on the foot, hand and rake. The first group made reaching movements to a target presented on an image of the actual tool interaction task while the control group completed math problems instead of any type of tool interaction. The results of the two groups were similar wherein the pattern of reaction times (RTs) indicated the emergence of tool-embodiment. The RTs to targets presented on the hand and the rake were found to be similar after completing a tool-irrelevant interaction task. These results indicate that the body-part compatibility task may not correctly assess tool-embodiment.Acknowledgments: This research was supported by grants from SSHRC and NSERC.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".