Master of the puppets: Evidence of physical tool appropriation following virtual practice
Bibliographic record
Abstract
Previous research has shown that humans appropriate tools and incorporate them into their body schema, effectively extending peripersonal space to include the volume of the tool. This tool appropriation occurs after physical practice and experience using the tool. The present study investigated whether manipulating a virtual representation of the tool via a computer interface would enable appropriation of the real (physical) tool. The index of tool appropriation was the change in reaction time (RT) to specific LEDs before and after virtual practice with a tool (a rake). In the RT task, participants fixated a central cross and were instructed to press a button with the left hand as quickly as possible when one of four LEDs illuminated. The LEDs were on the back of the right hand (1), the tip of a rake grasped in the right hand (2), and on the surface of the table to the left (3) and right (4) of the rake. During training, participants played a video game for ten minutes where they controlled (via keyboard) an avatar holding a rake identical to the one in the simple RT task. The key finding of the current experiment was a significant pre/post RT reduction for the LED on the tip of the rake, but not for the hand LED. This finding suggests that manipulating a tool via a keyboard-controlled virtual effector is sufficient to induce physical appropriation of a tool. The implications of this result for the interactions between real and virtual worlds will be discussed.Acknowledgments: This research was supported by funding from the Natural Sciences and Engineering Research Council of Canada, the Ontario Ministry of Research and Innovation, and the National Science Foundation.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".