Edward Rake-Hands: Evidence for tool appropriation following experience
Bibliographic record
Abstract
The purpose of the present research was to extend previous explorations of the processes by which task-specific objects are incorporated into our body schema and movement repertoire (i.e., tool appropriation). Previous research on tool appropriation suggests that through physical interaction with a tool in real time, the representation of our body is adjusted to “embody” the tool. As a result of the interaction, this dynamic change in body representation remains regardless of maintaining a physical connection with this tool. In the present experiment, tool embodiment was examined by asking participants to complete a body-part compatibility task in which they executed hand- and foot-press responses to coloured targets superimposed on the hand, foot and rake of a digital image before and after learning to use a rake. Consistent with previous research on the body-part compatibility effect, response times (RTs) were shortest when the responding limb and the target location were compatible (e.g., hand responses to targets on the hand) than when they were incompatible (e.g., hand responses to targets on the foot). Of greater theoretical relevance was the effect of tool-use on RTs to targets on the rake. After interacting with a rake, there was a significant reduction in the hand RTs to targets on the rake (p<0.05), with no significant changes in hand RTs to targets on the foot or hand (p>0.05). These results suggest that through physical interaction, a tool can become represented within the cortex as if it were a part of the human body.Acknowledgments: This research was supported by grants from NSERC and the Ontario Ministry of Research and Innovation.
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 | 0.002 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".