"Edward Rake-Hands" Part II: Does embodiment of a real tool occur via virtual tool interaction?
Bibliographic record
Abstract
The present study investigated the incorporation of task-specific objects into our body schema (i.e., tool appropriation/embodiment). Previous research on tool appropriation suggests that, through physical interaction with a tool, the representation of our body is adjusted to "embody" the tool. The present experiment considered the different mediums in which a tool-use can be learned. To this end, participants were asked to complete an adapted body-part compatibility task before and after completing a real or virtual tool interaction task in which they moved objects around with a rake (participants used buttons on a keyboard to control the rake in the virtual task). Participants were presented with images of a person holding a rake and were required to execute hand- and foot-press responses to coloured targets (red and blue, respectively) superimposed on the hand, foot and rake of the image. Consistent with previous research on the body-part compatibility effect, response times (RTs) were shorter 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). Evidence for tool embodiment after real experience was observed because hand RTs to targets presented on the hand were shorter than RTs to targets on the rake prior to experience, but there was no difference between RTs to targets on the hand and rake after the real rake task. The similarity in RTs emerged because there was a significant reduction in RTs to targets on the rake following experience. In contrast, hand RTs to targets on the rake did not change with virtual rake experience. These data suggest that the virtual tool interaction in the present experimental conditions was not sufficient for participants to embody the tool. Meeting abstract presented at VSS 2014
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".