Somatosensory-guided tool use modifies arm representation for action
Bibliographic record
Abstract
Tool-use changes both peripersonal space and body representations, with several effects being nowadays termed tool embodiment. Since somatosensation was typically accompanied by vision in most previous tool use studies, whether somatosensation alone is sufficient for tool embodiment remains unknown. Here we address this question via a task assessing arm length representation at an implicit level. Namely, we compared movement's kinematics in blindfolded healthy participants when grasping an object before and after tool-use. Results showed longer latencies and smaller peaks in the arm transport component after tool-use, consistent with an increased length of arm representation. No changes were found in the hand grip component and correlations revealed similar kinematic signatures in naturally long-armed participants. Kinematics changes did not interact with target object position, further corroborating the finding that somatosensory-guided tool use may increase the represented size of the participants' arm. Control experiments ruled out alternative interpretations based upon altered hand position sense. In addition, our findings indicate that tool-use effects are specific for the implicit level of arm representation, as no effect was observed on the explicit estimate of the forearm length. These findings demonstrate for the first time that somatosensation is sufficient for incorporating a tool that has never been seen, nor used before.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".