Agency and Control for the Integration of a Virtual Tool into the Peripersonal Space
Bibliographic record
Abstract
Our representation of the peripersonal space is tied to our representation of our bodies. This representation appears to be flexible and it can be updated to include the space in which tools work, particularly when the tool is actively used. One indicator of this update is the increased efficiency with which sensory events near the tool are processed. In the present study we examined the role of visuomotor control in extending peripersonal space to a common virtual tool-a computer mouse cursor. In particular, after participants were exposed to different spatial mappings between movements of the mouse cursor and movements of their hand, participants' performance in a motion-onset detection task was measured, with the mouse cursor as the stimulus. When participants, during exposure, had the ability to move the cursor efficiently and accurately (familiar hand-cursor mapping), they detected motion-onset targets more quickly than when they could not move the cursor at all during exposure (no hand-cursor mapping). Importantly, reversing the spatial correspondence between the movements of the hand and the cursor (unfamiliar hand-cursor mapping) during exposure, which was thought to preserve the ability to move the cursor (ie agency) while weakening the ability to make the movements efficiently and accurately (ie control), eliminated the detection-facilitation effect. These results provide evidence for the possible extension of peripersonal space to frequently used objects in the virtual domain. Importantly, these extensions seem to depend on the participant's knowledge of the dynamic spatial mapping between the acting limb and the visible virtual tool.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".