The Role of Graphical Feedback About Self-Movement when Receiving Objects in an Augmented Environment
Bibliographic record
Abstract
This work explored how the presence of graphical information about self-movement affected reach-to-grasp movements in an augmented environment. Twelve subjects reached to grasp objects that were passed by a partner or rested on a table surface. Graphical feedback about self-movement was available for half the trials and was removed for the other half. Results indicated that removing visual feedback about self-movement in an object-passing task dramatically affected both the receiver's movement to grasp the object and the time to transfer the object between partners. Specifically, the receiver's deceleration time, and temporal and spatial aspects of grasp formation, showed significant effects. Results also indicated that the presence of a graphic representation of self-movement had similar effects on the kinematics of reaching to grasp a stationary object on a table as for one held by a stationary or moving partner. These results suggest that performance of goal-directed movements, whether to a stationary object on a table surface or to objects being passed by a stationary or moving partner, benefits from a crude graphical representation of the finger pads. The role of providing graphic feedback about self-movement is discussed for tasks requiring precision. Implications for the use of kinematic measures in the field of Human-Computer Interaction (HCI) are also discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".