Bibliographic record
Abstract
The term Eye-hand co-ordination refers to hand movements controlled with visual feedback and reinforced by hand contact with objects. A correct perspective view of a virtual environment enables normal eye-hand co-ordination skills to be applied. But is it necessary for rapid interaction with 3D objects? A study of rapid hand movements is reported using an apparatus designed so that the user can touch a virtual object in the same place where he or she sees it. A Fitts tapping task is used to assess the effect of both contact with virtual objects and real-time update of the centre of perspective based on the user's actual eye position. A Polhemus tracker is used to measure the user's head position and from this estimate their eye position. In half of the conditions, head tracked perspective is employed so that visual feedback is accurate while in the other half a fixed eye-position is assumed. A Phantom force feedback device is used to make it possible to touch the targets in selected conditions. Subjects were required to change their viewing position periodically to assess the importance of correct perspective and of touching the targets in maintaining eye-hand co-ordination, The results show that accurate perspective improves performance by an average of 9% and contact improves it a further 12%. A more detailed analysis shows the advantages of head tracking to be greater for whole arm movements in comparison with movements from the elbow.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".