Constraints and principles for the design of human-machine interfaces: a virtual reality approach
Bibliographic record
Abstract
We conducted two experiments to compare the visual frames of reference used to scale grasping movements directed at objects with those used to estimate the size of the same objects - either immediately or after a 5-s delay. A virtual "workbench" was employed for presenting two different-sized objects in 3D. Subjects were instructed to pick up or estimate the marked one of the two objects. We found that the presence of the other object affected not only the estimate of the size of the target object when subjects made their estimates both immediately and after a 5-s delay, but also the scaling of grip aperture in flight when subjects picked up the target object after a 5-s delay. However, when subjects picked up the target object immediately, their grasp was scaled to the actual size of the target object and was not influenced by the presence of the other object. These findings suggest that the control of delayed motor actions utilizes the same relative metrics in allocentric frames of reference used by conscious perception, whereas the control of normal visually guided motor actions relies on absolute metrics in egocentric frames of reference. Implications of these findings for the design of human-machine interfaces are 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".