Acquiring Targets in the Velocity Domain: Toward Predictive Modeling of Virtual Tossing
Bibliographic record
Abstract
Tossing, throwing, or flicking objects in a user interface or virtual environment can be used as a faster, lower-precision alternative to traditional pointing, however there is currently no predictive model of user performance with tossing. We report experimental measurements of performance in a 1D tossing task from which a predictive model is derived. We consider a simplified form of tossing where a virtual object on a horizontal surface is accelerated and released, and then decelerates under friction, coming to rest at some final po- sition. The distance traveled after release is determined by the release velocity as well as by the friction model used. To abstract away the details of the friction model, our ex- periment measures the ability of users to accelerate and re- lease a virtual object in 1D (using a mouse) with a given tar- get velocity, with target velocities varying from 6.25 cm/s to 1 m/s. Results indicate that there is a linear relationship be- tween the target release velocity and the standard deviation of the release velocity achieved by the user. We also propose an automatic release technique (instead of requiring the user to manually release using a mouse button) that significantly improves precision. The model derived from our experiment predicts that a user should be able to toss at three different target speeds (effectively tossing toward target locations at three different distances) with an error rate under 4%. We also predict that having four or more targets in the same di- rection would cause the error rate to rise above 10%. Design implications for integrating tossing into graphical user inter- faces 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".