Bibliographic record
Abstract
In this thesis, we present the results of a user study that compares three different selection methods for moving targets in 1D and 2D space. The standard Chase-and-Click method involves pursuing an onscreen target with the mouse pointer and clicking on it once directly over it. The novel Click-to-Pause method involves first depressing the mouse button to pause all onscreen action, moving the cursor over the target and releasing the mouse button to select it. The Hybrid method combines the initial pursuit with the ability to pause the action by depressing the mouse button, affording an optimization of the point of interception. Our results show that the Click-to-Pause and Hybrid methods results in lower selection times than the Chase-and-Click method for small or fast targets, while the Click-to-Pause technique is the lowest overall for small-fast targets. We integrate the more practical Hybrid method into a multi-view video browser to enable the selection of hockey players in a pre-recorded hockey game. We demonstrate that the majority of correct player selections were performed while the video was paused and that our display method for extraneous information has no effect on selection task performance. We develop a kinematic model that is based on movement speed and direction in 1D as an adjustment to the effective width and distance of a target. Our studies show that target speed assists users when a target is approaching, up to a critical velocity where the direction is irrelevant and speed is entirely responsible for the index of difficulty. In addition, we suggest that existing linear and discrete models of human motor control are inadequate for modeling the selection of a moving target and recommend the minimum jerk law as a guide for measuring human motor acceleration. By combining our empirical results from moving target selection tasks in 1D with our theoretical model for motor control, we propose an extension to Fitts’ Law for moving targets in 2D polar space.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".