Necessary and Unnecessary Distractor Avoidance Movements Affect User Behaviors in Crossing Operations
Why this work is in the frame
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Bibliographic record
Abstract
The “crossing time” to pass between objects in lassoing tasks is predicted by Fitts’ law. When an unwanted object, or obstacle , intrudes into the user’s path, users curve the stroke to avoid hitting that obstacle. We empirically show that, in the presence of an obstacle, modified Fitts models for pointing with obstacle avoidance can significantly improve the prediction accuracy of movement time compared with standard Fitts’ law. Yet, we also found that when an object is (only) close to the crossing path, i.e., a distractor , users still curve their stroke, even though the object does not intrude. We tested the effects of distractor proximity and length. While the crossing motion is modified by a nearby distractor, our results also identify that overall its effect on crossing times was small, and thus Fitts’ law can still be applied safely with distractors.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it