Two-Part Models Capture the Impact of Gain on Pointing Performance
Why this work is in the frame
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Bibliographic record
Abstract
We establish that two-part models of pointing performance (Welford’s model) describe pointing on a computer display significantly better than traditional one-part models (Fitts’s Law). We explore the space of pointing models and describe how independent contributions of movement amplitude and target width to pointing time can be captured in a parameter k . Through a reanalysis of data from related work we demonstrate that one-part formulations are fragile in describing pointing performance, and that this fragility is present for various devices and techniques. We show that this same data can be significantly better described using two-part models. Finally, we demonstrate through further analysis of previous work and new experimental data that k increases linearly with gain. Our primary contribution is the demonstration that Fitts’s Law is more limited in applicability than previously appreciated, and that more robust models, such as Welford’s formulation, should be adopted in many cases of practical interest.
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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 | 0.000 | 0.002 |
| Open science | 0.001 | 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