A Fitts Law comparison of eye tracking and manual input in the selection of visual targets
Bibliographic record
Abstract
We present a Fitts' Law evaluation of a number of eye tracking and manual input devices in the selection of large visual targets. We compared performance of two eye tracking techniques, manual click and dwell time click, with that of mouse and stylus. Results show eye tracking with manual click outperformed the mouse by 16%, with dwell time click 46% faster. However, eye tracking conditions suffered a high error rate of 11.7% for manual click and 43% for dwell time click conditions. After Welford correction eye tracking still appears to outperform manual input, with IPs of 13.8 bits/s for dwell time click, and 10.9 bits/s for manual click. Eye tracking with manual click provides the best tradeoff between speed and accuracy, and was preferred by 50% of participants. Mouse and stylus had IPs of 4.7 and 4.2 respectively. However, their low error rate of 5% makes these techniques more suitable for refined target selection.
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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 | 0.007 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.001 |
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".