The displacement biases accompanying the "Violation of Fitts' Law"
Bibliographic record
Abstract
Numerous studies have revealed that movement times to the last target of a placeholder array are shorter than predicted by Fitts' Law. Glazebrook et al. (2015) suggested that this violation of Fitts' Law occurs because of re-accelerations following an optimal planning procedure biased toward the first target. The present study examined the planning and control procedures associated with this violation via a detailed analysis of amplitude biases and corrective submovements throughout the movement trajectory. Sixteen participants executed fast-and-accurate aiming movements toward one of five possible target locations within a placeholder array. Movement times were shorter for targets 1 and 2 compared to targets 3, 4 and 5, which were not different from each other. Movement times to target 5 were shorter than those predicted by Fitts' Law. This difference was due to the time after peak velocity. Although there was no difference in overall error between targets, participants overshoot the centre of the target more for target 5. This bias can be partly attributed to greater proportional amplitudes at peak velocity. Further, corrective submovements were observed on 89.3% of the trials, with a greater proportion of these submovements involving velocity zero-crossings (i.e., reversals), and fewer acceleration zero-crossings (i.e., secondary accelerations) for target 5. Overall our results indicate that, in the face of target uncertainty, participants biased their movement planning in favour of target 5 (i.e., highest index of difficulty movement). This notion is consistent with the idea that performers prepare for the worst-case scenario (Elliott et al., 2010).Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).
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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 | 0.002 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".