A case for using effective target width and terminal feedback when replicating Fitts's Law?
Bibliographic record
Abstract
Fitts's Law (1954) states that the time required to complete a pointing movement (MT) is dependent on the index of difficulty (ID) of the target, which is calculated using movement amplitude and target width. However, Heath et al. (2011) reported that the influence of amplitude and width on MT/ID slopes differ significantly, which challenges Fitts's Law. While early research on Fitts's Law (e.g., Fitts and Peterson, 1964) included terminal feedback of endpoint accuracy, more recent research has not. The current study aimed to reproduce the results of Heath et al. (2011) and also assess the influence of terminal feedback on the MT/ID slopes for amplitude and width manipulations. In two separate sessions, participants made fast and accurate discrete pointing movements to one of sixteen targets (4 amplitudes; 4 widths) with ID's ranging from 2.63 to 5.25. In one of the sessions, participants were given visual terminal feedback regarding endpoint accuracy following the completion of each trial (undershoot, accurate, overshoot). As in Heath et al. (2011), the influence of amplitude on MTs was significantly greater than the influence of target width. However, when using the effective target width to calculate the effective ID (IDe), the MT/IDe slopes associated with amplitude no longer differed from the MT/IDe slopes associated with target width. In addition, participants differed in their use of terminal feedback.Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council (NSERC), the Canada Foundation for Innovation (CFI) and the Ontario Research Fund (ORF).
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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 | 0.063 | 0.373 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".