The reach of Fitts' theorem into and beyond the real world
Bibliographic record
Abstract
Paul Fitts conducted landmark studies involving upper-limb movements to show how the amplitude of the reaching movement and the width of the target influence the time taken to complete the movements (Fitts 1954: J Exp Psychol; Fitts and Peterson 1964: J Exp Psychol). The formula developed in these studies has been shown to account for relationships between the speed of upper-limb movements and its accuracy. However, one notable problem with the proposed formula is the presumed unitary relationship between amplitude and width manipulations. On one hand, Heath et al. (2011: C J Exp Psychol) reported stronger than expected influences on movement time with amplitude compared to width manipulations. On the other hand, a close re-analysis of Fitts and Peterson indicates stronger than expected influences of width compared to amplitude manipulations. In this presentation, three possible explanations of the discordance between actual and expected influences of amplitude vs. width manipulations on movement time will be discussed. The first explanation is associated with the use of actual vs. effective target width. The second explanation concerns the availability of terminal feedback. The third explanation is related to the actual target sizes employed and their relevance in the real world.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".