Fitts (1954: J Exp Psychol)
Bibliographic record
Abstract
Paul Fitts' classic work (1954: J Exp Psychol) is a staple for all undergraduate courses in motor control and is a core topic in human factors and systems engineering. Fitts' work has garnered significant interest because it provides a basic and elegant formulation for predicting movement time for goal-directed actions (i.e., Index of Difficulty in bits of information: ID=[log2[2A/W]). Indeed, some researchers have stated that Fitts' work provides a law-based measure of human performance – an assertion quantifying Fitts' Law as the movement sciences law of relativity. The goal of this symposium is to outline recent work examining fundamental support for, and violations to, Fitts' formulation of speed-accuracy relations. The first talk (Zelaznik) will outline behavioural mechanisms associated with speed-accuracy relations and provide recent evidence that individual differences in speed-accuracy relations are correlated. The second talk (Tremblay) will examine speed-accuracy relations across a range of IDs and outline whether Fitts' theorem provides a unitary or non-unitary basis to predict movement time for amplitude- and width-based manipulation to an aiming environment. The third talk (Heath) will outline speed-accuracy relations related to Fitts' theorem in the oculomotor system. Last, Digby Elliott will serve as Reactor and discuss the relative merits of ascribing Fitts' work as a law-based or conceptual framework in the movement sciences. The ultimate aim of this symposium is to generate debate regarding the relative merits of expressing Fitts' work as a law-based phenomenon in the movement sciences. Supported by NSERC (MH, LT, DE).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.035 |
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".