Fitts' index of difficulty: A useful theorem for speed-accuracy relations in the oculomotor system
Bibliographic record
Abstract
The present discussion will outline work examining whether ID-based speed-accuracy relations hold for goal-directed eye movements (i.e., saccades). Indeed, although the oculomotor literature has shown that amplitude-based ID changes elicit a robust increase in movement time (MT) it is largely unclear whether width-based ID changes similarly influence MT. This distinction represents an important test of Fitts' theorem and the assertion that movements yielding the same ID produce equivalent MTs regardless of the response's amplitude and width combination (i.e., unitary MT/ID relations). To that end, participants completed saccades in separate conditions that manipulated the amplitude and width characteristics of a target object. Importantly, the separate amplitude and width conditions were equated for ID (i.e., 3.34, 3.67, 4.06 and 4.61 bits of information) – a manipulation that provided a test of whether saccades are governed via unitary MT/ID relation. MTs for primary saccades across amplitude and width conditions increased as a function of increasing ID (R2=0.99 and 0.76); however, the slope of the MT/ID relation for the amplitude condition (18 ms: CI95%=5) was steeper than the width condition (3: CI95%=2). Further, aggregation of MTs for primary and secondary saccades did not enhance the explanatory power for MT/ID relations (R2=0.97 and 0.54). Thus, the present results demonstrate that, for saccades, the fixed parameter nature of Fitts' ID cannot be applied to a continuous range of veridical movement amplitudes and target widths.Acknowledgments: Supported by 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 | 0.012 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".