Perception and the Fitts's Law Violation: Why is the last one the fastest one?
Bibliographic record
Abstract
Fitts's Law (FL) quantifies the tradeoff between speed and accuracy for manual movements, and it predicts that movement time (MT) increases logarithmically as the movement amplitude (i.e., distance) increases when target width is held constant. Replicated hundreds of times over 50 years, FL has proven to be incredibly robust; however, a violation of this law has recently been discovered. When targets of a constant width are placed in a structured perceptual array (e.g., visible placeholders denoting potential target locations), MTs to targets in the last position of the array are much shorter than predicted by FL (often shorter than MTs to targets at the second-to-last position). This violation holds for manual, saccadic, and even imagined, movements. Although it is known that the violation occurs in the movement planning stage, the underlying driving mechanism remains unknown. In the current study, we conducted three experiments to determine if the violation has a perceptual cause. In the first experiment, by measuring MTs to locations demarcated by extremely diminished placeholders (3 pixels long), we show that the violation does not occur due to perceptual interference.Experiment 2, which measured reaction times using a target detection task, showed that subjects are no faster in detecting targets appearing in the last location than they are detecting targets appearing at the other positions. Experiment 3, which measured accuracy using a brief presentation target identification task, showed that targets presented at the last position in the array are identified equally accurately in both placeholder present and absent conditions. Overall, these findings indicate that the changes in effectiveness of visual processing at the last position in the perceptual array do not drive the FL violation. Thus, while the locus of the FL violation appears to be in the movement planning stage, it is not due to perceptual mechanisms.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".