Limb-Target Control Increases With Effective Index of Difficulty
Bibliographic record
Abstract
Researchers have investigated the sensorimotor mechanisms that result in Fitts' law. One approach has been to analyse movement trajectories during Fitts' tasks to reveal the processes that occur during movement preparation and execution. We used trajectory analysis in the current study to investigate how limb-target control contributed to Fitts' law during the transition from ballistic movements to movements with online control. Twenty-five participants made discrete reaching movements in seven conditions with indexes of difficulty that ranged from one to seven. There were strong linear relationships between index of difficulty, effective index of difficulty and movement time. Trajectory analysis suggested that the easiest condition had movements that were mostly ballistic. There was enough time for visual-based online corrections, but the condition was probably too easy to require limb-target control. Trajectory analysis also suggested that there was an increased reliance on limb-target control as the index of difficulty increased. In conclusion, there was a strong linear relationship between effective index of difficulty and movement time even with conditions that ranged from mostly ballistic to a high degree of limb-target control. We suggest that there is a direct relationship between effective index of difficulty and degree of limb-target control.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".