Sequence effects during manual aiming: A departure from Fitts's Law?
Bibliographic record
Abstract
In 1954, Paul Fitts forwarded a formal account of the relationship between the difficulty of an aiming task and movement time associated with its completion. While most models used to explain this speed-accuracy tradeoff have been based upon visual feedback utilization and target-derived uncertainty, the idea that speed-accuracy constraints can also be dictated by previous aiming history has been largely ignored. In order to examine whether sequential movements are interdependent, we utilized a sequential-discrete aiming paradigm where the target changed difficulty mid-sequence, but between reaches. Individuals performed an adapted Fitts's task by performing discrete manual aiming movements between two equidistant targets from the midline. Responses were produced in sequences of 20 manual aiming movements separated by a fixed inter-trial-interval of 1 s. Four trial sequences were used in the experiment: in two of the sequences the target widths remained constant throughout trial (wide or narrow), in two sequences the target width changed (wide to narrow, narrow to wide) between the 7th to 12th movements of the sequence. Our main area of interest was in the trials immediately following the change in target width. Namely, we wanted to see if there were any carry-over effects from the preceding target width on the subsequent movements to a different target width. The extant sequential aiming literature suggests individuals plan several movements in advance during sequential movements, as compared to a single movement in isolation. In accord with this view, we demonstrated a gradual change in movement times and movement endpoint distributions following a switch in target width during a reciprocal aiming task. Importantly, this necessitates a transient departure from Fitts's law and highlights the role of visuomotor memory for the planning and execution of movements even in the presence of vision.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".