The influence of energy optimization on fast and accurate goal-directed aiming
Bibliographic record
Abstract
Goal-directed aiming movements are organized to be fast, accurate and energy optimal. Whereas the speed and accuracy components of these movements are constrained by the target width and amplitude (Fitts, 1954), energy optimization is strategic and used by performers to bias against errors associated with greater energy expenditure (Oliviera et al., 2005). In order to determine how energy and accuracy requirements jointly mediate the performance and kinematic characteristics of aiming movements, participants performed reciprocal aiming trials between sets of three target widths located either at the same level as, or at a 4cm distance above, the surface of a table. Because target misses in the latter would require a corrective movement against gravity, it was hypothesized that these movements would be both slower and described by higher trajectories than those to targets at table level. Results revealed that movement times were greater to the small targets when they were located above the surface of the table, a finding attributed to greater time spent after peak velocity. A decrease in proportional time to, and an increase in the trajectory height of, the primary sub-movement to the higher targets presumably allowed more space and time for an accurate approach to the target. The results are consistent with a Multiple Process of Model of limb control (Elliott et al., 2010), which suggests that the accuracy and energy requirements of a task shape how behaviour is organized.Acknowledgments: NSERC, Queen Elizabeth II Science & Technology, Anne Poucher Scholarship
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".