Learning to Optimize Speed, Accuracy, and Energy Expenditure: A Framework for Understanding Speed-Accuracy Relations in Goal-Directed Aiming
Why this work is in the frame
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Bibliographic record
Abstract
Over the last century, investigators have developed a number of models to explain the relation between speed and accuracy in target-directed manual aiming. The models vary in the extent to which they stress the importance of feedforward processes and the online use of sensory information (see D. Elliott, W. F. Helsen, & R. Chua, 2001, for a recent review). A common feature of those models is that the role of practice in optimizing speed, accuracy, and energy expenditure in goal-directed aiming is either ignored or minimized. The authors present a theoretical framework for understanding speed-accuracy tradeoffs that takes into account the strategic, trial-to-trial behavior of the performer. The strategic behavior enables individuals to maximize movement speed while minimizing error and energy expenditure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it