Energy and time trade-offs explain everyday human reaching movements
Bibliographic record
Abstract
Abstract Humans perform reaching movements with stereotypically smooth trajectories, attributed to neural optimality principles. Previous models focus on maximizing objectives like accuracy to explain smooth reaching under the speed-accuracy tradeoff, but none explain individuality. In everyday tasks such as reaching for a cup or pen, individuals self-select their own relaxed and idiosyncratic speeds that prioritize neither speed nor accuracy and appear to defy purely objective optimality. Here we propose an Energy-Time trade-off that better predicts the smoothness, speed, and individuality of everyday reaching. Energy refers to metabolic energy expenditure, and Time represents a weighted cost adjustable for individual and contextual factors. The balance between the two predicts human speed trajectories, durations, and peak speeds more accurately than prior models. Individuals move differently because they value time individualistically yet consistently. For example, one’s time valuation may be inferred from a single reach, and then applied to predict reaches of any distance by the same individual. Instructions to reach “faster” or “slower” also yield individualistic yet identifiable time valuations sufficient to predict other movements within each context. In fact, all movements across individuals and contexts align with a universal set of Energy-Time predictions. Energy economy objectively favors smoothness and slowness, whereas the time cost captures one’s individualistic preference to spend energy to save time.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".