Energy-Minimization Bias: Compensating for Intrinsic Influence of Energy-Minimization Mechanisms
Bibliographic record
Abstract
Anecdotal and scientific evidence suggest humans tend to undershoot targets in rapid movements. We investigated whether this undershoot bias derives from energy minimization mechanisms. Participants performed 200 trials of two tasks: (1) a simple slider push to a target, and (2) a modified version of (1), designed so overshooting was less energy consuming than undershooting. Results support that the undershoot bias found in (1), as well as the overshoot bias found in (2), results from an energy minimization mechanism. Energy minimization might be inherent to biological systems. Movement biases were un desirable for maximal performance. Nonetheless, participants presented biases despite financial incentives to perform maximally. Participants did, however, appear sensitive to systematic errors produced by the attraction to less energy costly responses. We suggest that the motor system is constrained such that maximal performance trades off with energetic optimality although humans are able to learn and compensate for the energy minimization biases.
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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 | 0.002 | 0.013 |
| 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) | 0.002 | 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".