Worst-Case Prediction Strategy in Force Programming When Visual Information is Obstructed
Bibliographic record
Abstract
A person's strategy for applying force while lifting an object is dependent upon visual cues. This study investigated the alteration of strategy in force programming when visual information about an object's size was obstructed at the moment of lifting. Seven subjects were instructed to use a precision grip for repeated lifts of a cube-like grip apparatus attached to a box. The grip apparatus was a special device designed to measure grip and load forces. Three different-sized plastic boxes of equal weight were pseudorandomly presented by attaching them beneath the grip apparatus to the subjects in two visual conditions. In the Full-vision condition, subjects could view the box's size prior to lifting. In the Obstructed-vision condition, a screen prevented subjects from seeing the box size prior to lifting. In the Full-vision condition, the grip force and load force used by subjects on the grip apparatus increased with box size. In contrast, the subjects in the Obstructed-vision condition used forces appropriate for the largest box regardless of box size. The present results suggest that absence of size information may cause an alteration of strategy used to determine force output in that subjects may apply a maximum force adequate for the largest box, which could be called a "worst-case" prediction strategy, i.e., when there is doubt, the most secure lift may be selected for all possible cases.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".