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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".