The role of vision in detecting and correcting fingertip force errors during object lifting
Bibliographic record
Abstract
Vision provides many reliable cues about the likely weight of an object, allowing individuals to predict how heavy it will be. The forces used to lift an object for the first time reflect these predictions. This, however, leads to inevitable errors during lifts of objects that weigh unexpected amounts. Fortunately, these errors are rarely made twice in a row-lifters have the impressive ability to detect and correct large or small misapplications of fingertip forces, even while experiencing weight illusions. Although it has been assumed that we detect and correct these errors exclusively with our sense of touch, recent evidence has demonstrated a role for vision in this fingertip force scaling. Here, we demonstrate that even when stimulus set size, delay, and modality are controlled for, individuals are unable to skillfully scale their grip and load force rates over repeated lifts without vision. However, eliminating only the task-relevant visual information, while maintaining the rest of the visual world, shifts participants back into the normal, skilled mode of control. These findings clarify the role of visual information in the ostensibly haptic task of lifting objects, suggesting individuals use priors under conditions where uncertainty is high.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".