In a pinch: Are pinch forces mediated by vision of the task hand?
Bibliographic record
Abstract
The absence of visual feedback leads to inaccurate representations of one's self-produced force, through an "overcompensation" effect wherein central predictive mechanisms related to reafference result in self-generated forces being perceived as weaker than they are (Therrien et al., 2010; 2012; 2013). These findings hold significant safety implications in situations where repetitive force productions are a requirement of a work environment (e.g., assembly lines). Thus, the goal of this study was to explore this force salience effect in an applied setting to determine if full vision (FV), no vision (NV), or augmented vision (AV) in a motor task involving pinch grips would result in differential force productions. We hypothesized that, consistent with Therrien et al, performing such a task with FV would lead to the lowest pinch grip forces, while NV of the task would lead to compensatory force production. Furthermore, we introduced the concept of AV, through a closed-circuit camera, to determine whether this overcompensation could be mediated by means other than direct visual perception. Twelve participants used a pinch grip to complete a buckle-fastening task in 2 force directions (down and forward) and 3 vision conditions (FV, NV, AV). Impulse measures supported our hypothesis with AV and NV showing a 34.9% and 59.0% increase from FV. Results for the primary variable of interest however, did not (i.e., FV resulted in pinch forces that were not different from NV). Results are discussed in the context of attentional distribution, multi-digit manipulation and task type in the attenuation of self-produced force feedback.
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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 | 0.004 |
| 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 | 0.000 |
| 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".