Action Possibility Judgments of People with Varying Motor Abilities Due to Spinal Cord Injury
Bibliographic record
Abstract
Predictions about one's own action capabilities as well as the action capabilities of others are thought to be based on a simulation process involving linked perceptual and motor networks. Given the central role of motor experience in the formation of these networks, one's present motor capabilities are thought to be the basis of their perceptual judgments about actions. However, it remains unknown whether the ability to form these action possibility judgments is affected by performance related changes in the motor system. To determine if judgments of action capabilities are affected by long-term changes in one's own motor capabilities, participants with different degrees of upper-limb function due to their level (cervical vs. below cervical) of spinal cord injury (SCI) were tested on a perceptual-motor judgment task. Participants observed apparent motion videos of reciprocal aiming movements with varying levels of difficulty. For each movement, participants determined the shortest movement time (MT) at which they themselves and a young adult could perform the task while maintaining accuracy. Participants also performed the task. Analyses of MTs revealed that perceptual judgments for participant's own movement capabilities were consistent with their actual performance- people with cervical SCI had longer judged and actual MTs than people with below cervical SCI. However, there were no between-group differences in judged MTs for the young adult. Although it is unclear how the judgments were adjusted (altered simulation vs. threshold modification), the data reveal that people with different motor capabilities due to SCI are not completely biased by their present capabilities and can effectively adjust their judgments to estimate the actions of others.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".