The action possibilities judgments of people with varying motor abilities due to spinal cord injury
Bibliographic record
Abstract
Previous research has revealed that individuals can make estimates of their own and other people’s movement capabilities. These judgments are thought to be based on a simulation of one’s own capabilities through the activation of ideomotor (bound perception/action) codes. To form judgments about others, the simulation likely remains intact while the threshold for what is deemed “possible” is modified. It is not known, however, if limited motor capacities alter these processes. In the present study, participants with different degrees of upper limb function, due to spinal cord injury (cervical vs. thoracic or lower SCI), were recruited to determine if their judgments of another person’s capabilities are biased by their own motor capabilities. Participants observed apparent motion videos of a reciprocal tapping task with varying index of difficulties. They were asked to determine the shortest movement time (MT) at which they and a young adult male could maintain endpoint accuracy. Participants also performed the task. Analyses of the MTs for the judgments of their own movement capabilities were consistent with those of their performance - people with cervical SCI had longer MTs than their peers with lower SCI. In contrast, there were no between-group differences for judgments of the young adult. Although it is unclear how the judgments were adjusted (simulation vs. threshold modification), the data reveal that people with different motor capabilities due to SCI are not biased by their own movement capabilities and can effectively adjust their judgments to estimate the actions of others.Acknowledgments: Natural Sciences and Engineering Research Council of Canada, Ontario Ministry of Research and Innovation
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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 | 0.009 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".