Implicit motor imagery: examining motor vs. visual strategies in laterality judgments among older adults
Bibliographic record
Abstract
Cognitive states like motor imagery (MI; simulating actions without overtly executing them) share a close correspondence with action execution, and hence, activate the motor system in a similar way. However, as people age, reduction in specific cognitive abilities like motor action simulation and action planning/prediction are commonly experienced. The present study examined the effect of visual-spatial processing for both typical and challenging upper-limb movements using the Hand Laterality Judgment Task (HLJT), in which participants were asked to judge whether the depicted hand is a left or right hand. Several main findings emerged: (1) Compared to younger adults, older adults exhibited slower responses and greater error rates in both Experiment 1 and 2. This suggests that visual-spatial transformations undergo alterations with age; (2) Older adults displayed higher error rates with realistic hands at both back and palm viewpoints of the hands compared to younger adults. However, this pattern did not hold for response times; (3) Participants responded faster to medial hand orientations (i.e., closer to the midline of the body) compared to lateral hand orientations (i.e., farther from the midline of the body) for palm-views in both Experiment 1 and Experiment 2. Given that we observed better performance on medial orientations compared to lateral orientations, this suggests that participants follow the same motor rules and biomechanical constraints of the represented movement. Novel information is provided about differences in individuals' use of strategies (visual vs. motor imagery) to solve the HLJT for both mannequin and real hands.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".