Individual Differences in Global Cognition Modulate the Effect of Motor-Relatedness on Object Naming in Healthy Older Adults
Bibliographic record
Abstract
Lexical retrieval difficulty is a common daily complaint among older adults. Recent evidence suggests that older adults name motor-related nouns (e.g., knife) more accurately than non-motor nouns (e.g., steak). However, it remains unclear whether this motor-relatedness effect can reduce older adults' object naming latency (a potentially more sensitive measure of word retrieval than accuracy) and how it may be modulated by individual differences (e.g., age and global cognition). Therefore, we recruited a large number of older adults to complete a Chinese version of the Montreal Cognitive Assessment (MoCA) and a timed picture-naming task, and we explored the two remaining issues with data from 76 community-dwelling older adults (65-81 years old), excluding participants with possible AD. Linear mixed-effects analysis revealed a main effect of motor-relatedness on naming latency in older adults and a significant interaction with the MoCA score after controlling for a number of stimulus-related factors (i.e., age of acquisition, familiarity, name agreement, and visual complexity) and participant-related factors (i.e., gender and education) as covariates, but age showed neither a main effect nor a significant interaction with motor-relatedness. Further simple slope analysis showed that older adults were faster at naming objects with high motor-relatedness and that older adults with low MoCA scores benefited more from the motor-relatedness effect. These findings suggest that motor-relatedness may compensate for the normal course of cognitive ageing in older adults. Implications for the motor-relatedness effect were discussed.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".