Iconic gestures during speaking and their relationship with aging and cognition
Bibliographic record
Abstract
Iconic gestures—movements that visually represent spoken content—support language and cognition, particularly under verbal demands. This study examined whether gesture production during procedural discourse varies with age and cognitive performance. Thirty-one adults (ages 30–80) described how to make a sandwich, do laundry, and plant a garden in a virtual testing environment. Iconic gestures were manually coded and normalized per 100 words of speech. Cognitive function was assessed using Montreal Cognitive Assessment (MoCA) subtests, Digit Span and Arithmetic from the Wechsler Adult Intelligence Scale-IV (WAIS-IV), and age-adjusted Working Memory Index (WMI) scores. Stepwise regression revealed that higher MoCA Delayed Recall scores predicted greater gesture use, while older age was associated with fewer gestures. A marginal age × delayed recall interaction suggested that older adults with poorer recall produced more gestures, consistent with a compensatory role for gesture. However, exploratory analysis of WMI classifications indicated that participants with Below Average working memory produced fewer gestures than those with Average or High Average scores. Whilst this was not a significant finding, this suggests that gesture production depends on sufficient working memory to coordinate speech and movement in real time. These findings support the Gesture as Simulated Action framework, which links gesture to sensorimotor simulations that facilitate communication. Results underscore the complex interplay between aging, memory, and gesture, suggesting that gestures may both reflect cognitive capacity and serve as sensitive behavioral markers of cognitive change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".