Playful brains: a possible neurobiological pathway to cognitive health in aging
Bibliographic record
Abstract
Healthy cognitive aging emphasizes preserving cognitive functions essential for independence and well-being. Developing interventions that promote cognition and resilience in older individuals is crucial. Social playfulness, characterized by spontaneity and mutual enjoyment, allows individuals to step away from routine roles and engage in novel and surprising exchanges. Emerging evidence suggests that social playfulness is a promising approach for supporting cognitive functions in aging in a joyful and engaging way. In this theory and hypothesis manuscript, we propose a neurobiological pathway mediating the effects of social playfulness on cognition. Playful interactions generate high levels of uncertainty, requiring continuous adaptation and exploration. We suggest that these demands engage the locus coeruleus-noradrenaline (LC-NA) system, which is crucial for navigating uncertainty and sustaining arousal and flexibility needed to adapt to the dynamic and unpredictable nature of playful interactions. Importantly, the collaborative and safe environment of playfulness transforms this uncertainty-driven noradrenergic activation into an engaging and rewarding experience, enhancing focus, positive affect, and flexibility. In older adults, where LC-NA functionality may decline with age, social playfulness could counteract cognitive decline by upregulating this system. We review evidence linking LC-NA integrity to cognitive health and explore how playfulness might mitigate the deterioration of cognitive functioning by training executive functions and promoting novelty and exploration. This framework bridges neuroscience, cognitive psychology, and creative-arts therapies, highlighting social playfulness as a tool for healthy aging. We emphasize the need for further research to validate this hypothesis and explore its implications for designing interventions that leverage social playfulness to enhance cognitive resilience in older populations.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".