Wisdom reconsidered: A dynamic network account of metacognition and complex thought.
Bibliographic record
Abstract
= 499), using event-reconstruction sampling to gather autobiographical reflections on adverse experiences. Participants evaluated their use of four core metacognitive features of wisdom: intellectual humility, recognition of uncertainty and change, perspective taking, and search for a compromise. Our findings challenge prevailing static models and supported a dynamic, context-sensitive account. A network model outperformed latent factor models, suggesting that wisdom comprises interrelated but distinct features rather than a unitary construct. The perceived relevance and use of these features varied across situations and showed lower temporal stability than personality traits or well-being, undermining assumptions of cross-situational consistency. Within-person and between-person patterns also diverged, violating isomorphism. Notably, individuals who reported higher-than-usual self-distancing and distress at one time point also reported elevated levels of wisdom-related features 3 months later, a pattern not observed for other proposed moderators such as social support or subjective appraisals. Together, these findings offer a revised understanding of wisdom and complex thought-as dynamic, context-sensitive processes, rather than fixed traits. Our findings carry implications for the ontological status of wisdom-related constructs and underscore the importance of longitudinal research and more precise temporal claims in psychological science. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".