The Wise Mind Balances the Abstract and the Concrete
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We explored how individuals’ mental representations of complex and uncertain situations impact their ability to reason wisely. To this end, we introduce situated methods to capture abstract and concrete mental representations and the switching between them when reflecting on social challenges. Using these methods, we evaluated the alignment of abstractness and concreteness with four integral facets of wisdom: intellectual humility, open-mindedness, perspective-taking, and compromise-seeking. Data from North American and UK participants (N = 1,151) revealed that both abstract and concrete construals significantly contribute to wise reasoning, even when controlling for a host of relevant covariates and potential response bias. Natural language processing of unstructured texts among high (top 25%) and low (bottom 25%) wisdom participants corroborated these results: semantic networks of the high wisdom group reveal greater use of both abstract and concrete themes compared to the low wisdom group. Finally, employing a repeated strategy-choice method as an additional measure, our findings demonstrated that individuals who showed a greater balance and switching between these construal types exhibited higher wisdom. Our findings advance understanding of individual differences in mental representations and how construals shape reasoning across contexts in everyday life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 it