Creative expertise is associated with transcending the here and now.
Bibliographic record
Abstract
Human imagination is bounded. As situations become more distant in time, place, perspective, and likelihood, they also become more difficult to simulate. What underlies the ability to successfully engage in distal simulations? Here we examine the psychological and neural mechanisms underlying distal simulation by studying individuals known for transcending these limits: creative experts. First, 2 behavioral studies establish that creative experts indeed succeed at engaging in vivid distal simulations, compared to less creative individuals. Performance on a traditional measure of creativity (Study 1) and real-world success in creative pursuits (Study 2) corresponded with more vivid distal simulations across temporal, spatial, social, and hypothetical domains. Study 3 used neuroimaging to identify the neural mechanism supporting creative experts' simulation success. Whereas creative experts and controls recruit the same neural mechanism (the medial prefrontal cortex) while simulating common or proximal events, creative experts preferentially engage a distinct neural mechanism (the dorsomedial subsystem of the default network) while simulating distal events. Moreover, creative experts showed greater functional connectivity within this network at rest, suggesting they may be prepared to engage this mechanism, by default. Studying creative expertise provides new insight into the ability to mentally transcend the here and now. (PsycINFO Database Record (c) 2019 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".