Neuroimaging predictors of creativity in healthy adults
Bibliographic record
Abstract
Neuroimaging has revealed numerous neural predictors of individual differences in creativity; however, with most of these identified in only one study, sometimes involving very small samples, their reliability is uncertain. To contribute to a convergent cognitive neuroscience of creativity, we conducted a pre-registered conceptual replication and extension study in which we assessed previously reported predictors of creativity using a multimodal approach, incorporating volumetric, white matter, and functional connectivity neuroimaging data. We assessed sets of pre-registered predictors against prevailing measures of creativity, including visual and verbal tests of divergent thinking, everyday creative behaviour, and creative achievement. We then conducted whole-brain exploratory analyses. Greater creativity was broadly predicted by features of the inferior frontal gyrus (IFG) and inferior parietal lobe (IPL), including both local grey matter and white matter predictors in the IFG, the superior longitudinal fasciculus that connects them, and IFG-IPL functional connectivity. As IFG and IPL are important nodes within executive control and default mode networks (DMN), respectively, this result supports the view that executive modulation of DMN activity optimizes creative ideation. Furthermore, white matter integrity of the basal ganglia was also a generalizable creativity predictor, and exploratory analyses revealed the anterior lobe of the cerebellum and the parahippocampal gyrus to both be reliable predictors of creativity across neuroimaging modalities. This pattern aligns with proposals ascribing roles of working and long-term memory to problem-solving and imagination. Overall, our findings help to consolidate some, but not all, neural correlates of individual differences that have been discussed in the cognitive neuroimaging of creativity, yielding a subset that appear particularly promising for focused future investigation.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".