Creative, internally-directed cognition is associated with reduced BOLD variability
Bibliographic record
Abstract
In a range of externally-directed tasks, intra-individual variability of fMRI BOLD signal has been shown to be a stronger predictor of cognitive performance than mean BOLD signal. BOLD variability's strong association with cognitive performance is hypothesised to be due to it capturing the dynamic range of neural systems. Although increased BOLD variability is also speculated to play a role in internally-directed thought, particularly when creative and flexible cognition is required, there is a relative lack of research exploring whether BOLD variability is related to internally-directed cognition. Thus, we investigated the relationship between BOLD variability and a key component of creativity - divergent thinking - in various tasks that required participants to think flexibly. We also determined whether any associations between BOLD variability and creativity overlapped with, or differed, from associations between mean BOLD signal and creativity. First, we performed task Partial Least Squares (PLS) analyses that compared BOLD signal (either mean or variability) during two future imagination conditions that differed in the amount of cognitive flexibility required: a Congruent condition in which autobiographical details (people, places, objects) comprising an imagined event belonged to the same social sphere (e.g., university) and an Incongruent condition in which details belonged to different social spheres and required greater cognitive flexibility to integrate. Results indicated that the Incongruent condition was associated with a widespread reduction in both BOLD variability and mean signal (relative to the Congruent condition), but in largely non-overlapping regions. Next, we used behavioral PLS to determine whether individual differences in performance on future simulation tasks as well as the Alternate Uses Task relates to BOLD variability and mean BOLD signal. Better performance on these tasks was predominantly associated with increases in mean BOLD signal and decreases in BOLD variability, in a range of disparate brain regions. Together, the results suggest that, unlike tasks requiring externally-directed cognition, superior performance on tasks requiring creative internal mentation is associated with less (not more) variability.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".