Divergent and convergent creativity relate to different aspects of semantic control
Bibliographic record
Abstract
Abstract Past work has demonstrated a link between semantic memory and verbal creativity. Yet, few studies have considered this relationship through the lens of the controlled semantic cognition account, which anticipates that multimodal concepts in long-term memory interact with semantic control processes to generate goal and context-appropriate patterns of retrieval. In particular, while the creativity literature has distinguished divergent and convergent aspects of creativity, little is known about their relationship with separable aspects of semantic control, or the semantic intrinsic functional architecture of the brain. We investigated whether tasks with greater reliance on controlled semantic retrieval (assessed through weak association) versus semantic selection (assessed through semantic feature matching) were differentially linked to divergent creativity (assessed with the unusual uses task; UUT) and convergent creativity (assessed with the remote associates task; RAT). Better performance on the RAT was linked to semantic selection, while stronger performance on UUT was linked to more efficient retrieval of weak associations. We also examined individual differences in the intrinsic functional architecture of the semantic system using resting-state fMRI. Greater coupling between the anterior temporal lobe (multimodal semantic store) and left inferior frontal gyrus (LIFG) (in the semantic control network) was linked to stronger convergent creativity. This pathway also correlated with semantic feature matching performance, but not the retrieval of weak associations. In contrast, better divergent creativity was linked to greater coupling between LIFG and language-related auditory-motor regions, and decoupling from the default mode and frontoparietal networks. These connections correlated with the retrieval of weak associations. Interestingly, while decoupling of LIFG with default mode and frontoparietal networks correlated with the retrieval of weak associations, coupling of LIFG with these networks correlated with semantic feature matching. These behavioural and neurocognitive dissociations show that semantic control and creativity are highly related yet multifaceted constructs that depend on the underlying intrinsic architecture of key sites related to semantic cognition.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".