The relationship between methods of scoring the alternate uses task and the neural correlates of divergent thinking: Evidence from voxel-based morphometry
Bibliographic record
Abstract
Divergent thinking tests have been used extensively in neuroscientific studies of creativity. However, output from tests of divergent thinking can be scored in different ways, and those scores can influence assessments of divergent thinking performance and its relationship with brain activation. Here we sought to investigate the relationship between various methods of scoring the Alternate Uses Task (AUT)-a well-known test of divergent thinking-and regional grey matter volume (GMV) using voxel-based morphometry (VBM). We assessed AUT performance based on (a) traditional approaches that involve scoring participants' output on fluency, flexibility, originality, and elaboration, (b) a subjective approach that involves scoring output directly on "snapshot" creativity, and (c) the definitional approach that involves scoring output separately on novelty and usefulness-the two criteria deemed necessary and jointly sufficient to categorize an idea as creative. Correcting for age, sex, intracranial volume, verbal IQ and working memory capacity, we found negative correlations between regional GMV in the left inferior temporal gyrus (ITG) and novelty and usefulness scores, but no correlation involving other scoring approaches. As part of the brain's core semantic system, this region is involved in concept retrieval and integration. We discuss the implications of these findings for our understanding of the neural bases of divergent thinking, and how ITG could be related to the generation of novel and useful responses.
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How this classification was reachedexpand
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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".