Internal Dialogue During Divergent Thinking Predicts Originality—But in Unexpected Ways
Bibliographic record
Abstract
Internal dialogue may serve a key self-regulatory function in creative thinking. This study extends previous research by contrasting the contributions of trait-level internal dialogue—reflecting general tendencies to engage in dialogical activity—and state-level internal dialogue, reported retrospectively after completing divergent thinking tasks. Three hundred fifty adults were recruited online and completed two Alternate Uses Tests. Originality was assessed using both a uniqueness-based method and a validated artificial intelligence scoring technique. After accounting for individual differences in openness to experience, trait-level internal dialogue showed an inconsistent association with originality. In contrast, state-level internal dialogue consistently predicted originality, demonstrating predictive power beyond trait-level dialogue and openness to experience. Contrary to our hypothesis, however, this association was negative. Higher levels of internal dialogue during the creative task were related to lower originality. This unexpected pattern suggests that internal dialogue may not always support ideation. Alternative explanations for these findings, along with discrepancies with prior research, are examined to guide future studies into the potential role of internal dialogue in creative thinking.
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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 | 0.002 | 0.018 |
| 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) | 0.001 | 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".