Productive and Re‐productive Thinking in Solving Insight Problems
Bibliographic record
Abstract
Abstract Many innovations in organizations result when people discover insightful solutions to problems. Insightful problem‐solving was considered by Gestalt psychologists to be associated with productive , as opposed to re‐productive , thinking. Productive thinking is characterized by shifts in perspective which allow the problem solver to consider new, sometimes transformational, approaches. Re‐productive thinking, on the other hand, involves the application of familiar, routine, procedures. This article reports a study which investigated how self‐reported productive and re‐productive thinking are related to an individual's ability to solve insight problems. Our measures were tested against the Kirton Adaption‐Innovation Inventory ( KAI ), and a battery of spatial insight problems. The results indicated that productive and re‐productive thinking and the KAI were successful in predicting performance on spatial insight problems. Furthermore, the measures of productive and re‐productive thinking accounted for spatial insight performance independently of scores on the KAI . In addition, the results suggested that re‐productive thinking consists of two different components—one based on group conventions and the other on personal experience. Each contributed differently to solving insight problems.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".