Categories of insight and their correlates: An exploration of relationships among classic‐type insight problems, rebus puzzles, remote associates and esoteric analogies
Bibliographic record
Abstract
A central question in creativity concerns how insightful ideas emerge. Anecdotal examples of insightful scientific and technical discoveries include Goodyear's discovery of the vulcanization of rubber, and Mendeleev's realization that there may be gaps as he tried to arrange the elements into the Periodic Table. Although most people would regard these discoveries as insightful, cognitive psychologists have had difficulty in agreeing on whether such ideas resulted from insights or from conventional problem solving processes. One area of wide agreement among psychologists is that insight involves a process of restructuring. If this view is correct, then understanding insight and its role in problem solving will depend on a better understanding of restructuring and the characteristics that describe it. This article proposes and tests a preliminary classification of insight problems based on several restructuring characteristics: the need to redefine spatial assumptions, the need to change defined forms, the degree of misdirection involved, the difficulty in visualizing a possible solution, the number of restructuring sequences in the problem, and the requirement for figure‐ground type reversals. A second purpose of the study was to compare performance on classic spatial insight problems with two types of verbal tests that may be related to insight, the Remote Associates Test (RAT), and rebus puzzles. In doing so, we report on the results of a survey of 172 business students at the University of Waikato in New Zealand who completed classic‐type insight, RAT and rebus 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 | 0.004 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".