Specificity and Abstraction of Examples: Opposite Effects on Fixation for Creative Ideation
Bibliographic record
Abstract
Abstract Fixation is one of the major obstacles that individuals face in creative idea generation contexts. Several studies have shown that individuals unintentionally tend to fixate to the examples they are shown in a creative ideation task, even when instructed to avoid them. Most of these studies used examples formulated with high level of specificity. However, no study has examined individuals’ creative performance under an instruction to diverge from given examples, when these examples are formulated with a high level of abstraction. In the present study, we show that (a) instructing participants to avoid using common examples when formulated with a high level of specificity increases fixation; whereas (b) instructing participants to avoid such examples while using a more abstract level for stating these common examples—such as a categorization of these examples—mitigates fixation and doubles the number of creative ideas generated. These findings give new insights on the key role of categorization in creative ideation contexts.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".