Specificity and Abstraction of Examples: Opposite Effects on Fixation for Creative Ideation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it