Does Visual Fixation Affect Idea Fixation?
Bibliographic record
Abstract
Abstract Divergent thinking, an aspect of creativity, is often studied by measuring performance on the Alternative Uses Test (AUT). There is however a gap in creativity research concerning how visual stimuli on the AUT are most effectively perceived. Research in memory and attention have used eye-tracking studies to reveal insights into how people think and perceive visual stimuli. Thus, the current work uses eye tracking to study how eye movements are related to creativity. Participants orally listed alternative uses for twelve objects, each visually represented for two minutes in four different views. Using eye tracking, we specifically explored where and for how long people fixate their eyes at objects during the AUT. Eye movements before and while naming alternative uses are studied. Results revealed that naming new instances and categories of alternative uses correlates more strongly with visual fixation towards multiple views than towards a single view of the object. Alternative uses in new, previously unnamed categories are also more likely named following increased visual fixation towards blank space. These and other findings reveal the cognitive-thinking styles and eye-movement behaviors associated with finding new ideas. Such findings may be applied to reduce fixation to existing ideas during design.
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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 | 0.015 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".