Visual Complexity and Affect: Ratings Reflect More Than Meets the Eye
Bibliographic record
Abstract
Pictorial stimuli can vary on many dimensions, several aspects of which are captured by the term 'visual complexity.' Visual complexity can be described as, "a picture of a few objects, colors, or structures would be less complex than a very colorful picture of many objects that is composed of several components." Prior studies have reported a relationship between affect and visual complexity, where complex pictures are rated as more pleasant and arousing. However, a relationship in the opposite direction, an effect of affect on visual complexity, is also possible; emotional arousal and valence are known to influence selective attention and visual processing. In a series of experiments, we found that ratings of visual complexity correlated with affective ratings, and independently also with computational measures of visual complexity. These computational measures did not correlate with affect, suggesting that complexity ratings are separately related to distinct factors. We investigated the relationship between affect and ratings of visual complexity, finding an 'arousal-complexity bias' to be a robust phenomenon. Moreover, we found this bias could be attenuated when explicitly indicated but did not correlate with inter-individual difference measures of affective processing, and was largely unrelated to cognitive and eyetracking measures. Taken together, the arousal-complexity bias seems to be caused by a relationship between arousal and visual processing as it has been described for the greater vividness of arousing pictures. The described arousal-complexity bias is also of relevance from an experimental perspective because visual complexity is often considered a variable to control for when using pictorial stimuli.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".