The Impact of Depth of Aesthetic Processing and Visual-Feature Transformations on Recognition Memory for Artworks and Constructed Design Patterns
Bibliographic record
Abstract
We conducted a study to examine how people perceptually encode and then recognize real artworks and constructed design patterns. We first manipulated depth of processing during an incidental perceptual encoding task (Phase 1) wherein participants made both affective/aesthetic and cognitive judgments. For painting stimuli, the contrast was between liking (yes/no) and a search for food in the paintings (present/absent). For design stimuli, the comparison was between liking and relative similarity of figure and ground in terms of color or texture. In Phase 2, we examined the effects of transforming visual features (i.e., color and texture) of the original stimuli on performance in a surprise recognition-memory task. Consistent with a depth-of-processing hypothesis, affective (i.e., liking) processing led to deeper perceptual encoding but, counter to our predictions, did not lead to better performance in the recognition-memory task. This benefit of aesthetic processing in the encoding phase was only observed with artworks but not with constructed design patterns that lacked salient semantic content. Moreover, texture transformations were discerned more accurately than color transformations across the different stimulus sets and tasks. This underscores the primacy of bottom-up processing of elementary stimulus features over top-down instructions to make affective judgments or search for semantic content.
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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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".