TOWARDS A BETTER UNDERSTANDING OF THE INFLUENCE OF VISUAL REFERENCES ON CONSUMER AESTHETIC PERCEPTION
Bibliographic record
Abstract
Abstract When viewing a product for the first time, a consumer's aesthetic perception is based on their knowledge of other products, artefacts, and concepts. These mental images function as visual references for consumers and affect the processing fluency of the new product. Designers frequently use visual references as inspiration during the research stage of the design process. It has been documented, however, that there is a gap between designer intent and consumer response; Consumers do not always realize the intent of designers nor draw on the same visual references when perceiving a product, which can reduce their processing fluency of new products. Visual references differ from one consumer to the other which make them difficult to study. In this paper, we argue for a new way of studying visual references: by analyzing the cognitive process that occurs when consumers view a new product and recognize aspects of that product that are similar to visual references in their memory. We present a framework of three approaches for recognizing this similarity and implications for design practice.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".