Visual Product Evaluation: Using the Semantic Differential to Investigate the Influence of Basic Geometry on User Perception
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
Products evoke emotions in people. Emotions can influence purchase decisions and product evaluations. It is widely acknowledged that better product performance and higher user satisfaction can be reached through aesthetic design. However, when designing a new product, most of the attention is generally paid to enhance its functionality and usability and much less consideration is given to the emotional needs of users. This paper investigates the connection between emotions and product features. Various forms of vases are used as a product case. Additionally, a compact list of product-specific semantic descriptors is first developed using a classification based on Jordan’s four pleasures model. Paper-based surveys, face-to-face interviews, and statistical methods were performed in this paper, where significant correlations between semantic descriptors and product geometry were found. Prototypes of two vases were developed based on elicited emotions and a short validation on aesthetic value was performed. Our results show core set of geometric features of a vase have the strongest impact on emotional responses from users: the opening of the neck, the height of the neck, the base of the neck (width), and the base (width).
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) | 0.006 | 0.001 |
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