Using a Drift Diffusion Model to Validate the Quantification of Style Prototypicality as Assessed by the Viewers of Paintings
Bibliographic Data
| ID | 10276773 |
|---|---|
| Authors | Shigen Fang Ogata (0000-0003-1497-7253, Shigen Fang Ogata, Global Education Center, Waseda University; Graduate School of Education, Hyogo University of Teacher Education, 942-1 Shimokume, Kato-shi, Hyogo, Japan. Email: [email protected]., corresponding author), Yoshimasa Tawatsuji (0009-0007-4554-7367, Yoshimasa Tawatsuji, Global Education Center, Waseda University; Center for Data Science, Waseda University, 1-6-1 Nishiwaseda, Tokyo, Japan. Email: [email protected]., corresponding author), Tatsunori Matsui (0000-0001-9508-7943, Tatsunori Matsui, Faculty of Human Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, Japan. Email: [email protected]., corresponding author) |
| Year | 2024 |
| Volume | 57 |
| Issue | 1 |
| Pages | 70-78 |
| Publication date | 2024-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Leonardo (JOURNAL) |
| Journal identifiers | ISSN: 0024-094X • E-ISSN: 1530-9282 |
| Publisher | MIT Press (PUBLISHER • US) |
| DOI | 10.1162/leon_a_02433 |
| OpenAlex | W4385481011 |
| Language | EN |
| References cited | 14 |
When appreciating a painting, people often classify it into a style. The extent to which the painting is regarded as a typical example of the style is called its style prototypicality. The authors propose a method of quantifying style prototypicalities and conduct an experiment to validate this method using the drift rate parameter of the drift diffusion model. This parameter is calculated using participants’ decision-making response times. The authors find a positive correlation (r = .88, p < .001) between the drift rates and the quantified prototypicalities for the paintings used in this study, confirming the psychological appropriateness of the proposed quantification method
Art · Diffusion · Painting · Physics · Style (visual arts) · Thermodynamics · Visual arts · Aesthetic Perception and Analysis · Art History and Market Analysis · Artificial Intelligence · Color perception and design · Computer Science · Psychology
| Citation velocity | historical |
|---|---|
| Highly cited | No |