Literature DB >> 27913105

The colors of paintings and viewers' preferences.

Sérgio M C Nascimento1, João M M Linhares2, Cristina Montagner2, Catarina A R João3, Kinjiro Amano4, Catarina Alfaro5, Ana Bailão6.   

Abstract

One hypothesis to explain the aesthetics of paintings is that it depends on the extent to which they mimic natural image statistics. In fact, paintings and natural scenes share several statistical image regularities but the colors of paintings seem generally more biased towards red than natural scenes. Is the particular option for colors in each painting, even if less naturalistic, critical for perceived beauty? Here we show that it is. In the experiments, 50 naïve observers, unfamiliar with the 10 paintings tested, could rotate the color gamut of the paintings and select the one producing the best subjective impression. The distributions of angles obtained are described by normal distributions with maxima deviating, on average, only 7 degrees from the original gamut orientation and full width at half maximum just above the threshold to perceive a chromatic change in the paintings. Crucially, for data pooled across observers and abstract paintings the maximum of the distribution was at zero degrees, i.e., the same as the original. This demonstrates that artists know what chromatic compositions match viewers' preferences and that the option for less naturalistic colors does not constrain the aesthetic value of paintings.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Keywords:  Aesthetic preference; Art statistics; Color vision; Colors of paintings; Visual arts

Mesh:

Year:  2016        PMID: 27913105     DOI: 10.1016/j.visres.2016.11.006

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  9 in total

1.  Kandinsky or Me? How Free Is the Eye of the Beholder in Abstract Art?

Authors:  Doris I Braun; Katja Doerschner
Journal:  Iperception       Date:  2019-09-04

2.  Global Image Properties Predict Ratings of Affective Pictures.

Authors:  Christoph Redies; Maria Grebenkina; Mahdi Mohseni; Ali Kaduhm; Christian Dobel
Journal:  Front Psychol       Date:  2020-05-12

3.  Aesthetic Image Statistics Vary with Artistic Genre.

Authors:  George Mather
Journal:  Vision (Basel)       Date:  2020-02-01

4.  Aesthetic Evaluation of Digitally Reproduced Art Images.

Authors:  Claire Reymond; Matthew Pelowski; Klaus Opwis; Tapio Takala; Elisa D Mekler
Journal:  Front Psychol       Date:  2020-12-11

5.  Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts.

Authors:  Mahdi Mohseni; Volker Gast; Christoph Redies
Journal:  Front Psychol       Date:  2021-03-31

Review 6.  Swipes and Saves: A Taxonomy of Factors Influencing Aesthetic Assessments and Perceived Beauty of Mobile Phone Photographs.

Authors:  Helmut Leder; Jussi Hakala; Veli-Tapani Peltoketo; Christian Valuch; Matthew Pelowski
Journal:  Front Psychol       Date:  2022-02-28

7.  Regularity of colour statistics in explaining colour composition preferences in art paintings.

Authors:  Shigeki Nakauchi; Hideki Tamura
Journal:  Sci Rep       Date:  2022-08-26       Impact factor: 4.996

8.  Pupillary response to representations of light in paintings.

Authors:  Serena Castellotti; Martina Conti; Claudia Feitosa-Santana; Maria Michela Del Viva
Journal:  J Vis       Date:  2020-10-01       Impact factor: 2.240

9.  Universality and superiority in preference for chromatic composition of art paintings.

Authors:  Shigeki Nakauchi; Taisei Kondo; Yuya Kinzuka; Yuma Taniyama; Hideki Tamura; Hiroshi Higashi; Kyoko Hine; Tetsuto Minami; João M M Linhares; Sérgio M C Nascimento
Journal:  Sci Rep       Date:  2022-03-11       Impact factor: 4.379

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.