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      Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model

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          Abstract

          Several automated aesthetic assessment models were developed to assist artists in producing artwork with high aesthetic appeal. However, most of them focused on static visual art, such as photographs and paintings, and evaluations of dynamic art received less attention. Dynamic visual art, especially computer-based art, includes diverse forms of artistic expression and can enhance an audience’s aesthetic experience. A model for evaluating dynamic visual art can provide valuable feedback and guidance for improving artistic skills and creativity, thereby benefiting audiences. In this study, we created eight generative artworks with dynamic art forms based on a commonly used method. We established a time-dependent model to predict the aesthetics based on visual features. We quantified the artworks according to selected image features and found that these features could effectively capture the characteristics of the changing visual forms during the generation process. To explore the effects of time-varying features on aesthetic appeal, we built a panel regression model and found that the aesthetic appeal of the generated artworks was significantly affected by their skewness of the luminance distribution, vertical symmetry, and mean hue value. Furthermore, our study demonstrated that the aesthetic appeal of dynamic generative artworks could be predicted by integrating image features into the temporal domain.

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          Most cited references53

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          Novelty, complexity, and hedonic value

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            The evolutionary psychology of facial beauty.

            What makes a face attractive and why do we have the preferences we do? Emergence of preferences early in development and cross-cultural agreement on attractiveness challenge a long-held view that our preferences reflect arbitrary standards of beauty set by cultures. Averageness, symmetry, and sexual dimorphism are good candidates for biologically based standards of beauty. A critical review and meta-analyses indicate that all three are attractive in both male and female faces and across cultures. Theorists have proposed that face preferences may be adaptations for mate choice because attractive traits signal important aspects of mate quality, such as health. Others have argued that they may simply be by-products of the way brains process information. Although often presented as alternatives, I argue that both kinds of selection pressures may have shaped our perceptions of facial beauty.
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              Natural image statistics and neural representation.

              It has long been assumed that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical properties of the signals to which they are exposed. Attneave (1954)Barlow (1961) proposed that information theory could provide a link between environmental statistics and neural responses through the concept of coding efficiency. Recent developments in statistical modeling, along with powerful computational tools, have enabled researchers to study more sophisticated statistical models for visual images, to validate these models empirically against large sets of data, and to begin experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2023
                21 September 2023
                : 18
                : 9
                : e0291647
                Affiliations
                [001] School of Design, Shanghai Jiao Tong University, Shanghai, China
                Federal University of Paraiba, BRAZIL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0009-0002-5298-9749
                https://orcid.org/0000-0002-7219-9237
                https://orcid.org/0009-0004-8130-7908
                Article
                PONE-D-23-09019
                10.1371/journal.pone.0291647
                10513343
                37733653
                15a7b71a-1c83-4a0c-81c5-f65a75630868
                © 2023 Meng et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 April 2023
                : 4 September 2023
                Page count
                Figures: 7, Tables: 3, Pages: 22
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Psychology
                Psychological Attitudes
                Social Sciences
                Psychology
                Psychological Attitudes
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Skewness
                Research and Analysis Methods
                Imaging Techniques
                Physical Sciences
                Physics
                Condensed Matter Physics
                Anisotropy
                Physical Sciences
                Materials Science
                Material Properties
                Anisotropy
                Physical Sciences
                Physics
                Electromagnetic Radiation
                Light
                Visible Light
                Luminance
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Perception
                Sensory Perception
                Vision
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Perception
                Sensory Perception
                Vision
                Social Sciences
                Psychology
                Cognitive Psychology
                Perception
                Sensory Perception
                Vision
                Biology and Life Sciences
                Neuroscience
                Sensory Perception
                Vision
                Physical Sciences
                Physics
                Electromagnetic Radiation
                Light
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Perception
                Sensory Perception
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Perception
                Sensory Perception
                Social Sciences
                Psychology
                Cognitive Psychology
                Perception
                Sensory Perception
                Biology and Life Sciences
                Neuroscience
                Sensory Perception
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                All relevant data are within the paper and its Supporting information files.

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