Toward Model Building for Visual Aesthetic Perception

Several models of visual aesthetic perception have been proposed in recent years. Such models have drawn on investigations into the neural underpinnings of visual aesthetics, utilizing neurophysiological techniques and brain imaging techniques including functional magnetic resonance imaging, magnetoencephalography, and electroencephalography. The neural mechanisms underlying the aesthetic perception of the visual arts have been explained from the perspectives of neuropsychology, brain and cognitive science, informatics, and statistics. Although corresponding models have been constructed, the majority of these models contain elements that are difficult to be simulated or quantified using simple mathematical functions. In this review, we discuss the hypotheses, conceptions, and structures of six typical models for human aesthetic appreciation in the visual domain: the neuropsychological, information processing, mirror, quartet, and two hierarchical feed-forward layered models. Additionally, the neural foundation of aesthetic perception, appreciation, or judgement for each model is summarized. The development of a unified framework for the neurobiological mechanisms underlying the aesthetic perception of visual art and the validation of this framework via mathematical simulation is an interesting challenge in neuroaesthetics research. This review aims to provide information regarding the most promising proposals for bridging the gap between visual information processing and brain activity involved in aesthetic appreciation.

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Bringing It All Together

10.1093/oso/9780197513620.003.0002 ◽

2021 ◽

pp. 8-12

Author(s):

Marcos Nadal ◽

Camilo J. Cela-Conde

Keyword(s):

Attentional Control ◽

Brain Activity ◽

Aesthetic Appreciation ◽

Second Stage ◽

Control Signals ◽

Affective Processes ◽

Personal Experiences ◽

Neural Underpinnings ◽

Two Stages ◽

Interpretative Model

The main goal of the article “The Neural Foundations of Aesthetic Appreciation” was to bring together the available evidence on the neural underpinnings of aesthetics from neuroimaging and neurology and offer an integral interpretative model. The authors relate how they wanted to explain how aesthetic appreciation was related to brain activity and why some studies had found that activity in some regions and other studies had found it in other regions. The authors proposed that there might be at least two stages of appreciation. The first stage is the formation of an initial impression. It involves perceptual processes interacting with attentional control signals and is mediated by a fronto-parieto-occipital network. The second stage is a deeper evaluation of the image and involves affective processes, searching for meaning, recalling personal experiences, and activating knowledge stored in memory.

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Implicit Measures in the Aesthetic Domain

The Oxford Handbook of Empirical Aesthetics ◽

10.1093/oxfordhb/9780198824350.013.11 ◽

2019 ◽

Author(s):

Letizia Palumbo

Keyword(s):

Information Processing ◽

Aesthetic Experience ◽

Implicit Measures ◽

Aesthetic Appreciation ◽

Implicit Processes ◽

The Aesthetic

The current chapter is concerned with implicit levels of information processing underlying hedonic responses. Commencing with an overview of the processes involved in the formation of the aesthetic experience, the discourse will focus on models that ascribe a role to the implicit dimension, a role that is marginal but necessary. We will see how a range of experimental procedures have contributed to measure automatic components of the aesthetic appreciation. The aim is to assign a specific place to the study of implicit processes within the broader debate on aesthetic experience. Ultimately, this will lead to some general remarks for the discipline of empirical aesthetics.

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The Power of Visual Texture in Aesthetic Perception: An Exploration of the Predictability of Perceived Aesthetic Emotions

Computational Intelligence and Neuroscience ◽

10.1155/2018/1812980 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8

Author(s):

Jianli Liu ◽

Edwin Lughofer ◽

Xianyi Zeng ◽

Zhengxin Li

Keyword(s):

Model Building ◽

Component Analysis ◽

Statistical Characteristics ◽

Selection Strategy ◽

Aesthetic Perception ◽

Visual Texture ◽

Information Redundancy ◽

Low Level ◽

The Aesthetic ◽

And Control

How to interpret the relationship between the low-level features, such as some statistical characteristics of color and texture, and the high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. Contrary to the black-box method widely used in the fields of machine learning and pattern recognition, we build a white-box model with the hierarchical feed-forward structure inspired by neurobiological mechanisms underlying the aesthetic perception of visual art. In the experiment, the aesthetic judgments for 8 pairs of aesthetic antonyms are carried out for a set of 151 visual textures. For each visual texture, 106 low-level features are extracted. Then, ten more useful and effective features are selected through neighborhood component analysis to reduce information redundancy and control the complexity of the model. Finally, model building of the beauty appreciation of visual textures using multiple linear or nonlinear regression methods is detailed. Compared with our previous work, a more robust feature selection algorithm, neighborhood component analysis, is used to reduce information redundancy and control computation complexity of the model. Some nonlinear models are also adopted and achieved higher prediction accuracy when compared with the previous linear models. Additionally, the selection strategy of aesthetic antonyms and the selection standards of the core set of them are also explained. This research also suggests that the aesthetic perception and appreciation of visual textures can be predictable based on the computed low-level features.

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Evaluation and Analysis of White Space in Wu Guanzhong’s Chinese Paintings

Leonardo ◽

10.1162/leon_a_01409 ◽

2019 ◽

Vol 52 (2) ◽

pp. 111-116 ◽

Cited By ~ 1

Author(s):

ZhenBao Fan ◽

Kang Zhang ◽

XianJun Sam Zheng

Keyword(s):

Information Processing ◽

Eye Tracking ◽

Visual Information ◽

Aesthetic Experience ◽

Visual Information Processing ◽

Aesthetic Perception ◽

White Space ◽

Saliency Model ◽

Background Space

This article reports on a recent study that examines the effect of white space on perception of Chinese paintings. The authors investigate whether white space in Chinese paintings is not simply a blank background space but rather meaningful for aesthetic perception. Applying a computational saliency model to analyze the influence of white space on viewers’ visual information processing, the authors conducted an eye-tracking experiment. As a case study, they analyzed paintings by a well-known artist, Wu Guanzhong, and collected users’ subjective aesthetic ratings. Their results show that white space is not just a silent background: It is intentionally designed to convey certain information and has a significant effect on viewers’ aesthetic experience.

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