Toward Model Building for Visual Aesthetic Perception

Jianli Liu, Edwin Lughofer, Xianyi Zeng

Research output: Contribution to journalArticlepeer-review

Abstract

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 functionalmagnetic resonance imaging, magnetoencephalography, and electroencephalography. Theneural 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
Original languageEnglish
Article number1292801
Pages (from-to)1-13
Number of pages13
JournalComputational Intelligence and Neuroscience
Volume2017
DOIs
Publication statusPublished - 2017

Fields of science

  • 101 Mathematics
  • 101013 Mathematical logic
  • 101024 Probability theory
  • 102001 Artificial intelligence
  • 102003 Image processing
  • 102019 Machine learning
  • 603109 Logic
  • 202027 Mechatronics

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Mechatronics and Information Processing
  • Nano-, Bio- and Polymer-Systems: From Structure to Function

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