Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science
Abstract The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two de...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-11-01
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| Series: | Brain Informatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40708-020-00118-w |
| _version_ | 1818458971109851136 |
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| author | Rui Li Junsong Zhang |
| author_facet | Rui Li Junsong Zhang |
| author_sort | Rui Li |
| collection | DOAJ |
| description | Abstract The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration. |
| first_indexed | 2024-12-14T23:06:56Z |
| format | Article |
| id | doaj.art-84b6538046694e92a278b118d1317adf |
| institution | Directory Open Access Journal |
| issn | 2198-4018 2198-4026 |
| language | English |
| last_indexed | 2024-12-14T23:06:56Z |
| publishDate | 2020-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj.art-84b6538046694e92a278b118d1317adf2022-12-21T22:44:17ZengSpringerOpenBrain Informatics2198-40182198-40262020-11-017111710.1186/s40708-020-00118-wReview of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer scienceRui Li0Junsong Zhang1National Engineering Laboratory for Educational Big Data, Central China Normal UniversityNational Engineering Laboratory for Educational Big Data, Central China Normal UniversityAbstract The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration.http://link.springer.com/article/10.1186/s40708-020-00118-wNeuroaestheticsComputational aestheticsComputational neuroaestheticsBrain functional connectivityMachine learning |
| spellingShingle | Rui Li Junsong Zhang Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science Brain Informatics Neuroaesthetics Computational aesthetics Computational neuroaesthetics Brain functional connectivity Machine learning |
| title | Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science |
| title_full | Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science |
| title_fullStr | Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science |
| title_full_unstemmed | Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science |
| title_short | Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science |
| title_sort | review of computational neuroaesthetics bridging the gap between neuroaesthetics and computer science |
| topic | Neuroaesthetics Computational aesthetics Computational neuroaesthetics Brain functional connectivity Machine learning |
| url | http://link.springer.com/article/10.1186/s40708-020-00118-w |
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