Sanghyun Yi's research while affiliated with California Institute of Technology and other places
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Publications (4)
Little is known about how the brain computes the perceived aesthetic value of complex stimuli such as visual art. Here, we used computational methods in combination with functional neuroimaging to provide evidence that the aesthetic value of a visual stimulus is computed in a hierarchical manner via a weighted integration over both low and high lev...
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Here, we developed and tested a computational framework to investigate how aesthetic values are formed. We show that it is possible to explain human preferences for a visual art piece based on a mixture of low-...
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesth...
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesth...
Citations
... Yet, space is not the only dimension across which efficient integration can mediate perceived beauty. For example, a recent study used DNN models to quantify the integration of visual features across hierarchical levels to model aesthetic perception, and has in turn linked such hierarchical integration processes to parietal and frontal brain systems (Iigaya et al., 2023). Future studies could also link perceived beauty to integration across time: Recent studies in neuroaesthetics increasingly focus on more naturalistic and dynamic . ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/272789865234439-1442049580433_Q64/John-Odoherty-2.jpg)
... It can be seen that the difference between realistic and abstract images is noticeable. Iigaya's study (2021) [35] on natural human behavior introduced four image characteristics, including whether the image is abstract or realistic and exhibiting a preference for abstract art. Previous studies have explored that pleasure during viewing is first enhanced by arousal of factors such as image novelty, com-plexity, unfamiliarity, etc., but then decreases as arousal (especially complexity) becomes too strong [36,37]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/272746630348800-1442039272099_Q64/Koranis-Tanwisuth.jpg)
... Literature [22] rated the aesthetic stimulation of 14 painting elements on people, and found that people's overall feelings about painting should take priority over individual visual elements. Literature [8] found that human visual preference differences can be shared through the computational framework, and this difference is the human feeling of the whole painting. Literature [13] attempts to study human's objective feelings about abstract art and attempts to digitize this feeling. ...
... This was done by manual annotation, but it can also be done with a human detection algorithm (e.g., see ref. 96). We included this presence-of-a-person feature in the low-level feature set originally 97 , though we found in our DCNN analysis that the feature shows a signature of a high-level feature 97 . Therefore in this current study, we included this presence of a person to the high-level feature set. ...
