Aesthetic Judgments, Movement Perception and the Neural Architecture of the Visual System
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We have developed a deep learning-based AI creativity system, which can be used to create computer-generated artworks, in the form of still images as well as time-based pieces (videos). Within the scope of this article, we will briefly describe our system and will then demonstrate its application in a psychological study on aesthetic experiences. We also propose a new hypothesis regarding a potential interaction between the neural architecture of the two visual pathways, and the effect of movement perception on the formation of aesthetic judgments. Specifically, we postulate that perceived movement within the visual scene engages reflexive attention, an attentional focus shift towards the processing of visual changes, and subsequently affects how information are relayed via the dorsal and ventral streams. We outline a recent pilot study in support of our proposed framework, which serves as the first study that investigates the relationship between the two visual streams and aesthetic experiences. Our study demonstrated evidence for our hypothesis, with time-based artworks showing higher aesthetic appeal at slower playback speeds.
KeywordsNeuroscience Brain simulation Artificial intelligence Deep learning Visual pathways Neural pathways Neuro-architecture Aesthetics
We acknowledge our colleague Graeme McCaig who was instrumental in creating our modified Deep Dream system and was invaluable in his mentorship of the work. This work was partially supported by SSHRC and NSERC grants respectively.
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