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Updated: Jun 16, 2026
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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
Published on: December 15, 2023
Tracing aesthetic experience from perception and conception to appraisal using deep convolutional neural networks
Yi Lin1, Lukas Verlinde1, Johan Wagemans1
1Brain and Cognition, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
Iscience
|June 15, 2026
Summary
Deep convolutional neural networks (DCNNs) reveal how the brain processes aesthetic experiences. Early DCNN layers predict perceptual fluency, while deeper layers capture conceptual information influencing liking, offering a unified framework for aesthetic appreciation.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
- Psychology of Aesthetics
Background:
- Aesthetic experience involves multiple processing stages, but unified theoretical models are lacking.
- Current models do not fully integrate perceptual and conceptual aspects of aesthetic appreciation.
Purpose of the Study:
- To develop a unified computational framework for understanding aesthetic experience.
- To investigate the neural mechanisms underlying perceptual fluency and liking using deep convolutional neural networks (DCNNs).
Main Methods:
- Utilized human-aligned DCNNs to model visual processing stages.
- Developed an integration metric based on representational complexity in DCNNs.
- Applied ridge regression to layer-wise DCNN activations to predict aesthetic judgments.
Main Results:
- Reduced representational complexity in early DCNN layers strongly predicted perceptual fluency for paintings.
- Distinct DCNN layers showed stage-specific mechanisms for processing fluency-related features.
- Deeper DCNN layers captured higher-level conceptual information (e.g., style), improving predictions of liking.
- A partial dissociation was observed between perceptual fluency and liking.
Conclusions:
- Layer-resolved DCNN modeling provides a principled framework for dissecting aesthetic appreciation.
- The findings highlight distinct computational mechanisms for perceptual fluency and liking.
- DCNNs offer a valuable tool for bridging computational and psychological approaches to aesthetics.
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