Mathematical Institute, Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig University Gießen, Gießen Germany.
Center for Mind, Brain and Behavior (CMBB), Philipps-University Marburg and Justus Liebig University Gießen, Marburg, Germany.
Sci Adv. 2024 Mar;10(9):eadi9294. doi: 10.1126/sciadv.adi9294. Epub 2024 Mar 1.
Previous research shows that the beauty of natural images is already determined during perceptual analysis. However, it is unclear which perceptual computations give rise to the perception of beauty. Here, we tested whether perceived beauty is predicted by spatial integration across an image, a perceptual computation that reduces processing demands by aggregating image parts into more efficient representations of the whole. We quantified integrative processing in an artificial deep neural network model, where the degree of integration was determined by the amount of deviation between activations for the whole image and its constituent parts. This quantification of integration predicted beauty ratings for natural images across four studies with different stimuli and designs. In a complementary functional magnetic resonance imaging study, we show that integrative processing in human visual cortex similarly predicts perceived beauty. Together, our results establish integration as a computational principle that facilitates perceptual analysis and thereby mediates the perception of beauty.
先前的研究表明，自然图像的美感在感知分析过程中就已经确定了。然而，目前尚不清楚是哪种感知计算产生了美感。在这里，我们测试了感知美是否可以通过跨图像的空间整合来预测，这是一种通过将图像部分聚合为整体的更有效的表示形式来减少处理需求的感知计算。我们在人工深度神经网络模型中量化了整合处理，其中整合程度由整个图像和其组成部分的激活之间的差异程度决定。这种整合的量化预测了来自四个具有不同刺激和设计的研究的自然图像的美感评分。在一项补充的功能磁共振成像研究中，我们表明人类视觉皮层中的整合处理同样可以预测感知美。总的来说，我们的研究结果确立了整合作为一种计算原则，它促进了感知分析，从而介导了美感的产生。
