人工和生物视觉中的整合处理预测了自然图像的感知美丽
Sanjeev Nara1, Daniel Kaiser1,2
1Mathematical Institute, Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig University Gießen, Gießen Germany.
Science advances
|March 1, 2024
概括
自然图像中的感知美感是由空间整合预测的,这个过程简化了图像分析. 在人工神经网络和人类大脑活动中观察到的这种计算原理增强了视觉感知,并调解了审美判断.
科学领域:
- 认知神经科学 认知神经科学
- 计算机视觉 计算机视觉
- 审美学的心理学
背景情况:
- 对自然图像的审美判断是在早期的感知处理过程中形成的.
- 美丽感知背后的特定感知计算在很大程度上是未知的.
研究的目的:
- 为了调查空间整合是否有效的视觉处理机制,预测自然图像的感知美丽.
- 探索将视觉感知与美学体验联系起来的计算原理.
主要方法:
- 使用深度神经网络模型量化空间集成,通过测量整个图像及其部分之间的激活差异.
- 在四项独立研究中,将模型的整合度与人类美丽评级相关联.
- 利用功能磁共振成像 (fMRI) 检查人类视觉皮层的整合性处理及其与美感的关系.
主要成果:
- 人工神经网络模型中的空间整合程度显著预测了自然图像的美丽评级.
- 与整合性处理相关的视觉皮层中的人类大脑活动也预测了感知到的美.
- 这些发现在不同的图像集和实验设计中是一致的.
结论:
- 空间整合作为一个关键的计算原理,促进视觉分析和美感的基础.
- 这项研究将计算建模和神经成像联系起来,通过高效的感知处理来解释美学体验.
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