一个费舍尔信息理论的美学偏好复杂性
Sébastien Berquet1,2, Hassan Aleem3, Norberto M Grzywacz3,4,5
1Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA.
Entropy (Basel, Switzerland)
|November 27, 2024
概括
人们更喜欢中等复杂度的感官刺激,与观察到的费舍尔信息联系在一起. 这项研究模拟了图像复杂性分布,并证实了复杂性和偏好之间的反转-U关系.
科学领域:
- 认知科学 认知科学
- 信息理论 信息理论
- 计算机视觉 计算机视觉
背景情况:
- 人类对感官刺激的偏好往往表现出一种关于复杂性的反转U形.
- 已经提出了偏好和观察到的渔民信息 (OFI) 之间的理论联系.
- 需要一个环境复杂性的计算模型来充分发展这个理论.
研究的目的:
- 开发一个参数模型,用于复杂度分布在现实世界的图像.
- 调查图像复杂性和OFI之间的关系.
- 测试偏好与OFI直接相关的假设.
主要方法:
- 计算了来自七个环境图像中的复杂度指数的规范化的Shannon Entropy.
- 将参数模型与观察到的复杂性分布相匹配.
- 测量图像的OFI与配套模型的参数相比.
主要成果:
- 图像复杂性分布通常是单模式的,负倾斜的和leptokurtotic的.
- 罗吉特-洛塞夫函数有效地模拟了这些复杂性分布.
- OFI显示了与复杂性的反向-U关系,与偏好模式保持一致.
结论:
- 罗吉特-洛塞夫函数为图像复杂性提供了一个强大的模型.
- 观察到的费舍尔信息是一个可行的措施,将刺激复杂性与偏好联系起来.
- 这些发现支持基于费舍尔信息的感官偏好理论.
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