类似人类面部的双面症 (pareidolia) 出现在深度神经网络中,这些神经网络被优化为面部和物体识别
Pranjul Gupta1, Katharina Dobs1,2
1Department of Experimental Psychology, Justus Liebig University Giessen, Giessen, Germany.
PLoS computational biology
|January 27, 2025
概括
面部pareidolia可能源于视觉系统优化识别面部和物体. 这表明我们的大脑使用通用对象分类来识别面孔,甚至在无生命的东西.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
背景情况:
- 人类的视觉系统在人脸识别方面表现出色,但其广泛调整的原因,导致面部pareidolia (对物体感知面部),是未知的.
- 了解面部双面症可能会为视觉感知和物体识别的基本原则提供见解.
研究的目的:
- 调查面部pareidolia是否来自视觉系统对识别面部和一般物体的优化.
- 使用任务优化的深卷积神经网络 (CNN) 建模人类 pareidolia 处理,并将其表示与人类神经和行为数据进行比较.
主要方法:
- 在面部和物体识别/检测任务的各种组合上训练有素的CNN.
- 使用表示相似性分析来比较CNN表示与人体磁脑电图 (MEG) 数据.
- 采用可解释性方法来检查CNN中对pareidolia分类的特征依赖.
主要成果:
- 经过对象分类训练的CNN更好地代表了pareidolia刺激,真实面孔和对象,类似于人类的神经反应.
- 特定的培训任务对CNN在网络层中代表 pareidolia 面孔的不同影响.
- 只有接受了面部识别和对象分类训练的CNN才使用类似面部的特征 (例如"眼睛") 来分类pareidolia刺激,模仿人类的感知.
结论:
- 人类面部像素可能是一个视觉系统的新兴属性,该视觉系统优化用于在更广泛的物体识别框架内识别面部.
- 一般化的对象分类可能在视觉系统在多样化和有时模两可的环境中检测面部的能力中起着至关重要的作用.
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