面部知觉的行为特征出现在深度神经网络中,这些神经网络被优化为面部识别
Katharina Dobs1,2,3,4, Joanne Yuan3, Julio Martinez3,5
1Department of Psychology, Justus Liebig University Giessen, Giessen 35394, Germany.
概括
深层卷积神经网络 (CNN) 揭示了人类面部识别"签名"来自任务优化,而不是天生的专业化. 这些发现表明,面部感知计算可能并不像以前认为的那样独特.
科学领域:
- 认知神经科学 认知神经科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 人类面部识别的特点是独特的行为"签名",如反转效应和专业知识效应.
- 这些现象在历史上支持了面部识别是一种专门的认知功能.
- 人类面部感知中这些专业性质的根本原因仍在争论中.
研究的目的:
- 测试假设人类面部识别签名是面部识别任务优化的结果.
- 调查这些特征是特定于面部还是任务优化的一般后果.
- 使用深度学习模型探索面部知觉的计算基础.
主要方法:
- 深层卷积神经网络 (CNN) 接受了面部识别和物体识别任务的训练.
- 评估CNN的行为特征与人类面部感知相似,包括反转效应.
- 美国有线电视新闻网 (CNN) 接受了汽车歧视的培训,以评估是否出现类似的签名用于非面部物体识别.
主要成果:
- 接受人脸识别训练的CNN显示了反转效应和专业效应等现象,反映了人类的表现.
- 经过一般物体识别训练的CNN没有显示这些特定的面部相关签名.
- 一个在汽车歧视方面受过训练的CNN也证明了汽车刺激的反转效应,这表明任务优化驱动了这些效应.
结论:
- 在人类面部感知中观察到的行为特征可能是优化面部识别任务的新兴特性.
- 这些发现挑战了面部识别依赖于独特的专业神经计算的概念.
- 任务优化,而不是固有的面部特定机制,可能解释人类面部感知的关键方面.
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