在prosopagnosia中的神经计算
Simon Faghel-Soubeyrand1,2, Anne-Raphaelle Richoz3, Delphine Waeber3
1Département de psychologie, Université de Montréal, 90 av. Vincent D'indy, Montreal, H2V 2S9, Canada.
Cerebral cortex (New York, N.Y. : 1991)
|May 25, 2024
概括
患者PS,获得的prosopagnosia,显示非典型的早期视觉处理和计算缺陷. 她的大脑活动类似于早期的深度神经网络层,与显示后期层相似的对照不同.
科学领域:
- 认知神经科学 认知神经科学
- 神经心理学 神经心理学
- 计算神经科学是一种神经科学.
背景情况:
- 获得的前性视觉障碍 (Acquired prosopagnosia) 是一种表现为无法识别人脸的疾病.
- 腹部流对于对象和面部识别至关重要.
- 了解prosopagnosia的神经基础可以阐明典型的面部处理机制.
研究的目的:
- 为了研究一个患有纯获得性前视觉症 (PS) 的患者的面部和物体识别背后的神经过程.
- 将PS中大脑表征的时间演变与神经典型个体进行比较.
- 通过将她的大脑活动与深度神经网络 (DNN) 进行比较,探索与PS病情相关的计算缺陷.
主要方法:
- 从患者PS和神经类型对照中收集了高密度的电生理学记录.
- 代表性相似性分析 (RSA) 用于生成时间解析的大脑表示.
- 时间概括被计算出来,以评估大脑表征随时间的演变.
- PS的大脑表示与来自不同层深度神经网络 (DNN) 的表示相关.
主要成果:
- 患者PS在早期和晚期的大脑表现之间表现出不寻常的相似性,这表明早期视觉模式的过度概括.
- 与对照组相比,PS的脑活动与视觉DNN早期层具有更强的相似性.
- 随着时间的推移,神经典型个体的大脑表现变得越来越类似于DNN的后期层,与PS不同.
- PS的脑表征与语义DNN的相似性降低,表明高层处理中的缺陷.
结论:
- 在prosopagnosia中腹部流中的病变可以导致视觉表征中的非典型时间动态.
- 在prosopagnosia中的计算缺陷可能涉及过度依赖早期水平的视觉处理,类似于早期DNN层.
- 这些发现提供了对支持人脸识别的神经计算的见解,以及大脑损伤对视觉处理的影响.
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