使用深度神经网络来解开人类感知和记忆中的视觉和语义信息
Adva Shoham1, Idan Daniel Grosbard2,3,4, Or Patashnik4
1School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel. advashoham@mail.tau.ac.il.
Nature human behaviour
|February 9, 2024
概括
这项研究揭示了视觉和语义信息如何形成心理表征. 通过使用深度神经网络,研究人员发现视觉在感知过程中占主导地位,在熟悉的面孔和物体的记忆回忆过程中占有语义主导地位.
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.
- 心理学 心理学 心理学
背景情况:
- 心理表现整合了视觉和语义信息.
- 在人类认知中解开这些组成部分是具有挑战性的,因为它们相互混合.
- 深度神经网络为分析这些组件提供了新的方法.
研究的目的:
- 解开人类心理表现的视觉,语义和综合视觉语义组成部分.
- 在感知和记忆回忆过程中调查熟悉的面孔和物体的心理表现的内容.
- 为理解视觉和语义信息在认知中的相互作用提出一个新的框架.
主要方法:
- 利用在图像,文本或配对图像-文本数据上训练的深度神经网络.
- 分析熟悉的面孔和物体的心理表现.
- 视觉感知与记忆回忆期间比较的表示.
主要成果:
- 图像的感知显示出比语义更大的视觉贡献.
- 图像的回忆回忆显示了一个反向的模式,具有更大的语义贡献.
- 在感知和记忆中确定了一个独特的,集成的视觉语义表示组件.
结论:
- 视觉和语义信息显然有助于心理表现.
- 一个全新的综合视觉语义组件在感知和记忆中起着至关重要的作用.
- 提出了一个框架,视觉和语义信息在心理表现中动态相互作用.
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