走向生物可信的人工视觉
1Department of Philosophy, Philosophy-Neuroscience-Psychology Program, Washington University in St. Louis, St. Louis, MO, USA w.mason@wustl.eduhttp://www.masonwestfall.com.
The Behavioral and brain sciences
|September 28, 2023
概括
深层卷积神经网络 (DCNNs) 在结构上与人类视觉有所不同. 然而,强化学习代理人的人工视觉可能更类似于人类,特别是支持思维语言假设的语言表示.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 在图像分类中使用的深卷积神经网络 (DCNNs) 与人类视觉系统相比显示结构上的差异.
- 人工视觉系统,特别是那些用于强化学习代理的系统,提供了一个与人类视觉更相似的模型.
研究的目的:
- 探索人工视觉系统和人类视觉之间的结构相似性和差异.
- 研究语言类表示在增强人工视觉表现中的作用及其对认知理论的影响.
主要方法:
- 深层卷积神经网络 (DCNNs) 和人类视觉处理的比较分析.
- 对导航3D环境的强化学习代理进行评估,重点关注具有和没有语言类表示的性能.
主要成果:
- 优化用于图像分类的DCNN表现出与人类视觉的显著结构不相似之处.
- 强化学习代理人在使用类似语言的表示时表现出更好的表现.
- 通过类似语言的表示来增强人工代理的性能,为思维语言假设 (LoTH) 提供了间接支持.
结论:
- 人工视觉系统,特别是那些在3D环境中采用强化学习的系统,比DCNN更有希望地为模拟人类视觉提供了一个更有希望的途径.
- 类似语言的表征对于开发更像人类的人工视觉至关重要,并为思维语言假设提供了信誉.
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