神经网络需要现实世界的行为.
Aedan Y Li1, Marieke Mur1,2
1Department of Psychology, Western University, London, ON, Canada aedan.li@uwo.ca, www.aedanyueli.com.
The Behavioral and brain sciences
|December 6, 2023
概括
控制实验对于评估深度神经网络 (DNN) 作为生物视觉模型至关重要. 将图像识别与现实的任务相结合,可以增强对现实世界对象识别行为的理解.
科学领域:
- 计算神经科学是一种计算神经科学.
- 计算机视觉 计算机视觉 计算机视觉
- 认知科学是一种认知科学.
背景情况:
- 深度神经网络 (DNN) 越来越多地用于模拟生物视觉系统.
- 仅在图像识别任务上评估DNN可能无法完全捕捉它们的生物相关性.
- "神经科学需要行为"原则强调了行为数据在理解神经系统中的重要性.
研究的目的:
- 倡导使用可控行为实验来评估DNN作为生物视觉模型.
- 提出一个评估DNN的框架,将传统的图像识别与行为范式相结合.
- 在计算模型中强调现实世界对象识别行为的必要性.
主要方法:
- 审查由尔斯等人提出的方法. 用于评估DNN的.
- 在"神经科学需要行为"和DNN评估之间进行并行.
- 建议将可控,现实的任务环境集成到DNN评估中.
- 专注于对象识别行为作为一个关键的行为措施.
主要成果:
- 控制行为实验提供了一个更强大的评估DNNs作为生物视觉的模型.
- 将行为任务与图像识别相结合,可以更好地理解视觉处理.
- 控制良好的,现实的环境对于参与和评估现实世界对象识别至关重要.
结论:
- 控制行为实验对于推进DNN作为生物视觉准确模型至关重要.
- 未来的研究应该纳入现实的行为任务,以弥合计算模型和生物系统之间的差距.
- 以行为为中心的方法对于计算神经科学和人工智能的进步至关重要.
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