深度神经网络如何为心理学科学中的理论提供信息?
Sam Whitman McGrath1, Jacob Russin2, Ellie Pavlick3
1Philosophy Department, Department of Cognitive, Linguistic & Psychological Sciences, Brown University.
Current directions in psychological science
|February 14, 2025
概括
深度神经网络 (DNN) 显示出类似人类的性能,但往往是难以辨认的. 最近的工作表明DNN机制是可解释的,桥梁AI和认知科学.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 深度神经网络 (DNN) 在复杂的任务中实现类似人类的性能.
- DNN的不可知性挑战了它们与人类认知理论的相关性.
- 现有的辩论两极分化了关于DNN对心理学科学的有用性的观点.
研究的目的:
- 审查关于解释DNN内部机制的研究.
- 挑战DNN与认知理论无关的假设.
- 展示DNN如何为认知和发展理论提供信息.
主要方法:
- 关于DNN可解释性的最近研究的文献综述.
- 分析DNN机制与心理构造之间的功能相似性.
- 在认知科学中对DNN极端观点的论证.
主要成果:
- 有证据表明,DNN内部机制可以从功能上解释.
- 这种可解释性反对DNN在心理上无关的观念.
- DNN为理解人类认知及其发展提供了一条新的途径.
结论:
- DNN的可解释性使人工智能的进步与认知科学相协调.
- DNN可以告知并可能重塑人类认知的理论.
- 对DNNs的细微视角对于推进跨学科研究至关重要.
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