Informatics Department, Indiana University Bloomington, Bloomington, Indiana, USA; email:
Cognitive Science Program, Indiana University Bloomington, Bloomington, Indiana, USA.
Annu Rev Vis Sci. 2024 Sep;10(1):145-170. doi: 10.1146/annurev-vision-101322-103628.
What are the core learning algorithms in brains? Nativists propose that intelligence emerges from innate domain-specific knowledge systems, whereas empiricists propose that intelligence emerges from domain-general systems that learn domain-specific knowledge from experience. We address this debate by reviewing digital twin studies designed to reverse engineer the learning algorithms in newborn brains. In digital twin studies, newborn animals and artificial agents are raised in the same environments and tested with the same tasks, permitting direct comparison of their learning abilities. Supporting empiricism, digital twin studies show that domain-general algorithms learn animal-like object perception when trained on the first-person visual experiences of newborn animals. Supporting nativism, digital twin studies show that domain-general algorithms produce innate domain-specific knowledge when trained on prenatal experiences (retinal waves). We argue that learning across humans, animals, and machines can be explained by a universal principle, which we call space-time fitting. Space-time fitting explains both empiricist and nativist phenomena, providing a unified framework for understanding the origins of intelligence.
大脑中的核心学习算法是什么？天生论者认为，智能源自先天的特定领域知识系统，而经验主义者则认为，智能源自从经验中学习特定领域知识的通用系统。为了解决这一争论，我们回顾了旨在反向工程新生儿大脑中学习算法的数字孪生研究。在数字孪生研究中，新生动物和人工智能体在相同的环境中被养育，并接受相同的任务测试，从而可以直接比较它们的学习能力。支持经验主义的数字孪生研究表明，当通用算法接受新生动物第一人称视觉经验的训练时，它们会学习类似动物的物体感知。支持天生论的数字孪生研究表明，当通用算法接受产前经验（视网膜波）的训练时，它们会产生先天的特定领域知识。我们认为，人类、动物和机器之间的学习可以用一个通用原则来解释，我们称之为时空拟合。时空拟合解释了经验主义和天生论的现象，为理解智能的起源提供了一个统一的框架。
