Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA.
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Neuron. 2020 Feb 5;105(3):416-434. doi: 10.1016/j.neuron.2019.12.002.
Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have exposed the power of optimizing millions of synaptic weights over millions of observations to operate robustly in real-world contexts. These models do not learn simple, human-interpretable rules or representations of the world; rather, they use local computations to interpolate over task-relevant manifolds in a high-dimensional parameter space. Counterintuitively, similar to evolutionary processes, over-parameterized models can be simple and parsimonious, as they provide a versatile, robust solution for learning a diverse set of functions. This new family of direct-fit models present a radical challenge to many of the theoretical assumptions in psychology and neuroscience. At the same time, this shift in perspective establishes unexpected links with developmental and ecological psychology.
进化是一种盲目的适应过程，通过这个过程，生物体适应了它们的环境。大脑是否利用类似的盲目适应过程来学习如何感知和作用于世界？人工神经网络的最新进展揭示了通过优化数百万个突触权重来适应数百万个观察结果的强大能力，从而在现实环境中稳健地运行。这些模型并没有学习简单的、人类可解释的世界规则或表示；相反，它们使用局部计算在高维参数空间中对任务相关流形进行插值。反直觉的是，与进化过程类似，过度参数化的模型可以很简单和简洁，因为它们为学习多样化的函数提供了一种通用的、稳健的解决方案。这组新的直接适应模型对心理学和神经科学中的许多理论假设提出了根本性的挑战。与此同时，这种视角的转变与发展心理学和生态心理学建立了意想不到的联系。
