Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Helmholtz Institute for Human-Centered AI, Munich, Germany
Behav Brain Sci. 2023 Nov 23;47:e147. doi: 10.1017/S0140525X23003266.
Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. Although the former requires the specification of a fixed set of computational structures and a definition of how these structures interact with each other, the latter necessitates the commitment to a particular prior and a likelihood function that - in combination with Bayes' rule - determine the model's behavior. In recent years, a new framework has established itself as a promising tool for building models of human cognition: the framework of meta-learning. In contrast to the previously mentioned model classes, meta-learned models acquire their inductive biases from experience, that is, by repeatedly interacting with an environment. However, a coherent research program around meta-learned models of cognition is still missing to date. The purpose of this article is to synthesize previous work in this field and establish such a research program. We accomplish this by pointing out that meta-learning can be used to construct Bayes-optimal learning algorithms, allowing us to draw strong connections to the rational analysis of cognition. We then discuss several advantages of the meta-learning framework over traditional methods and reexamine prior work in the context of these new insights.
心理学家和神经科学家广泛依赖计算模型来研究和分析人类思维。传统上，这种计算模型是由专家研究人员手动设计的。两个突出的例子是认知架构和认知的贝叶斯模型。虽然前者需要指定一组固定的计算结构，并定义这些结构如何相互作用，但后者需要承诺采用特定的先验和似然函数——与贝叶斯规则相结合——来确定模型的行为。近年来，一种新的框架已经确立为构建人类认知模型的有前途的工具：元学习框架。与前面提到的模型类不同，元学习模型从经验中获得其归纳偏差，即通过反复与环境交互来获得。然而，到目前为止，关于认知的元学习模型的连贯研究计划仍然缺失。本文的目的是综合该领域的先前工作，并建立这样一个研究计划。我们通过指出元学习可以用于构建贝叶斯最优学习算法来实现这一点，从而使我们能够与认知的理性分析建立强有力的联系。然后，我们讨论了元学习框架相对于传统方法的几个优势，并根据这些新的见解重新审视了先前的工作。
