Meta-learning is a framework for learning learning algorithms through
repeated interactions with an environment as opposed to designing them by hand.
In recent years, this framework has established itself as a promising tool for
building models of human cognition. Yet, a coherent research program around
meta-learned models of cognition is still missing. The purpose of this article
is to synthesize previous work in this field and establish such a research
program. We rely on three key pillars to accomplish this goal. We first point
out that meta-learning can be used to construct Bayes-optimal learning
algorithms. This result not only implies that any behavioral phenomenon that
can be explained by a Bayesian model can also be explained by a meta-learned
model but also allows us to draw strong connections to the rational analysis of
cognition. We then discuss several advantages of the meta-learning framework
over traditional Bayesian methods. In particular, we argue that meta-learning
can be applied to situations where Bayesian inference is impossible and that it
enables us to make rational models of cognition more realistic, either by
incorporating limited computational resources or neuroscientific knowledge.
Finally, we reexamine prior studies from psychology and neuroscience that have
applied meta-learning and put them into the context of these new insights. In
summary, our work highlights that meta-learning considerably extends the scope
of rational analysis and thereby of cognitive theories more generally.

本研究综合了先前的工作，旨在建立一个关于 cognition 的 meta-learned 模型的研究计划，证明 meta-learning 可以用于构建 Bayes-optimal learning algorithms，实现对 cognitive theories 的更加广泛的范围的建模