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

元学习认知模型

Meta-Learned Models of Cognition

Functional connectivity (FC) between regions of the brain can be assessed by
the degree of temporal correlation measured with functional neuroimaging
modalities. Based on the fact that these connectivities build a network,
graph-based approaches for analyzing the brain connectome have provided
insights into the functions of the human brain. The development of graph neural
networks (GNNs) capable of learning representation from graph structured data
has led to increased interest in learning the graph representation of the brain
connectome. Although recent attempts to apply GNN to the FC network have shown
promising results, there is still a common limitation that they usually do not
incorporate the dynamic characteristics of the FC network which fluctuates over
time. In addition, a few studies that have attempted to use dynamic FC as an
input for the GNN reported a reduction in performance compared to static FC
methods, and did not provide temporal explainability. Here, we propose STAGIN,
a method for learning dynamic graph representation of the brain connectome with
spatio-temporal attention. Specifically, a temporal sequence of brain graphs is
input to the STAGIN to obtain the dynamic graph representation, while novel
READOUT functions and the Transformer encoder provide spatial and temporal
explainability with attention, respectively. Experiments on the HCP-Rest and
the HCP-Task datasets demonstrate exceptional performance of our proposed
method. Analysis of the spatio-temporal attention also provide concurrent
interpretation with the neuroscientific knowledge, which further validates our
method. Code is available at this https URL

提出了一种名为 STAGIN 的方法，该方法可以学习到大脑连通性的动态图表示，其中包括空间 - 时间关注的新型读取功能和变压器编码器，对 HCP - 休息和 HCP - 任务数据集的实验表现优异，并且具有与神经科学知识的相应解释。