Biological and artificial information processing systems form representations
of the world that they can use to categorize, reason, plan, navigate, and make
decisions. To what extent do the representations formed by these diverse
systems agree? Can diverging representations still lead to the same behaviors?
And how can systems modify their representations to better match those of
another system? These questions pertaining to the study of
\textbf{\emph{representational alignment}} are at the heart of some of the most
active research areas in contemporary cognitive science, neuroscience, and
machine learning. Unfortunately, there is limited knowledge-transfer between
research communities interested in representational alignment, and much of the
progress in one field ends up being rediscovered independently in another, when
greater cross-field communication would be advantageous. To improve
communication between fields, we propose a unifying framework that can serve as
a common language between researchers studying representational alignment. We
survey the literature from the fields of cognitive science, neuroscience, and
machine learning, and demonstrate how prior work fits into this framework.
Finally, we lay out open problems in representational alignment where progress
can benefit all three fields. We hope that our work can catalyze
cross-disciplinary collaboration and accelerate progress for all communities
studying and developing information processing systems. We note that this is a
working paper and encourage readers to reach out with their suggestions for
future revisions.

生物和人工信息处理系统形成世界的表示，以用于分类、推理、规划、导航和决策。研究这些不同系统形成的表示在多大程度上一致？不同的表示是否仍然可以导致相同的行为？系统如何修改其表示以更好地匹配另一个系统的表示？研究表示对齐的这些问题是当代认知科学、神经科学和机器学习最活跃的研究领域之一。为了改善不同领域之间的沟通，我们提出了一个统一的框架，可以作为研究表示对齐的研究人员之间的共同语言。我们从认知科学、神经科学和机器学习领域的文献中进行综述，并展示了以前的工作如何适应这个框架。最后，我们提出了在表示对齐方面的开放问题，进展可以使这三个领域受益。我们希望我们的工作能够促进跨学科合作，加速所有研究和开发信息处理系统的社区的进步。请注意，这是一篇工作论文，欢迎读者提出他们对未来修订的建议。