Getting aligned on representational alignment

TL;DR

This paper reviews recent advances in measuring and improving representational alignment across cognitive science, neuroscience, and machine learning, proposing a unifying framework to foster cross-disciplinary collaboration.

Contribution

It introduces a unifying framework for representational alignment research, facilitating knowledge transfer and collaboration across multiple scientific disciplines.

Findings

Survey of recent developments in representational alignment

Identification of limited cross-field knowledge transfer

Proposal of a unifying framework for research and collaboration

Abstract

Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the similarity between the representations formed by these diverse systems? Do similarities in representations then translate into similar behavior? If so, then how can a system's representations be modified to better match those of another system? These questions pertaining to the study of representational alignment are at the heart of some of the most promising research areas in contemporary cognitive science, neuroscience, and machine learning. In this Perspective, we survey the exciting recent developments in representational alignment research in the fields of cognitive science, neuroscience, and machine learning. Despite their overlapping interests, there is limited knowledge transfer between these fields, so work in one field ends up duplicated in another, and useful innovations are not shared effectively. To improve communication, we propose a unifying framework that can serve as a common language for research on representational alignment, and map several streams of existing work across fields within our framework. We also lay out open problems in representational alignment where progress can benefit all three of these fields. We hope that this paper will catalyze cross-disciplinary collaboration and accelerate progress for all communities studying and developing information processing systems.