Clarifying the conceptual dimensions of representation in neuroscience

Abstract

Despite the centrality of the notion of representation in neuroscience, the field lacks a unified framework for the concepts used to characterize representation, leading to disparate use of both terminology and the measures associated with it. To offer clarification, we propose a core set of conceptual dimensions that characterize representations in neuroscience. These dimensions describe relations between a neural response, features that may be represented and downstream effects of the neural response. A neural response may be shown to be sensitive or specific to a feature, invariant to other features or functional (it is used downstream in the brain). We use information-theoretic measures to illustrate these conceptual dimensions and explain how they relate to data analysis methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence or adaptation. We consider several canonical examples, including models of the representation of orientation, numerosity and spatial location, which illustrate how the evidence put forth in support or criticism of these models is systematized by our framework. By offering a unified conceptual framework to characterize representation in neuroscience, we hope to aid the comparison and integration of results across studies and research groups and to help to determine when evidence for a neural representation is strong. Appeals to representation are widespread, despite neuroscientists’ uncertainty about what kind of findings count as evidence for such claims. In this Perspective, Pohl and colleagues develop a unified framework that distinguishes four conceptual dimensions relevant to representation, illustrating them in information-theoretic terms to explicitly characterize representation in neuroscience.