Does the brain represent words? An evaluation of brain decoding studies of language understanding

TL;DR

This paper critically evaluates brain decoding studies of language, highlighting their limitations in determining the true nature of neural representations and proposing methodological improvements for more accurate interpretations.

Contribution

It demonstrates the indeterminacy in current brain decoding methods and suggests modifications to better identify neural language representations.

Findings

Current studies underdetermine the content of neural representations.

Standard evaluations cannot distinguish different language processing mechanisms.

Proposed paradigm changes can improve claims about neural language representations.

Abstract

Language decoding studies have identified word representations which can be used to predict brain activity in response to novel words and sentences (Anderson et al., 2016; Pereira et al., 2018). The unspoken assumption of these studies is that, during processing, linguistic information is transformed into some shared semantic space, and those semantic representations are then used for a variety of linguistic and non-linguistic tasks. We claim that current studies vastly underdetermine the content of these representations, the algorithms which the brain deploys to produce and consume them, and the computational tasks which they are designed to solve. We illustrate this indeterminacy with an extension of the sentence-decoding experiment of Pereira et al. (2018), showing how standard evaluations fail to distinguish between language processing models which deploy different mechanisms and which are optimized to solve very different tasks. We conclude by suggesting changes to the brain decoding paradigm which can support stronger claims of neural representation.