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

arXiv: https://arxiv.org/abs/1806.00591 · HTML: https://mlsanity.com/paper/1806.00591/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification](https://mlsanity.com/paper/2112.02690/) (2021)
- [Decoding EEG Brain Activity for Multi-Modal Natural Language Processing](https://mlsanity.com/paper/2102.08655/) (2021)
- [Does injecting linguistic structure into language models lead to better alignment with brain recordings?](https://mlsanity.com/paper/2101.12608/) (2021)
- [Can Brain Signals Reveal Inner Alignment with Human Languages?](https://mlsanity.com/paper/2208.06348/) (2022)
- [Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain](https://mlsanity.com/paper/2303.14871/) (2023)
- [Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding](https://mlsanity.com/paper/2312.09430/) (2023)
- [Mapping Brains with Language Models: A Survey](https://mlsanity.com/paper/2306.05126/) (2023)
- [Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guide](https://mlsanity.com/paper/2203.05300/) (2022)
- [Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding](https://mlsanity.com/paper/2502.17465/) (2025)

## Abstract (arXiv)

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.