# Joint processing of linguistic properties in brains and language models

arXiv: https://arxiv.org/abs/2212.08094 · HTML: https://mlsanity.com/paper/2212.08094/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 61 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [What you can cram into a single vector: Probing sentence embeddings for linguistic properties](https://mlsanity.com/paper/1805.01070/) (2018)
- [Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection](https://mlsanity.com/paper/2004.07667/) (2020)
- [Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks](https://mlsanity.com/paper/1608.04207/) (2016)
- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [Linguistic generalization and compositionality in modern artificial neural networks](https://mlsanity.com/paper/1904.00157/) (2019)
- [Inducing brain-relevant bias in natural language processing models](https://mlsanity.com/paper/1911.03268/) (2019)
- [Linking artificial and human neural representations of language](https://mlsanity.com/paper/1910.01244/) (2019)
- [Exploring the Role of BERT Token Representations to Explain Sentence Probing Results](https://mlsanity.com/paper/2104.01477/) (2021)
- [CogniVal: A Framework for Cognitive Word Embedding Evaluation](https://mlsanity.com/paper/1909.09001/) (2019)
- [Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?](https://mlsanity.com/paper/2205.01404/) (2022)
- [Language models and brains align due to more than next-word prediction and word-level information](https://mlsanity.com/paper/2212.00596/) (2022)
- [Relating Simple Sentence Representations in Deep Neural Networks and the Brain](https://mlsanity.com/paper/1906.11861/) (2019)
- [Analysis and Prediction of NLP Models Via Task Embeddings](https://mlsanity.com/paper/2112.05647/) (2021)

## Built on by (most cited first, 30 of 37)

- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Scaling laws for language encoding models in fMRI](https://mlsanity.com/paper/2305.11863/) (2023)
- [Explaining black box text modules in natural language with language models](https://mlsanity.com/paper/2305.09863/) (2023)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [Improving Semantic Understanding in Speech Language Models via Brain-tuning](https://mlsanity.com/paper/2410.09230/) (2024)
- [Speech language models lack important brain-relevant semantics](https://mlsanity.com/paper/2311.04664/) (2023)
- [Vision Function Layer in Multimodal LLMs](https://mlsanity.com/paper/2509.24791/) (2025)
- [ROSE: A Neurocomputational Architecture for Syntax](https://mlsanity.com/paper/2303.08877/) (2023)
- [Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models](https://mlsanity.com/paper/2409.05771/) (2024)
- [Crafting Interpretable Embeddings by Asking LLMs Questions](https://mlsanity.com/paper/2405.16714/) (2024)
- [Mapping Brains with Language Models: A Survey](https://mlsanity.com/paper/2306.05126/) (2023)
- [Multi-modal brain encoding models for multi-modal stimuli](https://mlsanity.com/paper/2505.20027/) (2025)
- [Decomposing Representation Space into Interpretable Subspaces with Unsupervised Learning](https://mlsanity.com/paper/2508.01916/) (2025)
- [Generative causal testing to bridge data-driven models and scientific theories in language neuroscience](https://mlsanity.com/paper/2410.00812/) (2024)
- [Brain-Language Model Alignment: Insights into the Platonic Hypothesis and Intermediate-Layer Advantage](https://mlsanity.com/paper/2510.17833/) (2025)
- [Computational Models to Study Language Processing in the Human Brain: A Survey](https://mlsanity.com/paper/2403.13368/) (2024)
- [Triple Phase Transitions: Understanding the Learning Dynamics of Large Language Models from a Neuroscience Perspective](https://mlsanity.com/paper/2502.20779/) (2025)
- [Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)](https://mlsanity.com/paper/2505.20029/) (2025)
- [Language models align with brain regions that represent concepts across modalities](https://mlsanity.com/paper/2508.11536/) (2025)
- [Far from the Shallow: Brain-Predictive Reasoning Embedding through Residual Disentanglement](https://mlsanity.com/paper/2510.22860/) (2025)
- [Divergences between Language Models and Human Brains](https://mlsanity.com/paper/2311.09308/) (2023)
- [Metric Learning Encoding Models: A Multivariate Framework for Interpreting Neural Representations](https://mlsanity.com/paper/2402.11608/) (2024)
- [Brain-like Functional Organization within Large Language Models](https://mlsanity.com/paper/2410.19542/) (2024)
- [Interpretable Next-token Prediction via the Generalized Induction Head](https://mlsanity.com/paper/2411.00066/) (2024)
- [LITcoder: A General-Purpose Library for Building and Comparing Encoding Models](https://mlsanity.com/paper/2509.09152/) (2025)
- [Fine-grained Analysis of Brain-LLM Alignment through Input Attribution](https://mlsanity.com/paper/2510.12355/) (2025)
- [Hierarchical Frequency Tagging Probe (HFTP): A Unified Approach to Investigate Syntactic Structure Representations in Large Language Models and the Human Brain](https://mlsanity.com/paper/2510.13255/) (2025)
- [Emergence of Phonemic, Syntactic, and Semantic Representations in Artificial Neural Networks](https://mlsanity.com/paper/2601.18617/) (2026)
- [Abstraction Induces the Brain Alignment of Language and Speech Models](https://mlsanity.com/paper/2602.04081/) (2026)
- [Sparse Autoencoders Map Brain-LLM Alignment onto Cortical Semantic Topography](https://mlsanity.com/paper/2605.23035/) (2026)

## Abstract (arXiv)

Language models have been shown to be very effective in predicting brain recordings of subjects experiencing complex language stimuli. For a deeper understanding of this alignment, it is important to understand the correspondence between the detailed processing of linguistic information by the human brain versus language models. We investigate this correspondence via a direct approach, in which we eliminate information related to specific linguistic properties in the language model representations and observe how this intervention affects the alignment with fMRI brain recordings obtained while participants listened to a story. We investigate a range of linguistic properties (surface, syntactic, and semantic) and find that the elimination of each one results in a significant decrease in brain alignment. Specifically, we find that syntactic properties (i.e. Top Constituents and Tree Depth) have the largest effect on the trend of brain alignment across model layers. These findings provide clear evidence for the role of specific linguistic information in the alignment between brain and language models, and open new avenues for mapping the joint information processing in both systems. We make the code publicly available [https://github.com/subbareddy248/linguistic-properties-brain-alignment].