# Left-right asymmetry in predicting brain activity from LLMs' representations emerges with their formal linguistic competence

arXiv: https://arxiv.org/abs/2602.12811 · HTML: https://mlsanity.com/paper/2602.12811/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 69 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 22 of 22)

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- [From Language to Cognition: How LLMs Outgrow the Human Language Network](https://mlsanity.com/paper/2503.01830/) (2025)
- [Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and Context](https://mlsanity.com/paper/2302.14389/) (2023)
- [A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese](https://mlsanity.com/paper/2411.06096/) (2024)
- [Disentangling the Factors of Convergence between Brains and Computer Vision Models](https://mlsanity.com/paper/2508.18226/) (2025)
- [fMRI predictors based on language models of increasing complexity recover brain left lateralization](https://mlsanity.com/paper/2405.17992/) (2024)

## Abstract (arXiv)

When humans and large language models (LLMs) process the same text, activations in the LLMs correlate with brain activity measured, e.g., with functional magnetic resonance imaging (fMRI). Moreover, it has been shown that, as the training of an LLM progresses, the performance in predicting brain activity from its internal activations improves more in the left hemisphere than in the right one. The aim of the present work is to understand which kind of competence acquired by the LLMs underlies the emergence of this left-right asymmetry. Using the OLMo-2 7B language model at various training checkpoints and fMRI data from English participants, we compare the evolution of the left-right asymmetry in the correlation between brain activity and model predictions alongside performance on several benchmarks. We observe that the asymmetry co-emerges with the formal linguistic abilities of the LLM. These abilities are demonstrated in two ways: by the model's capacity to assign a higher probability to an acceptable sentence than to a grammatically unacceptable one within a minimal contrasting pair, and by its ability to produce well-formed text. By contrast, the left-right asymmetry does not align with the performance on arithmetic or Dyck language tasks; nor with text-based tasks involving world knowledge and reasoning. We generalize these results to another family of LLMs (Pythia) and two other languages, French and Chinese. Our observations indicate that the left-right asymmetry in brain predictivity matches the progress in formal linguistic competence.