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[2602.12811] Left-right asymmetry in predicting brain activity from LLMs' representations emerges with their formal linguistic competence
Summary
This article explores how left-right asymmetry in predicting brain activity from large language models (LLMs) correlates with their formal linguistic competence, revealing insights into the cognitive processes shared between humans and LLMs.
Why It Matters
Understanding the relationship between LLMs and human brain activity can enhance the development of AI systems that better mimic human linguistic abilities. This research highlights the importance of linguistic competence in AI and its implications for cognitive science and AI development.
Key Takeaways
- Left-right asymmetry in brain activity prediction emerges with LLMs' linguistic competence.
- The study uses OLMo-2 and fMRI data to analyze brain responses to language processing.
- Asymmetry correlates with formal linguistic abilities, not with arithmetic or reasoning tasks.
- Findings generalize to other LLMs and languages, indicating broader implications.
- This research contributes to understanding cognitive processes in AI and humans.
Computer Science > Computation and Language arXiv:2602.12811 (cs) [Submitted on 13 Feb 2026] Title:Left-right asymmetry in predicting brain activity from LLMs' representations emerges with their formal linguistic competence Authors:Laurent Bonnasse-Gahot, Christophe Pallier View a PDF of the paper titled Left-right asymmetry in predicting brain activity from LLMs' representations emerges with their formal linguistic competence, by Laurent Bonnasse-Gahot and Christophe Pallier View PDF HTML (experimental) Abstract: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 brain scores 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 sentenc...
