ArticleNature communications2026
Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Review
- The human language processing system straightens natural speech.bioRxiv : the preprint server for biology · 2026Article
- Sharpened Visual Memory Representations Are Reflected in Inferotemporal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- A Deep Neural Network Model of Audiovisual Speech Recognition Reports the McGurk Effect.bioRxiv : the preprint server for biology · 2025Article
- Evaluating scientific theories as predictive models in language neuroscience.bioRxiv : the preprint server for biology · 2025Article
- Low-Rank Tensor Encoding Models Decompose Natural Speech Comprehension Processes.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Emerging research seeks to draw neuroscientific insights from the neural predictivity of large language models (LLMs). However, as results rapidly proliferate, there is a growing need for large-scale assessments of their robustness. Here, we analyze a wide range of models and methodological approaches across three widely used neural datasets. We find that the use of shuffled train-test splits has contributed to findings that are influential but spurious. Furthermore, how activations are extracted from LLMs can bias results in favor of specific model classes. Lastly, we find that confounding variables, particularly positional signals and word rate, perform competitively with trained LLMs and fully account for the neural predictivity of untrained LLMs on these neural datasets. Although many studies in the field avoid these pitfalls, our results indicate that some apparent alignment between LLMs and brains has emerged from non-robust methods and overlooked confounds.
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