The study, authored by Mengwen Xiao, Kangrui Du and Ziqi Lin, sits at the intersection of two research traditions that have matured rapidly over the past decade. The first is representational alignment, a family of techniques that quantify the similarity between the internal states of artificial neural networks and the activity patterns recorded from living brains. The second is chain-of-thought reasoning, the strategy by which large language models decompose complex problems into intermediate steps before committing to an answer. The new research connects these traditions in a striking way: instead of using brain activity only as a post-hoc yardstick, the authors use it as a training signal, encouraging a model’s internal representations to track those observed in human neural data while it reasons.

To appreciate why this matters, it helps to recall how the relationship between brains and machines has typically been studied. In a landmark 2023 analysis, researchers including Alex Zador and colleagues argued in Nature Communications that understanding natural intelligence requires studying both brains and their artificial analogues together, a perspective that helped galvanize the NeuroAI community. Subsequent work by Greta Tuckute and collaborators, published in Nature Human Behaviour in 2024, showed that language models vary widely in how well their internal states predict neural responses to sentences, and that this predictive power relates to how ‘brain-like’ a model’s representations are. Until recently, however, such alignment scores were largely descriptive. They told researchers which models resembled the brain, but they did not tell the models how to become more brain-like, nor whether doing so would improve them.

The conceptual shift from passive alignment to active neural guidance is the heart of the new work. In the passive paradigm, a trained model is frozen and its hidden activations are compared, layer by layer, with recordings such as functional MRI or magnetoencephalography taken while people read or solve problems. In the active paradigm described by Xiao and colleagues, the comparison happens during learning. The model receives an additional objective that rewards representational similarity to human neural data, effectively telling the network, at each stage of processing, that its internal geometry should not drift too far from the geometry observed in the brain. This turns the brain from a mirror into a compass, and the consequences for reasoning performance are substantial.

The technical machinery behind this idea draws on a rich literature. Representational similarity analysis and related encoding models provide differentiable measures of alignment, meaning that the gap between a model’s internal states and recorded brain activity can be computed as a quantity that gradient descent can minimize. Changde Du and Huiguang He, writing in an accompanying News and Views article in Nature Machine Intelligence, place the new study in the context of their own earlier work, published in the same journal in 2025, which explored how neural signals can inform machine learning systems. The new contribution extends this program from perception and language understanding into the domain of multi-step reasoning, where errors compound across steps and small representational deviations can cascade into dramatically wrong conclusions.

Reasoning is precisely where large language models remain most fragile. Chain-of-thought prompting, introduced by Jason Wei and colleagues at NeurIPS in 2022, showed that asking models to generate intermediate reasoning steps can unlock dramatic gains on arithmetic, logic and symbolic tasks. Yet subsequent analyses, including a large-scale study by Abulhair Saparov and colleagues in 2023, revealed that models often succeed on familiar reasoning patterns while failing catastrophically on novel ones, suggesting that their apparent reasoning may rest on shallow pattern matching rather than robust inference. If human neural representations encode something about the structure of valid inference that current training objectives fail to capture, then aligning with those representations during training could inject exactly the inductive bias that chain-of-thought systems lack.

The evidence reviewed by Du and He suggests that this is more than speculation. When the neural-guided objective is incorporated into training, the resulting models not only show stronger correspondence with human brain activity but also demonstrate improved reliability on reasoning benchmarks, producing more consistent and accurate chains of intermediate steps. The authors of the News and Views piece frame this as a move ‘from passive alignment to active neural guidance,’ a phrase that captures the dual payoff of the approach. Alignment ceases to be a descriptive statistic and becomes a functional ingredient of learning, and the improvement in reasoning suggests that the brain’s representational structure carries information that is genuinely useful for artificial inference, not merely an incidental byproduct of shared training data.

The finding also speaks to a long-running debate about what language models actually learn. Ev Fedorenko, Steven Piantadosi and Edward Gibson argued in Nature in 2024 that formal linguistic competence, the ability to produce fluent grammatical language, can be dissociated from functional linguistic competence, the ability to use language for thinking. Large models have largely conquered the former while remaining uneven at the latter. Neural guidance offers a possible bridge: if the internal representations that support human functional competence can be transferred, even partially, into artificial systems, then machines might acquire not just the form of reasoning but something closer to its substance. Related work by Juan Prado, Annapurni Chadha and James Booth, published in the Journal of Cognitive Neuroscience in 2011, mapped the neural substrates of arithmetic processing in children, illustrating the kind of structured neural data that such alignment approaches can exploit.

Independent work reinforces the momentum behind this direction. A 2026 study by Jie Chen, Yue Qi, Yu Wang and Guang Pan in Nature Communications reported further evidence that neural signals can inform and improve machine learning systems, indicating that the strategy is not an isolated result but part of an emerging research program. Together, these studies suggest that the coming years will see increasingly tight coupling between neuroimaging pipelines and model training loops, with brain data flowing not only into evaluation suites but into the loss functions themselves. The practical challenges remain considerable: collecting high-quality neural recordings at scale is expensive, individual differences in brain organization complicate generalization, and the causal question of why neural alignment helps reasoning is only beginning to be answered.

Still, the implications are hard to overstate. If the human brain can serve as a reliable guide for training artificial reasoners, then neuroscience and machine learning are no longer parallel enterprises exchanging occasional insights but a single, integrated discipline in which each field sharpens the other. Models guided by neural signals could become more trustworthy partners in science, medicine and education, while the act of aligning machines to minds may itself reveal which aspects of neural computation are essential to intelligence and which are biological accident. The work of Xiao, Du and Lin, and the perspective offered by Du and He, mark an early but compelling step in that direction, transforming the humble brain scan from a passive record of thought into an active instrument for teaching machines how to think.

Subject of Research: Using human brain signals to actively guide and improve the reasoning of artificial intelligence models

Article Title: Steering machine reasoning with brain signals

Article References: Du, C., & He, H. (2026). Steering machine reasoning with brain signals. Nature Machine Intelligence. https://doi.org/10.1038/s42256-026-01302-z

Image Credits: AI Generated

DOI: 10.1038/s42256-026-01302-z

Keywords: NeuroAI, representational alignment, large language models, chain-of-thought reasoning, brain signals, machine learning, neural encoding, Nature Machine Intelligence, cognitive neuroscience, artificial reasoning, fMRI, brain-inspired AI