{"slug": "neuroscientists-demonstrate-that-logical-reasoning-does-not-require-language", "title": "Neuroscientists demonstrate that logical reasoning does not require language", "summary": "A study published in the Proceedings of the National Academy of Sciences by MIT neuroscientists, including Hope Kean and Evelina Fedorenko, found that logical reasoning operates independently of the brain's language processing systems. Using fMRI on 29 healthy adults and testing individuals with severe language impairments, the researchers showed that abstract logical thinking does not rely on neural networks responsible for language. The findings challenge theories that language is the medium for complex thought and may inform AI design, as large language models like ChatGPT and Claude emulate reasoning through text.", "body_md": "A recent study provides evidence that the human ability to reason logically operates independently of the brain’s language processing systems. By examining brain activity in healthy adults and testing individuals with severe language impairments, scientists found that abstract logical thinking does not rely on the neural networks responsible for language. These findings were published in the [ Proceedings of the National Academy of Sciences](https://doi.org/10.1073/pnas.2520095123), a leading scientific journal.\n\nPhilosophers and cognitive scientists have debated whether natural language serves as the medium for complex thought. Humans have a capacity for two specific types of logical thought: inductive reasoning, which involves forming generalizations from a few examples, and deductive reasoning, which involves drawing specific conclusions from known premises. Because natural language allows humans to express highly complex, structured meanings, some theories propose that reasoning relies directly on linguistic representations.\n\n“Scientists, linguists, and philosophers have debated for millennia about the relationship between language and reasoning,” said [Hope Kean](https://www.mit.edu/~hopekean/), a postdoctoral research fellow in the Department of Brain and Cognitive Sciences at MIT. “Is language merely a tool for expressing our thoughts, or is it the very medium in which complex thought takes place?”\n\nThis idea is sometimes linked to the language of thought hypothesis, which notes that both language and complex thoughts are built from smaller pieces arranged in hierarchical structures. Sentences are built from words, much like logical thoughts are built from basic concepts. Despite these structural similarities, previous research hints at a separation between language and other forms of complex cognition.\n\nFor instance, people with aphasia, a condition characterized by a loss of language abilities following brain damage, can often still perform mathematical and causal reasoning. Brain imaging studies also indicate that the brain regions responsible for language comprehension are not typically engaged during tasks like solving math problems or understanding computer code.\n\nAbstract logical reasoning has received less attention in this debate. Prior studies attempting to isolate the brain areas responsible for deductive reasoning have yielded mixed results, partly because the tasks used often engage general cognitive effort alongside language processing. Recent advances in artificial intelligence, where language models show some ability to solve reasoning puzzles, have also reignited questions about whether linguistic competence is the foundation of logical ability.\n\n“This ancient question has taken on new importance with the rise of AI and computational perspectives on intelligence more broadly,” Kean said. “Large language models (LLMs), like ChatGPT and Claude, are trained by predicting text inputs and express their outputs in text, yet they can convincingly emulate some/many forms of human reasoning. If the human brain instead separates language from abstract logical thought, understanding this architectural difference may help explain both the successes and characteristic failures of reasoning in LLMs, and could inform the design of future AI systems.”\n\nTo systematically investigate this relationship, Kean, [Evelina Fedorenko](https://evlab.mit.edu), an associate professor of brain and cognitive sciences at MIT, and their colleagues designed a two-part approach. First, the authors used functional magnetic resonance imaging (fMRI) to monitor the brain activity of 29 healthy adults as they completed different types of logical reasoning tasks. The team independently mapped each participant’s language network by having them read sentences and lists of pronounceable non-words.\n\n[Add PsyPost to your preferred sources](https://www.google.com/preferences/source?q=psypost.org)\n\nFor the fMRI experiment, the participants completed three reasoning tests. To test inductive reasoning, participants tried to deduce the mathematical or structural rule that transformed an input list of numbers into an output list. For deductive reasoning, they evaluated classic syllogisms, judging whether a specific conclusion logically followed two premises. The third test involved matrix reasoning, where participants had to identify an outlier among a grid of geometric patterns.\n\nThe brain scans indicated that the language network showed almost no response during these logical reasoning tasks. While the language areas reacted strongly when participants read standard sentences, they were mostly inactive when participants were busy solving the logic problems.\n\nInstead, the inductive and matrix reasoning tasks activated the multiple demand network. This is a domain-general brain system known to support a wide range of goal-directed and cognitively demanding behaviors. The deductive reasoning task did not activate the multiple demand network, but rather recruited a distinct set of frontal and parietal brain regions, suggesting that different forms of reasoning rely on different neural pathways.\n\n“The convergence between the two approaches was important to us,” Kean told PsyPost. “Brain imaging allowed us to ask whether language areas participate in logical reasoning, while the aphasia cases allowed us to ask the stronger causal question of whether an intact language system is necessary for reasoning. Either method alone leaves room for alternative explanations, but together they provide complementary evidence.”\n\nIn the second part of the research, Kean and colleagues evaluated two individuals who had sustained extensive damage to the left hemisphere of their brains. This damage resulted in profound aphasia, leaving both individuals with severe grammatical and linguistic impairments. The scientists wanted to see if these severe language deficits also impaired their ability to reason logically.\n\nThe two patients, alongside a control group of 40 adults without brain injuries, completed non-verbal versions of the inductive and matrix reasoning tasks. The syllogism task was excluded because it requires reading and understanding complex sentences, which the patients could no longer do.\n\nDespite their profound language impairments, both patients performed remarkably well on the logic puzzles. In the inductive reasoning test, the two patients correctly solved 76 percent and 98 percent of the rule-finding problems, performing on par with the healthy control group.\n\nTheir performance on the matrix reasoning test was even more impressive. Both patients solved the vast majority of the geometric pattern problems, scoring well above the average for their age group. These behavioral results suggest that an intact language system is not necessary for someone to successfully engage in complex logical deduction and induction.\n\n“We are especially grateful to the participants with aphasia, whose contributions have made it possible to learn something fundamental about the organization of the human mind,” Kean said. “Working with the participants with aphasia was one of the most meaningful parts of this research. There is an old adage (now widely promulgated by institutions like the National Aphasia Association) that aphasia is a disorder of communication and not the intellect (aphasia.org).”\n\n“Because language is ordinarily how we reveal our thoughts to other people, a profound difficulty in speaking or understanding language can easily be mistaken for a loss of intellect,” Kean continued. “Our participants are a powerful demonstration that this inference is wrong. We hope that the study contributes not only to our scientific understanding of the mind, but also to a fuller recognition of the minds of people living with neurological disorders such as aphasia.”\n\nThe study focuses exclusively on adult brains, leaving open questions about the role of language during early childhood development. It is possible that learning to reason logically in the first place requires language exposure, even if the adult brain later separates these functions into distinct networks. Future research might explore whether infants and young children rely on language to build these logical frameworks.\n\n“People often assume that complex thoughts take the form of an internal monologue, based in language,” Kean noted. “Our findings suggest that, at least for formal logical reasoning in adults, this is not strictly the case.”\n\nThese results do not mean that language and thought are isolated from one another in daily life. The language network constantly interacts with our reasoning systems to communicate ideas and might act as a tool to hold information during difficult cognitive tasks.\n\n“Even though we ultimately discovered that this separation between language and reasoning is a feature of human cognition, that does not mean in principle that this is the only architecture capable of supporting complex thought,” Kean said. “Artificial systems, for instance, may achieve similar abilities using very different internal representations and computations.”\n\nRelying on just two participants with aphasia presents a limitation in sample size. Finding individuals with such profound, specific language impairments but intact general cognition is rare in neuropsychological research, making large-scale studies difficult. The research team suggests that future studies could expand on this work by testing a broader array of logical reasoning tasks to see if these patterns hold across different types of logic.\n\n“The long term goal is to map the geography of thought,” Kean said. “Ultimately, I also hope to move beyond mapping these systems toward intervening on them causally.”\n\nOne path forward involves noninvasive neuromodulation, a set of methods that temporarily alter brain activity using external tools like mild electrical or magnetic stimulation. Kean hopes to use these techniques “in order to see whether human reasoning might eventually be not only influenced, but enhanced.”\n\nThe study, “[Evidence from formal logical reasoning reveals that the language of thought is not natural language](https://doi.org/10.1073/pnas.2520095123),” was authored by Hope Kean, Alexander Fung, Paris Jaggers, Jason Chen, Joshua S. Rule, Yael Benn, Joshua B. Tenenbaum, Steven T. Piantadosi, Rosemary A. Varley, and Evelina Fedorenko.", "url": "https://wpnews.pro/news/neuroscientists-demonstrate-that-logical-reasoning-does-not-require-language", "canonical_source": "https://www.psypost.org/neuroscientists-reveal-that-logical-reasoning-does-not-require-language/", "published_at": "2026-09-01 10:00:23+00:00", "updated_at": "2026-09-01 10:24:09.037267+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research"], "entities": ["MIT", "Hope Kean", "Evelina Fedorenko", "Proceedings of the National Academy of Sciences", "ChatGPT", "Claude"], "alternates": {"html": "https://wpnews.pro/news/neuroscientists-demonstrate-that-logical-reasoning-does-not-require-language", "markdown": "https://wpnews.pro/news/neuroscientists-demonstrate-that-logical-reasoning-does-not-require-language.md", "text": "https://wpnews.pro/news/neuroscientists-demonstrate-that-logical-reasoning-does-not-require-language.txt", "jsonld": "https://wpnews.pro/news/neuroscientists-demonstrate-that-logical-reasoning-does-not-require-language.jsonld"}}