Thought Beyond Language: Neural Dissociation of Algebra and Natural Language
Abstract
A central question in cognitive science is whether natural language provides combinatorial operations that are essential to diverse domains of thought. In the study reported here, we addressed this issue by examining the role of linguistic mechanisms in forging the hierarchical structures of algebra. In a 3-T functional MRI experiment, we showed that processing of the syntax-like operations of algebra does not rely on the neural mechanisms of natural language. Our findings indicate that processing the syntax of language elicits the known substrate of linguistic competence, whereas algebraic operations recruit bilateral parietal brain regions previously implicated in the representation of magnitude. This double dissociation argues against the view that language provides the structure of thought across all cognitive domains.
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Neuroimaging studies have consistently shown that the language network remains largely inactive during various reasoning tasks.
“Neuroimaging studies have consistently shown that the language network -a set of brain regions responsible for language comprehension and production -remains largely inactive during various reasoning tasks”
From Training Large Language Models to Reason in a Continuous Latent Space (2024) · cites this paper as Monti et al., 2012
fMRI reveals that the language network responds reliably when people listen to, read, or generate sentences, but not when they perform arithmetic tasks, engage in logical reasoning, understand computer programs, listen to music, categorize objects or events, reason about people's mental states, or process non-verbal communicative information like facial expressions or gestures.
“it responds reliably when people listen to, read, or generate sentences, but not when they perform arithmetic tasks, engage in logical reasoning, understand computer programs, listen to music, categorize objects or events, reason about people’s mental states, or process non-verbal communicative information like facial expressions or gestures [37, 41, 42, 43, 44, 45, 46, 47, 48].”
From Dissociating language and thought in large language models (2024) · cites this paper as [47]
The multiple demand network supports reasoning even when the task is presented linguistically.
“Importantly, the multiple demand network supports reasoning even when the task is presented linguistically [41, 47, 140]”
From Dissociating language and thought in large language models (2024) · cites this paper as [47]
The multiple demand network is engaged in logic, mathematical reasoning, physical reasoning, and computer code comprehension.
“these regions are engaged in many cognitively demanding tasks: logic [47], mathematical reasoning [41], physical reasoning [139], and computer code comprehension [46, 140].”
From Dissociating language and thought in large language models (2024) · cites this paper as [47]
Previous studies have shown that sentences with content words lead to larger responses in the perisylvian fronto-temporal language network than spoken equations or logical statements with variables.
“Although all of the tasks in the language/math/logic localizer contain language stimuli, previous studies have shown that sentences with content words lead to larger responses in the perisylvian fronto-temporal language network than spoken equations or logical statements with variables (Kanjlia et al., 2016; Monti et al., 2009; Monti et al., 2012).”
From Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network (2020) · cites this paper as Monti et al., 2012
Previous studies have found that responses to language, logic and math when compared to each other were similar to what was observed for each domain relative to independent control conditions.
“Furthermore, previous studies have found that responses to language, logic and math when compared to each other were similar to what was observed for each domain relative to independent control conditions (e.g. sentences relative to lists of non-words for language, and easy vs. hard logic problems; Kanjlia et al., 2016; Monti et al., 2009, Monti et al., 2012).”
From Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network (2020) · cites this paper as Monti et al., 2012
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- Authors
- Monti, M. M., Parsons, L. M., & Osherson, D. N.
- Affiliations
- University of California, Los Angeles (US)
- University of Sheffield (GB)
- Princeton University (US)
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2012-07-03
- Volume
- 23
- Issue
- 8
- Pages
- 914-922
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W2107698275
- Cited by
- 171
- References
- 40
- Citations by year
- 2026: 9 · 2025: 26 · 2024: 16 · 2023: 13 · 2022: 9 · 2021: 18 · 2020: 14 · 2019: 12 · 2018: 10 · 2017: 7 · 2016: 11 · 2015: 7 · 2014: 10 · 2013: 5 · 2012: 4
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Cognitive and developmental aspects of mathematical skills
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Keywords
- natural language, combinatorial operations, language and thought, parietal cortex, numerical magnitude representation, double dissociation, mathematical cognition
- MeSH terms
- Brain, Brain Mapping, Cognition, Female, Frontal Lobe, Humans, Language, Magnetic Resonance Imaging, Male, Mathematics, Parietal Lobe, Temporal Lobe, Thinking, Functional Neuroimaging
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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