Cited by 413Cognitive and developmental aspects of mathematical skillsNeurobiology of Language and BilingualismNeuroscience and Music Perception

Origins of the brain networks for advanced mathematics in expert mathematicians

Open access
Amalric, Marie, Dehaene, Stanislas·2016·Proceedings of the National Academy of Sciences

Abstract

Significance: Our work addresses the long-standing issue of the relationship between mathematics and language. By scanning professional mathematicians, we show that high-level mathematical reasoning rests on a set of brain areas that do not overlap with the classical left-hemisphere regions involved in language processing or verbal semantics. Instead, all domains of mathematics we tested (algebra, analysis, geometry, and topology) recruit a bilateral network, of prefrontal, parietal, and inferior temporal regions, which is also activated when mathematicians or nonmathematicians recognize and manipulate numbers mentally. Our results suggest that high-level mathematical thinking makes minimal use of language areas and instead recruits circuits initially involved in space and number. This result may explain why knowledge of number and space, during early childhood, predicts mathematical achievement.

Integrity signals

Registry

Crossref · 10.1073/pnas.1603205113

Cites

61 of 61 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 [41]

  • 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 [41]

  • 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 [41]

  • Logic and math recruit a fronto-parietal network including dorsolateral PFC and IPS as well as putative symbol representations in inferior temporal cortex.

    “Rather than recruiting perisylvian fronto-temporal areas, logic and math recruit a fronto-parietal network, including the dorsolateral prefrontal cortex (PFC) and the intraparietal sulcus (IPS) as well as putative symbol representations (i.e. numberform area) in inferior temporal cortex (Amalric and Dehaene, 2016; Coetzee and Monti, 2018; Goel et al., 2007; Monti et al., 2009).”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Amalric, M., & Dehaene, S.
Affiliations
Université Paris-Sud (FR)
Venue
Proceedings of the National Academy of Sciences
Published
2016-04-11
Volume
113
Issue
18
Pages
4909-4917
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2341658315
Cited by
413
References
61
Citations by year
2026: 22 · 2025: 43 · 2024: 29 · 2023: 31 · 2022: 42 · 2021: 55 · 2020: 70 · 2019: 37 · 2018: 58 · 2017: 21 · 2016: 5
FWCI
40.78
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • PubMed Central (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
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
Keywords
mathematical expertise, higher mathematics, brain networks, number sense, mathematical judgment, intraparietal cortex, frontal cortex, face processing, language and mathematics
MeSH terms
Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Mathematics, Semantics

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review