A distinct cortical network for mathematical knowledge in the human brain

Amalric, Marie; Dehaene, Stanislas

doi:10.1016/j.neuroimage.2019.01.001

Cited by 86 publications

(91 citation statements)

References 47 publications

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“…However, music does not have semantics (Slevc, 2012); perhaps some degree of meaningfulness is required to engage linguistic syntactic mechanisms? Our findings, along with prior findings from math and logic (Amalric & Dehaene, 2019;Monti et al, 2009Monti et al, , 2012, argue against this possibility: the language system does not respond to meaningful structured input that is non-linguistic. The lack of the language system engagement during code comprehension adds to the body of work that demonstrates high input selectivity of these regions (Fedorenko et al, 2011;Jouravlev et al, 2019;Monti et al, 2009Monti et al, , 2012Pritchett et al, 2018).…”

Section: The Language System Is Functionally Conservativecontrasting

confidence: 48%

“…The fact that coding can be learned in adulthood suggests that it may rely on existing cognitive systems. Here, we tested the role of two candidate neural systems in computer code comprehension: the domain-general multiple demand (MD) system (Duncan, 2010) that has been linked to diverse executive demands and implicated in both math and logic (e.g., Amalric & Dehaene, 2019;Goel, 2007;Monti et al, 2007Monti et al, , 2009, and the language-selective system (Fedorenko et al, 2011) that has been linked to lexical and combinatorial linguistic processes (e.g., Bautista & Wilson, 2016;Fedorenko et al, 2010Fedorenko, Nieto-Castañon, et al, 2012;Keller et al, 2001;Mollica et al, 2020). We found robust bilateral responses to code problems within the MD system, a pattern that held across two very different programming languages (Python and ScratchJr), types or problems (math and string manipulation), and problem structure (sequential statements, for loops, and if statements).…”

Section: Discussionmentioning

confidence: 99%

“…Much evidence suggests that the MD system can flexibly store task-relevant information in the short term (e.g., Fedorenko et al, 2013;Freedman et al, 2001;Shashidhara, Mitchell, et al, 2019;Wen et al, 2019;Woolgar et al, 2011). However, evidence from studies on processing mathematics (e.g., Amalric & Dehaene, 2019) and physics (e.g., Cetron et al, 2019;Fischer et al, 2016) further suggests that the MD system can store some domain-specific representations in the long term, perhaps for evolutionarily lateemerging and ontogenetically late-acquired domains of knowledge. Our data add to this body of evidence by showing that the MD system stores and uses information required for code comprehension.…”

Section: System's Engagement Reflects the Use Of Domain-general Rementioning

confidence: 99%

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Comprehension of computer code relies primarily on domain-general executive brain regions

Ivanova

Srikant

Sueoka

et al. 2020

Preprint

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Computer programming is a novel cognitive tool that has transformed modern society. An integral part of programming is code comprehension: the ability to process individual program tokens, combine them into statements, which, in turn, combine to form a program. What cognitive and neural mechanisms support this ability to process computer code? Here, we used fMRI to investigate the role of two candidate brain systems in code comprehension: the multiple demand (MD) system, typically recruited for math, logic, problem solving, and executive function, and the language system, typically recruited for linguistic processing. Across two experiments, we examined brain responses to code written in two programing languages: Python, a text-based programming language (Experiment 1) and ScratchJr, a graphical programming language for children (Experiment 2). To isolate neural activity evoked by code comprehension per se rather than by processing program content, we contrasted responses to code problems with responses to contentmatched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments. In contrast, the language system responded strongly to sentence problems, but only weakly or not at all to code problems. We conclude that code comprehension relies primarily on domaingeneral executive resources, demonstrating that the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.

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Section: The Language System Is Functionally Conservativecontrasting

confidence: 48%

Section: Discussionmentioning

confidence: 99%

Section: System's Engagement Reflects the Use Of Domain-general Rementioning

confidence: 99%

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Comprehension of computer code relies primarily on domain-general executive brain regions

Ivanova

Srikant

Sueoka

et al. 2020

Preprint

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show abstract

“…However, the use of a verbal memory task as a baseline condition in our experiment likely contributed to this observation; our paradigm is not tuned to detect contributions from semantic language networks towards math processing. Even with this caveat, our observation that the right IPL and STG participate in arithmetic processing networks represents new information to integrate into models of how the brain performs math calculations, consistent with recent reports using non-invasive imaging (Amalric and Dehaene 2019).…”

Section: Previous Human Electrophysiology Experiments Have Suggested supporting

confidence: 89%

Intracranial EEG reveals bihemispheric parietal and extra parietal brain networks supporting mental arithmetic

Umbach¹,

Rugg²,

Worrell

et al. 2019

Preprint

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Mathematical reasoning is central to everyday life. Lesion data and functional MRI studies suggest that even simple arithmetic involves the coordination of multiple spatially diverse brain regions, but to date, math processing has not been well characterized using direct brain recordings, especially outside of the parietal cortex. To address this, we utilized an unparalleled data set of 310 subjects implanted with intracranial electrodes to investigate the spatial and temporal dynamics of arithmetic reasoning. Our data support the importance of regions previously implicated in numerosity such as the superior parietal lobule and intraparietal sulcus. However, we also identify contributions to arithmetic processing from regions such as the entorhinal cortex and temporal pole. Using the excellent bihemispheric coverage afforded by our data set and the precise temporal resolution of intracranial recordings, we characterize in detail the spatial and temporal characteristics of an arithmetic processing network, quantifying subtle hemispheric differences for selected regions.We also examine how activity in these regions predicts arithmetic ability and look for gender differences in the pattern of network activation. Our findings further define the complex network of regions involved in human arithmetical reasoning.

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“…In two further studies, we sought to replicate the observed dissociation between mathematical and general semantic processing, and to probe the nature of the boundary between language and mathematical processes [14].…”

Section: (B) Control Studiesmentioning

confidence: 99%

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks

Amalric

Dehaene

2018

Phil. Trans. R. Soc. B

Self Cite

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Is mathematical language similar to natural language? Are language areas used by mathematicians when they do mathematics? And does the brain comprise a generic semantic system that stores mathematical knowledge alongside knowledge of history, geography or famous people? Here, we refute those views by reviewing three functional MRI studies of the representation and manipulation of high-level mathematical knowledge in professional mathematicians. The results reveal that brain activity during professional mathematical reflection spares perisylvian language-related brain regions as well as temporal lobe areas classically involved in general semantic knowledge. Instead, mathematical reflection recycles bilateral intraparietal and ventral temporal regions involved in elementary number sense. Even simple fact retrieval, such as remembering that 'the sine function is periodical' or that 'London buses are red', activates dissociated areas for math versus non-math knowledge. Together with other fMRI and recent intracranial studies, our results indicated a major separation between two brain networks for mathematical and non-mathematical semantics, which goes a long way to explain a variety of facts in neuroimaging, neuropsychology and developmental disorders.This article is part of a discussion meeting issue 'The origins of numerical abilities'.

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A distinct cortical network for mathematical knowledge in the human brain

Cited by 86 publications

References 47 publications

Comprehension of computer code relies primarily on domain-general executive brain regions

Comprehension of computer code relies primarily on domain-general executive brain regions

Intracranial EEG reveals bihemispheric parietal and extra parietal brain networks supporting mental arithmetic

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks

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