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Ozernov-Palchik O, O’Brien AM, Jiachen Lee E, Richardson H, Romeo R, Lipkin B, Small H, Capella J, Nieto-Castañón A, Saxe R, Gabrieli JDE, Fedorenko E. Precision fMRI reveals that the language network exhibits adult-like left-hemispheric lateralization by 4 years of age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594172. [PMID: 38798360 PMCID: PMC11118489 DOI: 10.1101/2024.05.15.594172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Left hemisphere damage in adulthood often leads to linguistic deficits, but many cases of early damage leave linguistic processing preserved, and a functional language system can develop in the right hemisphere. To explain this early apparent equipotentiality of the two hemispheres for language, some have proposed that the language system is bilateral during early development and only becomes left-lateralized with age. We examined language lateralization using functional magnetic resonance imaging with two large pediatric cohorts (total n=273 children ages 4-16; n=107 adults). Strong, adult-level left-hemispheric lateralization (in activation volume and response magnitude) was evident by age 4. Thus, although the right hemisphere can take over language function in some cases of early brain damage, and although some features of the language system do show protracted development (magnitude of language response and strength of inter-regional correlations in the language network), the left-hemisphere bias for language is robustly present by 4 years of age. These results call for alternative accounts of early equipotentiality of the two hemispheres for language.
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Affiliation(s)
- Ola Ozernov-Palchik
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Amanda M. O’Brien
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
| | - Elizabeth Jiachen Lee
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
| | - Hilary Richardson
- School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom
| | - Rachel Romeo
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD 20742, United States
| | - Benjamin Lipkin
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
| | - Hannah Small
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, 21218, United States
| | - Jimmy Capella
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | | | - Rebecca Saxe
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
| | - John D. E. Gabrieli
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
| | - Evelina Fedorenko
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
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Sueoka Y, Paunov A, Tanner A, Blank IA, Ivanova A, Fedorenko E. The Language Network Reliably "Tracks" Naturalistic Meaningful Nonverbal Stimuli. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:385-408. [PMID: 38911462 PMCID: PMC11192443 DOI: 10.1162/nol_a_00135] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/08/2024] [Indexed: 06/25/2024]
Abstract
The language network, comprised of brain regions in the left frontal and temporal cortex, responds robustly and reliably during language comprehension but shows little or no response during many nonlinguistic cognitive tasks (e.g., Fedorenko & Blank, 2020). However, one domain whose relationship with language remains debated is semantics-our conceptual knowledge of the world. Given that the language network responds strongly to meaningful linguistic stimuli, could some of this response be driven by the presence of rich conceptual representations encoded in linguistic inputs? In this study, we used a naturalistic cognition paradigm to test whether the cognitive and neural resources that are responsible for language processing are also recruited for processing semantically rich nonverbal stimuli. To do so, we measured BOLD responses to a set of ∼5-minute-long video and audio clips that consisted of meaningful event sequences but did not contain any linguistic content. We then used the intersubject correlation (ISC) approach (Hasson et al., 2004) to examine the extent to which the language network "tracks" these stimuli, that is, exhibits stimulus-related variation. Across all the regions of the language network, meaningful nonverbal stimuli elicited reliable ISCs. These ISCs were higher than the ISCs elicited by semantically impoverished nonverbal stimuli (e.g., a music clip), but substantially lower than the ISCs elicited by linguistic stimuli. Our results complement earlier findings from controlled experiments (e.g., Ivanova et al., 2021) in providing further evidence that the language network shows some sensitivity to semantic content in nonverbal stimuli.
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Affiliation(s)
- Yotaro Sueoka
- Department of Brain and Cognitive Sciences, Massachusetts Instititute of Technology, Cambridge, MA, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Alexander Paunov
- Department of Brain and Cognitive Sciences, Massachusetts Instititute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Instititute of Technology, Cambridge, MA, USA
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, Gif/Yvette, France
| | - Alyx Tanner
- McGovern Institute for Brain Research, Massachusetts Instititute of Technology, Cambridge, MA, USA
| | - Idan A. Blank
- Department of Psychology and Linguistics, University of California Los Angeles, Los Angeles, CA, USA
| | - Anna Ivanova
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Instititute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Instititute of Technology, Cambridge, MA, USA
- Program in Speech and Hearing Biosciences and Technology, Harvard University, Cambridge, MA, USA
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Mahowald K, Ivanova AA, Blank IA, Kanwisher N, Tenenbaum JB, Fedorenko E. Dissociating language and thought in large language models. Trends Cogn Sci 2024; 28:517-540. [PMID: 38508911 DOI: 10.1016/j.tics.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 03/22/2024]
Abstract
Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of separate mechanisms specialized for formal versus functional linguistic competence.
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Fedorenko E, Piantadosi ST, Gibson EAF. Language is primarily a tool for communication rather than thought. Nature 2024; 630:575-586. [PMID: 38898296 DOI: 10.1038/s41586-024-07522-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/03/2024] [Indexed: 06/21/2024]
Abstract
Language is a defining characteristic of our species, but the function, or functions, that it serves has been debated for centuries. Here we bring recent evidence from neuroscience and allied disciplines to argue that in modern humans, language is a tool for communication, contrary to a prominent view that we use language for thinking. We begin by introducing the brain network that supports linguistic ability in humans. We then review evidence for a double dissociation between language and thought, and discuss several properties of language that suggest that it is optimized for communication. We conclude that although the emergence of language has unquestionably transformed human culture, language does not appear to be a prerequisite for complex thought, including symbolic thought. Instead, language is a powerful tool for the transmission of cultural knowledge; it plausibly co-evolved with our thinking and reasoning capacities, and only reflects, rather than gives rise to, the signature sophistication of human cognition.
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Affiliation(s)
- Evelina Fedorenko
- Massachusetts Institute of Technology, Cambridge, MA, USA.
- Speech and Hearing in Bioscience and Technology Program at Harvard University, Boston, MA, USA.
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Fedorenko E, Ivanova AA, Regev TI. The language network as a natural kind within the broader landscape of the human brain. Nat Rev Neurosci 2024; 25:289-312. [PMID: 38609551 DOI: 10.1038/s41583-024-00802-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2024] [Indexed: 04/14/2024]
Abstract
Language behaviour is complex, but neuroscientific evidence disentangles it into distinct components supported by dedicated brain areas or networks. In this Review, we describe the 'core' language network, which includes left-hemisphere frontal and temporal areas, and show that it is strongly interconnected, independent of input and output modalities, causally important for language and language-selective. We discuss evidence that this language network plausibly stores language knowledge and supports core linguistic computations related to accessing words and constructions from memory and combining them to interpret (decode) or generate (encode) linguistic messages. We emphasize that the language network works closely with, but is distinct from, both lower-level - perceptual and motor - mechanisms and higher-level systems of knowledge and reasoning. The perceptual and motor mechanisms process linguistic signals, but, in contrast to the language network, are sensitive only to these signals' surface properties, not their meanings; the systems of knowledge and reasoning (such as the system that supports social reasoning) are sometimes engaged during language use but are not language-selective. This Review lays a foundation both for in-depth investigations of these different components of the language processing pipeline and for probing inter-component interactions.
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Affiliation(s)
- Evelina Fedorenko
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- The Program in Speech and Hearing in Bioscience and Technology, Harvard University, Cambridge, MA, USA.
| | - Anna A Ivanova
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Tamar I Regev
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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Liu YF, Wilson C, Bedny M. Contribution of the language network to the comprehension of Python programming code. BRAIN AND LANGUAGE 2024; 251:105392. [PMID: 38387220 DOI: 10.1016/j.bandl.2024.105392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024]
Abstract
Does the perisylvian language network contribute to comprehension of programming languages, like Python? Univariate neuroimaging studies find high responses to code in fronto-parietal executive areas but not in fronto-temporal language areas, suggesting the language network does little. We used multivariate-pattern-analysis to test whether the language network encodes Python functions. Python programmers read functions while undergoing fMRI. A linear SVM decoded for-loops from if-conditionals based on activity in lateral temporal (LT) language cortex. In searchlight analysis, decoding accuracy was higher in LT language cortex than anywhere else. Follow up analysis showed that decoding was not driven by presence of different words across functions, "for" vs "if," but by compositional program properties. Finally, univariate responses to code peaked earlier in LT language-cortex than in the fronto-parietal network. We propose that the language system forms initial "surface meaning" representations of programs, which input to the reasoning network for processing of algorithms.
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Affiliation(s)
- Yun-Fei Liu
- Department of Psychological and Brain Sciences, Johns Hopkins Universtiy, 232 Ames Hall, 3400 N. Charles Street, Baltimore, MD 21218, USA.
| | - Colin Wilson
- Department of Cognitive Science, Johns Hopkins University, 237 Krieger Hall, 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Marina Bedny
- Department of Psychological and Brain Sciences, Johns Hopkins Universtiy, 232 Ames Hall, 3400 N. Charles Street, Baltimore, MD 21218, USA
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Kuo CH, Prat CS. Computer programmers show distinct, expertise-dependent brain responses to violations in form and meaning when reading code. Sci Rep 2024; 14:5404. [PMID: 38443678 PMCID: PMC10914777 DOI: 10.1038/s41598-024-56090-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/01/2024] [Indexed: 03/07/2024] Open
Abstract
As computer programming becomes more central to the workforce, the need for better models of how it is effectively learned has become more apparent. The current study addressed this gap by recording electrophysiological brain responses as 62 Python programmers with varying skill levels read lines of code with manipulations of form (syntax) and meaning (semantics). At the group level, results showed that manipulations of form resulted in P600 effects, with syntactically invalid code generating more positive deflections in the 500-800 ms range than syntactically valid code. Meaning manipulations resulted in N400 effects, with semantically implausible code generating more negative deflections in the 300-500 ms range than semantically plausible code. Greater Python expertise within the group was associated with greater sensitivity to violations in form. These results support the notion that skilled programming, like skilled natural language learning, is associated with the incorporation of rule-based knowledge into online comprehension processes. Conversely, programmers at all skill levels showed neural sensitivity to meaning manipulations, suggesting that reliance on pre-existing semantic relationships facilitates code comprehension across skill levels.
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Affiliation(s)
- Chu-Hsuan Kuo
- Department of Psychology, University of Washington, Seattle, WA, USA.
| | - Chantel S Prat
- Department of Psychology, University of Washington, Seattle, WA, USA
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, USA
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Malik-Moraleda S, Jouravlev O, Taliaferro M, Mineroff Z, Cucu T, Mahowald K, Blank IA, Fedorenko E. Functional characterization of the language network of polyglots and hyperpolyglots with precision fMRI. Cereb Cortex 2024; 34:bhae049. [PMID: 38466812 PMCID: PMC10928488 DOI: 10.1093/cercor/bhae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 03/13/2024] Open
Abstract
How do polyglots-individuals who speak five or more languages-process their languages, and what can this population tell us about the language system? Using fMRI, we identified the language network in each of 34 polyglots (including 16 hyperpolyglots with knowledge of 10+ languages) and examined its response to the native language, non-native languages of varying proficiency, and unfamiliar languages. All language conditions engaged all areas of the language network relative to a control condition. Languages that participants rated as higher proficiency elicited stronger responses, except for the native language, which elicited a similar or lower response than a non-native language of similar proficiency. Furthermore, unfamiliar languages that were typologically related to the participants' high-to-moderate-proficiency languages elicited a stronger response than unfamiliar unrelated languages. The results suggest that the language network's response magnitude scales with the degree of engagement of linguistic computations (e.g. related to lexical access and syntactic-structure building). We also replicated a prior finding of weaker responses to native language in polyglots than non-polyglot bilinguals. These results contribute to our understanding of how multiple languages coexist within a single brain and provide new evidence that the language network responds more strongly to stimuli that more fully engage linguistic computations.
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Affiliation(s)
- Saima Malik-Moraleda
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA 02114, United States
| | - Olessia Jouravlev
- Department of Cognitive Science, Carleton University, Ottawa K1S 5B6, Canada
| | - Maya Taliaferro
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Zachary Mineroff
- Eberly Center, Carnegie Mellon University, Pittsburgh, PA 15289, United States
| | - Theodore Cucu
- Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15289, United States
| | - Kyle Mahowald
- Department of Linguistics, The University of Texas at Austin, Austin, TX 78712, United States
| | - Idan A Blank
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, United States
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA 02114, United States
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Bonilha AC, Ribeiro LW, Mapurunga M, Demarzo M, Ota F, Andreoni S, Ramos LR. Preventing cognitive decline via digital inclusion and virtual game management: an intervention study with older adults in the community. Aging Ment Health 2024; 28:268-274. [PMID: 37712842 DOI: 10.1080/13607863.2023.2258104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/06/2023] [Indexed: 09/16/2023]
Abstract
OBJECTIVE To evaluate the impact of cognitive stimulation via digital inclusion and the practice of video games on the cognition of the older population. METHOD This is a randomized controlled intervention study, nested in a population cohort study. Based on the application of the Clinical Dementia Rating (CDR) test, individuals aged 60 years or older with scores 0 and 0.5 were included and randomly allocated in the Intervention Group (IG) or Control Group (CG). Initially, 160 participants met the selection criteria and underwent neuropsychological evaluation via the Montreal Cognitive Assessment (MoCA), applied before and after intervention. The IG (n = 62) participated in computer-based intervention once a week for one-and-a-half hours, for 4 months. The CG (n = 47) participated in the mindfulness workshops held in the same period. RESULTS The digital literacy intervention group averaged 2.6 points more in the MoCA after 4 months. The change in the final MoCA decreased in 0.46 points at each unit in the basal MoCA. Individuals with average schooling had an increase of 0.93 points in the change of the MoCA in relation to individuals with low or high schooling. CONCLUSION Digital inclusion combined with the practice of video games has the potential to improve the cognition of the older population.
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Affiliation(s)
- Ana Cláudia Bonilha
- Department of Preventive Medicine, Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil
| | - Lemmy Willian Ribeiro
- Department of Preventive Medicine, Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil
| | - Marcelo Mapurunga
- Department of Preventive Medicine, Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil
| | - Marcelo Demarzo
- Department of Preventive Medicine, Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil
| | - Fábio Ota
- Department of Nursing, Universidade de São Paulo (USP), São Paulo, Brazil
| | - Solange Andreoni
- Department of Preventive Medicine, Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil
| | - Luiz Roberto Ramos
- Department of Preventive Medicine, Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil
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Olson HA, Chen EM, Lydic KO, Saxe RR. Left-Hemisphere Cortical Language Regions Respond Equally to Observed Dialogue and Monologue. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2023; 4:575-610. [PMID: 38144236 PMCID: PMC10745132 DOI: 10.1162/nol_a_00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 09/20/2023] [Indexed: 12/26/2023]
Abstract
Much of the language we encounter in our everyday lives comes in the form of conversation, yet the majority of research on the neural basis of language comprehension has used input from only one speaker at a time. Twenty adults were scanned while passively observing audiovisual conversations using functional magnetic resonance imaging. In a block-design task, participants watched 20 s videos of puppets speaking either to another puppet (the dialogue condition) or directly to the viewer (the monologue condition), while the audio was either comprehensible (played forward) or incomprehensible (played backward). Individually functionally localized left-hemisphere language regions responded more to comprehensible than incomprehensible speech but did not respond differently to dialogue than monologue. In a second task, participants watched videos (1-3 min each) of two puppets conversing with each other, in which one puppet was comprehensible while the other's speech was reversed. All participants saw the same visual input but were randomly assigned which character's speech was comprehensible. In left-hemisphere cortical language regions, the time course of activity was correlated only among participants who heard the same character speaking comprehensibly, despite identical visual input across all participants. For comparison, some individually localized theory of mind regions and right-hemisphere homologues of language regions responded more to dialogue than monologue in the first task, and in the second task, activity in some regions was correlated across all participants regardless of which character was speaking comprehensibly. Together, these results suggest that canonical left-hemisphere cortical language regions are not sensitive to differences between observed dialogue and monologue.
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Chen X, Affourtit J, Ryskin R, Regev TI, Norman-Haignere S, Jouravlev O, Malik-Moraleda S, Kean H, Varley R, Fedorenko E. The human language system, including its inferior frontal component in "Broca's area," does not support music perception. Cereb Cortex 2023; 33:7904-7929. [PMID: 37005063 PMCID: PMC10505454 DOI: 10.1093/cercor/bhad087] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 04/04/2023] Open
Abstract
Language and music are two human-unique capacities whose relationship remains debated. Some have argued for overlap in processing mechanisms, especially for structure processing. Such claims often concern the inferior frontal component of the language system located within "Broca's area." However, others have failed to find overlap. Using a robust individual-subject fMRI approach, we examined the responses of language brain regions to music stimuli, and probed the musical abilities of individuals with severe aphasia. Across 4 experiments, we obtained a clear answer: music perception does not engage the language system, and judgments about music structure are possible even in the presence of severe damage to the language network. In particular, the language regions' responses to music are generally low, often below the fixation baseline, and never exceed responses elicited by nonmusic auditory conditions, like animal sounds. Furthermore, the language regions are not sensitive to music structure: they show low responses to both intact and structure-scrambled music, and to melodies with vs. without structural violations. Finally, in line with past patient investigations, individuals with aphasia, who cannot judge sentence grammaticality, perform well on melody well-formedness judgments. Thus, the mechanisms that process structure in language do not appear to process music, including music syntax.
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Affiliation(s)
- Xuanyi Chen
- Department of Cognitive Sciences, Rice University, TX 77005, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Josef Affourtit
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rachel Ryskin
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive & Information Sciences, University of California, Merced, Merced, CA 95343, United States
| | - Tamar I Regev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Samuel Norman-Haignere
- Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States
| | - Olessia Jouravlev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive Science, Carleton University, Ottawa, ON, Canada
| | - Saima Malik-Moraleda
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
| | - Hope Kean
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rosemary Varley
- Psychology & Language Sciences, UCL, London, WCN1 1PF, United Kingdom
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
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Liu J, Li S, Liu M, Xu X, Zhang Y, Cheng J, Zhang W. Impaired brain networks functional connectivity after acute mild hypoxia. Medicine (Baltimore) 2022; 101:e30485. [PMID: 36197178 PMCID: PMC9509199 DOI: 10.1097/md.0000000000030485] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
This study aimed to analyze the changes in brain networks functional connectivity of pilots exposed to simulated hypoxia using resting-state functional magnetic resonance imaging (fMRI). A total of 35 healthy male pilots exposed to 14.5% oxygen concentration (corresponding to an altitude of 3000 m) underwent resting-state fMRI scans. The independent component analysis (ICA) approach was used to analyze changes in the resting-state brain networks functional connectivity of pilots after hypoxic exposure, and 9 common components in brain functional networks were identified. In the functional connections that showed significant group differences, linear regression was used to examine the association between functional connectivity and clinical characteristics. The brain networks functional connectivity after hypoxia exposure decreased significantly, including the left frontoparietal network and visual network 1-area, left frontoparietal network and visual network 2-area, right frontoparietal network and visual network 2-area, dorsal attention network and ventral attention network, dorsal attention network and auditory network, and ventral attention network and visual network 1-area. We found no correlation between the altered functional connectivity and arterial oxygen saturation level. Our findings provide insights into the mechanisms underlying hypoxia-induced cognitive impairment in pilots.
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Affiliation(s)
- Jie Liu
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shujian Li
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingxi Liu
- Department of Radiology, Chaoyang Hospital Affiliated to Capital Medical University, Beijing, China
| | - Xianrong Xu
- Department of Air Duty, Air Force General Hospital, Beijing, China
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- * Correspondence: Jingliang Cheng, Department of MRI, the First Affiliated Hospital of Zhengzhou University, 1 East Construction Road, Erqi District, Zhengzhou 450052, Henan Province, China (e-mail: )
| | - Wanshi Zhang
- Department of Radiology, Air Force General Hospital, Beijing, China
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Paunov AM, Blank IA, Jouravlev O, Mineroff Z, Gallée J, Fedorenko E. Differential Tracking of Linguistic vs. Mental State Content in Naturalistic Stimuli by Language and Theory of Mind (ToM) Brain Networks. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2022; 3:413-440. [PMID: 37216061 PMCID: PMC10158571 DOI: 10.1162/nol_a_00071] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 04/11/2022] [Indexed: 05/24/2023]
Abstract
Language and social cognition, especially the ability to reason about mental states, known as theory of mind (ToM), are deeply related in development and everyday use. However, whether these cognitive faculties rely on distinct, overlapping, or the same mechanisms remains debated. Some evidence suggests that, by adulthood, language and ToM draw on largely distinct-though plausibly interacting-cortical networks. However, the broad topography of these networks is similar, and some have emphasized the importance of social content / communicative intent in the linguistic signal for eliciting responses in the language areas. Here, we combine the power of individual-subject functional localization with the naturalistic-cognition inter-subject correlation approach to illuminate the language-ToM relationship. Using functional magnetic resonance imaging (fMRI), we recorded neural activity as participants (n = 43) listened to stories and dialogues with mental state content (+linguistic, +ToM), viewed silent animations and live action films with mental state content but no language (-linguistic, +ToM), or listened to an expository text (+linguistic, -ToM). The ToM network robustly tracked stimuli rich in mental state information regardless of whether mental states were conveyed linguistically or non-linguistically, while tracking a +linguistic / -ToM stimulus only weakly. In contrast, the language network tracked linguistic stimuli more strongly than (a) non-linguistic stimuli, and than (b) the ToM network, and showed reliable tracking even for the linguistic condition devoid of mental state content. These findings suggest that in spite of their indisputably close links, language and ToM dissociate robustly in their neural substrates-and thus plausibly cognitive mechanisms-including during the processing of rich naturalistic materials.
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Affiliation(s)
- Alexander M. Paunov
- Department of Brain and Cognitive Sciences, MIT, Cambridge, USA
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin Center, 91191Gif/Yvette, France
| | - Idan A. Blank
- Department of Brain and Cognitive Sciences, MIT, Cambridge, USA
- Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Olessia Jouravlev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Institute for Cognitive Science, Carleton University, Ottawa, ON, Canada
| | - Zachary Mineroff
- Department of Brain and Cognitive Sciences, MIT, Cambridge, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Eberly Center for Teaching Excellence & Educational Innovation, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jeanne Gallée
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA, USA
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Ivanova AA, Mineroff Z, Zimmerer V, Kanwisher N, Varley R, Fedorenko E. The Language Network Is Recruited but Not Required for Nonverbal Event Semantics. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2021; 2:176-201. [PMID: 37216147 PMCID: PMC10158592 DOI: 10.1162/nol_a_00030] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 01/07/2021] [Indexed: 05/24/2023]
Abstract
The ability to combine individual concepts of objects, properties, and actions into complex representations of the world is often associated with language. Yet combinatorial event-level representations can also be constructed from nonverbal input, such as visual scenes. Here, we test whether the language network in the human brain is involved in and necessary for semantic processing of events presented nonverbally. In Experiment 1, we scanned participants with fMRI while they performed a semantic plausibility judgment task versus a difficult perceptual control task on sentences and line drawings that describe/depict simple agent-patient interactions. We found that the language network responded robustly during the semantic task performed on both sentences and pictures (although its response to sentences was stronger). Thus, language regions in healthy adults are engaged during a semantic task performed on pictorial depictions of events. But is this engagement necessary? In Experiment 2, we tested two individuals with global aphasia, who have sustained massive damage to perisylvian language areas and display severe language difficulties, against a group of age-matched control participants. Individuals with aphasia were severely impaired on the task of matching sentences to pictures. However, they performed close to controls in assessing the plausibility of pictorial depictions of agent-patient interactions. Overall, our results indicate that the left frontotemporal language network is recruited but not necessary for semantic processing of nonverbally presented events.
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Affiliation(s)
- Anna A. Ivanova
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zachary Mineroff
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vitor Zimmerer
- Division of Psychology and Language Sciences, University College London, London, UK
| | - Nancy Kanwisher
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rosemary Varley
- Division of Psychology and Language Sciences, University College London, London, UK
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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Expert Programmers Have Fine-Tuned Cortical Representations of Source Code. eNeuro 2021; 8:ENEURO.0405-20.2020. [PMID: 33318072 PMCID: PMC7877476 DOI: 10.1523/eneuro.0405-20.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/14/2020] [Accepted: 12/01/2020] [Indexed: 11/30/2022] Open
Abstract
Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many studies have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain of programmers are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on a source-code categorization task. Our results suggest that programming expertise is built on fine-tuned cortical representations specialized for the domain of programming.
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Ivanova AA, Srikant S, Sueoka Y, Kean HH, Dhamala R, O'Reilly UM, Bers MU, Fedorenko E. Comprehension of computer code relies primarily on domain-general executive brain regions. eLife 2020; 9:e58906. [PMID: 33319744 PMCID: PMC7738192 DOI: 10.7554/elife.58906] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 11/06/2020] [Indexed: 12/22/2022] Open
Abstract
Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.
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Affiliation(s)
- Anna A Ivanova
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Shashank Srikant
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Yotaro Sueoka
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Hope H Kean
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Riva Dhamala
- Eliot-Pearson Department of Child Study and Human Development, Tufts UniversityMedfordUnited States
| | - Una-May O'Reilly
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Marina U Bers
- Eliot-Pearson Department of Child Study and Human Development, Tufts UniversityMedfordUnited States
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
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