The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index; none of the 39 sources its claims cite is checked for support yet.
Functional specificity for high-level linguistic processing in the human brain
Open accessAbstract
Neuroscientists have debated for centuries whether some regions of the human brain are selectively engaged in specific high-level mental functions or whether, instead, cognition is implemented in multifunctional brain regions. For the critical case of language, conflicting answers arise from the neuropsychological literature, which features striking dissociations between deficits in linguistic and nonlinguistic abilities, vs. the neuroimaging literature, which has argued for overlap between activations for linguistic and nonlinguistic processes, including arithmetic, domain general abilities like cognitive control, and music. Here, we use functional MRI to define classic language regions functionally in each subject individually and then examine the response of these regions to the nonlinguistic functions most commonly argued to engage these regions: arithmetic, working memory, cognitive control, and music. We find little or no response in language regions to these nonlinguistic functions. These data support a clear distinction between language and other cognitive processes, resolving the prior conflict between the neuropsychological and neuroimaging literatures.
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Written from the paper’s full text.
TL;DR
Using fMRI, the authors functionally defined language regions in each subject and tested whether those regions respond to arithmetic, working memory, cognitive control, or music. They found little or no response to these nonlinguistic functions, supporting a clear distinction between language and other cognitive processes.
Key findings
- Across seven nonlinguistic tasks, language-sensitive ROIs showed little or no response; no other ROIs showed reliable responses to any nonlinguistic task after correction, except one left middle frontal gyrus (LMFG) ROI that responded significantly to verbal working memory.
- The music contrast produced some response in several language ROIs, but effects were weak overall and none survived correction for multiple comparisons.
- Whole-brain overlap analyses at the ROI-analysis threshold found no spatially systematic regions where language activations overlapped with any of the seven nonlinguistic tasks; with liberal thresholding, three overlap regions emerged, including posterior left inferior frontal gyrus (LIFG) overlap with verbal working memory in 85% of subjects and a similar region with Stroop in 71%.
- The authors conclude that most key cortical regions for high-level linguistic processing are not engaged by mental arithmetic, general working memory, cognitive control, or musical processing, with the LMFG as the main exception.
Methods
- Forty-eight right-handed native English speakers (31 females, ages 18–40) participated; each was run on a language localizer and one or more nonlinguistic tasks. Task-specific samples were: math n = 11, spatial WM n = 12, verbal WM n = 13, MSIT n = 15, vMSIT n = 12, Stroop n = 14, and music n = 12, with overlapping subjects across tasks.
- The language localizer contrasted reading sentences vs. pronounceable nonwords with a memory probe; nonlinguistic experiments contrasted harder vs. easier conditions for arithmetic, spatial WM, verbal WM, MSIT, vMSIT, and Stroop, and intact vs. scrambled music.
- fMRI data were collected on a 3-T Siemens Trio, analyzed with SPM5 and custom MATLAB scripts; language ROIs were defined per subject by intersecting group-level parcels with individual localizer maps thresholded at P < 0.001 uncorrected, and responses were estimated using cross-validation across runs.
Limitations
- The paper states that the investigated regions are not the only ones engaged in or necessary for language: language also requires auditory/visual and motor cortex, and domain-general frontal/parietal regions may be necessary for many linguistic functions.
- The paper states that the computations in the posterior LIFG overlap region remain an open question and that higher spatial resolution might reveal the apparent overlap to be artifactual due to proximity of language-sensitive and demanding-task regions.
- The paper notes that the localizer targets high-level linguistic processing and does not address other aspects of language such as sound-level processes; evident from the text, the whole-brain overlap findings required liberal thresholding, and per-task samples were small (11–15 participants) with overlapping subjects across experiments.
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Claims the paper makes
21 Own result · 14 Not checked yet · 12 Own interpretation · 4 Own method · 4 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the response to intact music is barely above the fixation baseline (and lower than the response to the language localizer control task)”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the threshold used for the ROI analyses, no spatially systematic regions were detected in which language activations overlapped with any of the seven nonlinguistic tasks, confirming the overall picture of language specificity found in the ROI analyses.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the response to intact music is barely above the fixation baseline (and lower than the response to the language localizer control task)”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The current results show that most of the key cortical regions engaged in high-level linguistic processing are not engaged by mental arithmetic, general working memory, cognitive control, or musical processing, indicating a high degree of functional specificity in the brain regions that support language.”
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“[This threshold is not too stringent to discover true overlap when it exists; at the same threshold, we discovered robust overlap regions in the frontal and parietal lobes between Stroop and the multisource interference task (MSIT) cognitive control task in a subset of the subjects run on both tasks.]”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“One frontal language ROI left middle frontal gyrus (LMFG) showed a significant response to verbal working memory after correcting for the number of ROIs; this region is, thus, not specialized for sentence understanding.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“we find no hint of activation for either of the other two cognitive control tasks [the MSIT task (39) and its variant, verbal MSIT (vMSIT)]”
What the sources say
- Not checked yetcites [39] Bush G, Shin LM. The Multi-Source Interference Task: An fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network. Nat Protoc. 2006;1:308–313. doi: 10.1038/nprot.2006.48. [DOI] [PubMed] [Google Scholar] source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Furthermore, although this region does respond more strongly to intact than scrambled musical stimuli (consistent with some previous claims) (21), the response to intact music is barely above the fixation baseline (and lower than the response to the language localizer control task)”
What the sources say
- Not checked yetcites [21] Maess B, Koelsch S, Gunter TC, Friederici AD. Musical syntax is processed in Broca's area: An MEG study. Nat Neurosci. 2001;4:540–545. doi: 10.1038/87502. [DOI] [PubMed] [Google Scholar] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The third region, present in 85% of the subjects, emerged in the posterior part of our left inferior frontal gyrus (LIFG) language ROI and responded to both language and verbal WM”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The music contrast did produce some response in several language ROIs (SI Text), suggesting possibly overlapping circuits; however, these effects were weak overall, and none survived correction for multiple comparisons.”
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 10
- Probably not a substantial claim. 8
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The music contrast did produce some response in several language ROIs (SI Text), suggesting possibly overlapping circuits; however, these effects were weak overall, and none survived correction for multiple comparisons.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nevertheless, a complementary analysis shows that a portion of the LIFG language ROI is selective for high-level linguistic processing and not engaged in either verbal WM or Stroop (Fig. 4).”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the activation patterns were similar across the two tasks, we collapsed the data from these two groups of participants for the current analyses.”
plainn = 4- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that the brain regions investigated here are surely not the only ones engaged in or necessary for language processing.”
boosted- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The order of conditions was counterbalanced; each condition was equally likely to appear in the earlier vs. later parts of each run and was as likely to follow every other condition as it was to precede it.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The resulting piece had component sounds similar to the intact music but lacked musical structure.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These data support a clear distinction between language and other cognitive processes, resolving the prior conflict between the neuropsychological and neuroimaging literatures.”
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, ROI analyses can obscure functional heterogeneity within an ROI or miss important overlapping regions outside the borders of the ROI.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of course, the question of the relationship between linguistic and nonlinguistic processes extends to other aspects of language (e.g., sound-level processes), which we are not targeting with the current language localizer task.”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The responses to the sentences and nonwords conditions were estimated using cross-validation across the functional runs, and therefore, the data used to estimate the effects were always independent of the data used for ROI definition (46).”
What the sources say
- Not checked yetcites [46] Vul E, Kanwisher N. Begging the question: The non-independence error in fMRI data analysis. In: Hanson S, Bunzl M, editors. Foundational issues for human brain mapping. Cambridge, MA: MIT Press; 2010. pp. 71–91. [Google Scholar] source
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Probably not a substantial claim.
In the paper
“and to be robust to changes in stimulus modality (visual/auditory) (see also ref. 32), specific stimuli, and task (passive reading vs. reading with a probe task, which is used here).”
What the sources say
- Not checked yetcites [32] Braze D, et al. Unification of sentence processing via ear and eye: An fMRI study. Cortex. 2011;47:416–431. doi: 10.1016/j.cortex.2009.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar] source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“However, the neuroimaging claims are based largely on observations of activation for a nonlanguage task in or near a general brain region previously implicated in language (e.g., Broca's area), without a direct demonstration of overlap of linguistic and nonlinguistic activations in the same subjects.”
What the sources say
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plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ROI-based analyses afford great statistical power and reduce the blurring of functional activations inevitable in group analyses.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“(here and throughout the study, responses were estimated using data from left-out runs not used for defining the ROIs).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
The individual-subject overlap analysis strategy has almost never been applied in neuroimaging investigations of high-level linguistic processing.
“a data analysis strategy that has almost never been applied in neuroimaging investigations of high-level linguistic processing”
The paper relies on this without support.
Most claims about the overlap between linguistic and nonlinguistic processes have concerned syntactic processing.
“However, most claims about the overlap between linguistic and nonlinguistic processes have concerned (i) syntactic processing, which is included in our functional contrast, and (ii) brain regions, which are robustly activated by our localizer (e.g., Broca's area).”
The paper relies on this without support.
Most claims about the overlap between linguistic and nonlinguistic processes have concerned brain regions robustly activated by the language localizer.
“However, most claims about the overlap between linguistic and nonlinguistic processes have concerned (i) syntactic processing, which is included in our functional contrast, and (ii) brain regions, which are robustly activated by our localizer (e.g., Broca's area).”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The hard > easy contrast has been found to robustly activate regions of the MD network (Fedorenko et al., 2011(Fedorenko et al., , 2013)).”
From Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation (2018) · cites this paper as Fedorenko et al., 2011
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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 Fedorenko et al., 2011
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“There is little doubt that perisylvian regions are more involved in processing language inputs than some other brain systems (see, e.g., Blank & Fedorenko, 2017), and may be particularly involved in language tasks (e.g., Fedorenko, Behr, & Kanwisher, 2011).”
From Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension (2018) · cites this paper as Fedorenko, Behr, & Kanwisher, 2011
In the citing paper
“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 [37]
In the citing paper
“is sensitive to linguistic regularities at multiple levels: from phonological/sub-lexical [36] to phrase/sentence level [37, 38]”
From Dissociating language and thought in large language models (2024) · cites this paper as [37]
Citations
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“…also showed less overlap with the language network, by either having less anatomical overlap with the established functional language localizer map or showing anatomical overlap with superior frontal and posterior temporal brain areas less reliably activated in language studies 9,10,16,17,25,26 .”
BackgroundMethodResultInfluentialBrain-Like Language Processing via a Shallow Untrained Multihead Attention NetworkPreprint
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“…day it was and out on the wide ... remarkable selectivity for language processing compared to various non-linguistic inputs and tasks, such as music perception (Fedorenko et al., 2012a; Chen et al., 2023) or arithmetic computation (Fedorenko et al., 2011) (for review, see (Fedorenko et al., 2024)).”
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“…that the ROIs selected for the present study do form part of the more general “ language network ” , highlighted by extensive fMRI research (e.g., Fedorenko et al., 2011; Hagoort, 2014; Price, 2012) and confirming the picture already painted by lesion studies in the 19th century (Broca, 1861;…”
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Dunagan, D., Zhang, S., Li, J., et al. (2021) · bioRxiv · doi:10.1101/2021.05.11.443670 · Cited by 10
Reorganized language network connectivity after left arcuate fasciculus resection: A case study
Chernoff, B. L., Teghipco, A., Garcea, F. E., Belkhir, J. R., Sims, M. H., Paul, D. A., Tivarus, M. E., Smith, S. O., Hintz, E. B., Pilcher, W. H., et al. (2019) · Cortex · doi:10.1016/j.cortex.2019.07.022 · Cited by 12
Johnson, L., Fitzhugh, M. C., Yi, Y., Mickelsen, S., Baxter, L. C., Howard, P., & Rogalsky, C. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01626 · Cited by 10
“…with numerous previous neuroimaging studies of L1 spoken languages that report greater Broca’s area activation for reading sentences than word lists (Fedorenko et al., 2010, 2011, 2012a,b,c; Fedorenko and Kanwisher, 2011; Blank et al., 2016), as well as with previous neuroimaging and…”
MethodResultYang, L., Gao, C., Wang, P., Friederici, A. D., Zaccarella, E., & Chen, L. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1151518 · Cited by 12
Linguistic network in early deaf individuals: A neuroimaging meta-analysis
Yang, T., Fan, X., Hou, B., Wang, J., & Chen, X. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120720 · Cited by 13
“This cluster has a high overlap with the language-selective region within Broca ’ s area (Fedorenko et al., 2011; Fedorenko and Blank, 2020).”
BackgroundMethodWu, J.-J., Cheng, Y., Qu, X., Kang, T.-M., Cai, Y., Wang, P., Zaccarella, E., Friederici, A., Hartwigsen, G., & Chen, L.-Y. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00140 · Cited by 9
“However, the causal relevance of LpIFG for syntactic processes remains largely unclear (Blank & Fedorenko, 2017; Buchsbaum et al., 2005; Diachek et al., 2020; Fedorenko et al., 2011; Hickok et al., 2003; Santi & Grodzinsky, 2007b, 2010).”
BackgroundSemantic systems are mentalistically activated for and by social partners
Forgács, B., Gervain, J., Parise, E., Gergely, G., Elek, L. P., Üllei-Kovács, Z., & Király, I. (2022) · Scientific Reports · doi:10.1038/s41598-022-08306-w · Cited by 11
“Based on prior research mean ERP amplitudes were calculated for the N400 in the 300–500 ms time window5 over a centroparietal Region-of-Interest (ROI) including 18 electrodes (31, 37, 53, 54, 55, 60, 61, 62, 67, 72, 77, 78, 79, 80, 85, 86, 87, REF); for the later frontal and parietal effects in the 600–800 time window…”
MethodMusical and linguistic syntactic processing in agrammatic aphasia: An ERP study
Chiappetta, B., Patel, A. D., & Thompson, C. K. (2021) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2021.101043 · Cited by 10
Stimulating parietal regions of the multiple-demand cortex impairs novel vocabulary learning
Sliwinska, M. W., Elson, R., & Pitcher, D. (2021) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2021.108047 · Cited by 10
Bilinguals on the garden-path: Individual differences in syntactic ambiguity resolution
Brothers, T. A., Hoversten, L. J., & Traxler, M. J. (2021) · Bilingualism Language and Cognition · doi:10.1017/s1366728920000711 · Cited by 15
Djokic, V. G., Maillard, J., Bulat, L., & Shutova, E. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1508 · Cited by 5
“, lexicalsemantics/syntax) (Fedorenko et al., 2011).”
Roncaglia-Denissen, M. P., Bouwer, F. L., & Honing, H. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00038 · Cited by 11
“Despite plenty supporting experimental evidence, recent studies have challenged the view that language and music make use of shared brain areas for their syntactic processing (Fedorenko et al., 2011; Fitch and Martins, 2014) or a common syntactic parser (Perruchet and Poulin-Charronnat, 2013; Bigand et al.”
BackgroundMcNab, F., Hillebrand, A., Swithenby, S., & Rippon, G. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00273 · Cited by 8
“It is possible that more contemporary approaches to fMRI analysis may allow a similar demonstration of functional specificity at the spatial level (Fedorenko et al., 2011) but this would still not offer the temporal segregation that can be offered by EEG/MEG techniques, nor the insights offered by examining oscillator…”
BackgroundInfluentialConcept Nativism and Neural Plasticity
Laurence, S., & Margolis, E. (2015) · The MIT Press eBooks · doi:10.7551/mitpress/9383.003.0010 · Cited by 67
Enhancing Few-Shot Vision-Language Classification With Large Multimodal Model Features
Mitra, C., Huang, B., Chai, T., Lin, Z., Arbelle, A., Feris, R., Karlinsky, L., Darrell, T., Ramanan, D., & Herzig, R. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00265 · Cited by 16
“One source of inspiration for our method is long-standing work in the field of neuroscience that suggests certain areas of the brain are reserved for specific tasks [16, 36] (i.e. functional specificity).”
He, Z., Achterberg, J., Collins, K., Nejad, K. K., Akarca, D., Yang, Y., Gurnee, W., Sucholutsky, I., Tang, Y., Vitanov, R. I., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.12664 · Cited by 14
“…selectively recruits the fusiform face area [Kanwisher et al., 1997], language processing relies on a set of specialized regions called the language network [Fedorenko et al., 2011], and theory of mind reasoning relies on an area in the temporo-parietal junction [Saxe and Kanwisher, 2003].”
BackgroundNo evidence for a special role of language in feature-based categorizationPreprint
Benn, Y., Ivanova, A. A., Clark, O. J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.18.436075 · Cited by 6
“This evidence includes the lack of activity in the language brain regions during non-linguistic tasks that allegedly require language (e.g., Amalric & Dehaene, 2016, 2019; Fedorenko et al., 2011; Ivanova et al., 2021; Monti et al., 2009, 2012), the retained ability of some individuals with aphasia to perform such task…”
BackgroundSelf-citationKumari, A., Shrivastava, N., Mishra, M., Somvanshi, P., & Grover, A. (2021) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2021.103612 · Cited by 8
Cortical collateralization induced by language and arithmetic in non-right-handers
Nakai, T., & Okanoya, K. (2019) · Cortex · doi:10.1016/j.cortex.2019.11.009 · Cited by 9
The many timescales of context in language processing
Ryskin, R. A., & Fang, X. (2021) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/bs.plm.2021.08.001 · Cited by 9
Graded sensitivity to structure and meaning throughout the human language networkPreprint
Shain, C., Kean, H., Casto, C., Lipkin, B., Affourtit, J., Siegelman, M., Mollica, F., & Fedorenko, E. G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.12.467812 · Cited by 12
“However, the empirical literature that is used to support this position (from both neuroimaging and neuropsychology) widely assumes that spatial coordinates in the brain implement the same function across individuals—an assumption that is known to be incorrect for the language system and to lead systematically both to…”
BackgroundSelf-citationA Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search
Ellis-Mohr, A. R., Nayak, A., & Varshney, L. (2025) · Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · doi:10.48550/arxiv.2507.00004 · Cited by 7
Malik-Moraleda, S., Jouravlev, O., Taliaferro, M., Mineroff, Z., Cucu, T., Mahowald, K., Blank, I., & Fedorenko, E. G. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae049 · Cited by 32
Self-citationSun, J., Zhang, X., & Moens, M. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230524 · Cited by 6
“LSTG, LMFG, and LIFG contain acknowledged important components of the brain language network [7].”
BackgroundAdaptive ICA for Speech EEG Artifact Removal
Koelbl, N., Schilling, A., & Krauss, P. (2023) · 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART) · doi:10.1109/biosmart58455.2023.10162054 · Cited by 7
“In recent decades, the large progress in fMRI (functional magnetic resonance imaging) technology has led to major breakthroughs in neurolinguistics research [2], [3].”
BackgroundHemispheric dissociations in regions supporting auditory sentence comprehension in older adults
Lee, Y. S., Rogers, C. S., Grossman, M., Wingfield, A., & Peelle, J. E. (2022) · Aging Brain · doi:10.1016/j.nbas.2022.100051 · Cited by 7
Neural Redundancy and Its Relation to Neural Reuse
Zerilli, J. (2019) · Philosophia Scientiæ · doi:10.1086/705529 · Cited by 7
How thalamic relays might orchestrate supervised deep training and symbolic computation in the brainPreprint
Hayworth, K. J., & Marblestone, A. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/304980 · Cited by 4
“…responses in higher cortical areas show specializations for object shape, face identity, place, and many other properties (e.g., (Deen et al., 2017; Fedorenko et al., 2011; Saygin et al., 2016; Vul et al., 2012)), and that focal lesions to these areas produce correspondingly specific cognitive…”
BackgroundThe “grammar” in Universal Grammar: A biolinguistic clarification
Trettenbrein, P. C. (2015) · doi:10.1515/qal-2015-0005 · Cited by 7
“…has been localised to BA 44 does not imply that this area is not a crucial part of the syntactic network, uniquely involved in processing language (Fedorenko, Behr, and Kanwisher, 2011) and especially hierarchical structure (Musso et al., 2003), independent of other cognitive functions such as…”
BackgroundPatients with focal cerebellar lesions show reduced auditory cortex activation during silent reading
Moberget, T., Hilland, E., Andersson, S., Lundar, T., Due‐Tønnessen, B. J., Heldal, A., Ivry, R. B., & Endestad, T. (2015) · Brain and Language · doi:10.1016/j.bandl.2015.08.004 · Cited by 6
Syntactic Structures as Descriptions of Sensorimotor Processes
Knott, A. (2014) · Biolinguistics · doi:10.5964/bioling.8991 · Cited by 7
Parallel systems for social and spatial cognition reaching the cortical apex
Deen, B., & Freiwald, W. A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2520067122 · Cited by 48
“These networks have also been found to be distinct from a third, spatially adjacent system sensitive to linguistic content ( 27 , 28 ).”
Schneider, J. M., Scott, T. L., Legault, J., & Qi, Z. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae123 · Cited by 10
Tripathi, V., Devaney, K. J., Lazar, S. W., & Somers, D. C. (2024) · Mindfulness · doi:10.1007/s12671-024-02316-7 · Cited by 7
“The language network—which spans regions within the temporal, lateral frontal, and medial cortices—is associated with the processing, production, and comprehension of language (Fedorenko et al., 2011).”
The language network reliably ‘tracks’ naturalistic meaningful non-verbal stimuliPreprint
Sueoka, Y., Paunov, A. M., Ivanova, A. A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.24.489316 · Cited by 11
“…2019), music (e.g., Fedorenko et al., 2011; Rogalsky et al., 2011; Chen et al., 2023), logic (e.g., Monti et al., 2009), executive control (e.g., Fedorenko et al., 2011; Mineroff, Blank et al., 2018), action/gesture observation (e.g., Pritchett et al., 2018; Jouravlev et al., 2019), Theory of…”
BackgroundInfluentialSelf-citationA systematic review of neuroimaging approaches to mapping language in individuals
Bajracharya, A., & Peelle, J. (2019) · Journal of Neurolinguistics · doi:10.31234/osf.io/7aru3 · Cited by 6
On the domain‐specificity of the visual and non‐visual face‐selective regions
Axelrod, V. (2016) · European Journal of Neuroscience · doi:10.1111/ejn.13290 · Cited by 6
“We used the reading task as a test case for number of reasons: on one the hand, the reading of sentences engages rich and relatively non-specialized cognitive processing (but see: Fedorenko et al., 2011); on other hand, this task is unrelated to the processing of faces; in addition, Table 3.”
MethodThe role of disrupted functional connectivity in aphasia
Meier, E. L. (2022) · Handbook of clinical neurology · doi:10.1016/b978-0-12-823384-9.00005-0 · Cited by 10
Neural correlates of second-language communication and the effect of language anxiety
Jeong, H., Sugiura, M., Suzuki, W., Sassa, Y., Hashizume, H., & Kawashima, R. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.02.012 · Cited by 49
“Alternatively, the left middle frontal gyrus has been associated with verbal working memory for sentence processing (Fedorenko et al., 2011).”
Malik-Moraleda, S., Cucu, T., Lipkin, B., & Fedorenko, E. G. (2021) · Neurobiology of Language · doi:10.1162/nol_a_00058 · Cited by 7
“Given the well-documented interindividual variability in the precise locations of the MD and language areas (e.g., Fedorenko et al., 2011, 2013; Shashidhara et al., 2020), an anatomical location cannot be used to interpret an effect as arising within the MD network vs. the language network.”
BackgroundSelf-citationBayesian modelling disentangles language versus executive control disruption in stroke
Hartwigsen, G., Lim, J., Bae, H., Yu, K., Kuijf, H. J., Weaver, N. A., Biesbroek, J. M., Kopál, J., & Bzdok, D. (2024) · Brain Communications · doi:10.1093/braincomms/fcae129 · Cited by 6
“In contrast, the overlap Correlations of factors with maps of the language network and multiple-demand network 47,48 show a relatively stronger overlap of executive speech functions and language with the language network.”
BackgroundMethodAttention weights accurately predict language representations in the brain
(2022) · doi:10.1101/2022.12.07.519480 · Cited by 5
“…that have previously been associated with language representations: the high-level auditory cortex (AC), the superior temporal sulcus (STS), Broca’s area and the superior ventral premotor speech area (sPMv) (Fedorenko et al., 2011; Huth et al., 2016; de Heer et al., 2017; Deniz et al., 2019).”
BackgroundSelective Attention, Processing Load, and Semantics
Petten, C. K. V. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398451-7.00019-1 · Cited by 8
Semantic reasoning takes place largely outside the language networkPreprint
Ivanova, A. A., Kauf, C., Gao, R., She, J. S., Kean, H., Goldhaber, T., Nieto-Castañón, A., Varley, R. A., Kanwisher, N., & Fedorenko, E. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.07.692873 · Cited by 5
“…al., 2021; Rapoport et al., 2000; Schmahmann, 2019; Schmahmann et al., 2019; Strick et al., 2009) and language in particular (Casto et al, 2025 b; Fedorenko et al., 2011; Highnam & Bleile, 2011; LeBel et al., 2021; Mariën & Borgatti, 2018; Smet et al., 2007; Starowicz-Filip et al., 2017), as…”
Self-citationGao, C., Wu, J., Cheng, Y., Ke, Y., Qu, X., Yang, M., Hartwigsen, G., & Chen, L. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121014 · Cited by 6
Bogdanov, M., Bustamante, L., Devine, S., Sheldon, S., & Otto, A. (2024) · Journal of Neuroscience · doi:10.1101/2024.09.27.615545 · Cited by 5
“…performance (accuracy, p = 0.302; log RTs, p = 0.372), nor an interaction between trial type and tDCS condition (accuracy, p = 0.466; log RTs, p = 0.127), indicating that performance did not di ff er meaning-fully between brain stimulation conditions (for full coe ffi cient estimates, see Table 4).”
InfluentialMartin, R. C., Yue, Q., Zahn, R., & Lu, Y. (2024) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-024-01217-5 · Cited by 6
“A network of frontal, temporal, and parietal regions, principally in the left hemisphere, have been identified to be involved in sentence processing (Fedorenko et al., 2011; Matchin & Hickok, 2020; Uddén et al., 2022).”
Clercq, P. D., Gonsalves, A. R., Gerrits, R., & Vandermosten, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.12.575350 · Cited by 8
“These results are shown in Figure 5A. Healthy controls showed significant activation in these voxels for the left (V=66, p=0.001) and right hemisphere (V=63, p=0.003).”
BackgroundResultInfluentialCan ChatGPT help researchers understand how the human brain handles language?
Waldrop, M. (2024) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2410196121 · Cited by 5
“According to advanced fMRI techniques pioneered more than a decade ago (5), “the system responds in the same way to speaking, writing—all the kinds of languages a person knows and speaks, including sign languages,” says psychologist Anna Ivanova at the Georgia Institute of Technology, who is a…”
BackgroundBrain areas for reversible symbolic reference, a potential singularity of the human brain
van Kerkoerle, T., Pape, L., Ekramnia, M., Feng, X., Tasserie, J., Dupont, M., Li, X., Jarraya, B., Vanduffel, W., Dehaene, S., et al. (2023) · eLife · doi:10.7554/elife.87380.3 · Cited by 7
“…multiple parallel and partially dissociable human brain networks possess symbolic abilities and deploy them in different domains such as natural language, music and mathematics (Amalric and Dehaene, 2017; Chen et al., 2021; Dehaene et al., 2022; Fedorenko et al., 2011; Fedorenko and Varley, 2016).”
Neural Mechanisms Underlying the Dynamic Updating of Native Language
Sharer, K., & Thothathiri, M. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00023 · Cited by 8
“Fedorenko and colleagues too have reported weak Stroop effects in some language-specific functional ROIs in some studies (Fedorenko et al., 2011; Fedorenko et al., 2012).”
BackgroundNeurolinguistic Studies of Sentence Comprehension
Skeide, M. A., & Friederici, A. D. (2017) · doi:10.1002/9781118829516.ch19 · Cited by 4
Are Neural Substrates of Language and Communication Distinct?
Rytel, J. (2014) · Psychology of Language and Communication · doi:10.2478/plc-2014-0013 · Cited by 7
Ren, L., Lv, M., Wang, X., Schwieter, J. W., & Liu, H. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae356 · Cited by 4
Li, A., Chen, C., Wu, X., Feng, Y., Yang, J., Feng, X., Hu, R., & Mei, L. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120923 · Cited by 5
“The non-verbal task is a spatial working memory task (Fedorenko et al., 2011) that demands more visuospatial attention than does the verbal task.”
Another look at the universal grammar hypothesis: Commentary on Evans 2014
Goldberg, A. E. (2016) · Language · doi:10.1353/lan.2016.0018 · Cited by 4
Shain, C., & Fedorenko, E. G. (2026) · Nature Communications · doi:10.1038/s41467-026-75745-8 · Cited by 17
Self-citationThe emergence of the language system in the toddler brainPreprint
Olson, H. A., Chen, E. M., Osumah, C., Du, H., Fedorenko, E. G., & Saxe, R. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.707550
Self-citationA 3.5-minute-long reading-based fMRI localizer for the language network
Tuckute, G., Lee, E. J., Sathe, A., et al. (2026) · bioRxiv · doi:10.1101/2024.07.02.601683 · Cited by 4
“…(as defined by standard versions of the language localizer task) are highly selective for language relative to diverse non-linguistic inputs and tasks (e.g., Fedorenko et al., 2011; Ivanova et al., 2020, 2021; Chen et al., 2023; for reviews, see Fedorenko & Blank, 2020; Fedorenko et al., 2024).”
InfluentialSelf-citationThe detection of algebraic auditory structures emerges with self-supervised learning
Orhan, P., Boubenec, Y., & King, J.-R. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013271 · Cited by 5
“This possibility, however, would need to reconcile the fact that mathematical, rather than language areas, seem to represent the algebraic auditory patterns [8,53].”
Si, K., Zhang, Y., & Zhang, D. (2025) · Discover Sustainability · doi:10.1007/s43621-025-01447-8 · Cited by 8
“At the same time, the rise in “mathematics” (3.76) underscores how the abstract logic of mathematical notation clashes with the linear structure of natural language—an issue that warrants targeted empirical research on L2 authors [114–116].”
InfluentialSurprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly
Lin, Y.-C., & Schuler, W. (2025) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.48550/arxiv.2506.11338 · Cited by 4
“This dataset includes the time series of BOLD signals of several functional regions of interest (fROIs) in the language network (Fedorenko et al., 2011), which were identified with a localizer task.”
Lin, Y.-C., Zhu, H.-G., & Schuler, W. (2025) · arXiv.org · doi:10.48550/arxiv.2505.12196 · Cited by 4
Billot, A., Jhingan, N., Varkanitsa, M., Wolna, A., Shain, C., Blank, I., Ryskin, R. A., Kiran, S., & Fedorenko, E. G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.23.619954 · Cited by 5
“This task has been shown to be robust to the materials, task, and modality of presentation 73,112,192,193 .”
Self-citationOlson, H. A., Johnson, K. T., Nishith, S., Frosch, I. R., Gabrieli, J. D. E., & D’Mello, A. M. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00339 · Cited by 4
“…narratives increased activation more than neutral narratives in several neocortical and cerebellar regions typically activated by language in both adults and children ( Enge et al., 2020; Fedorenko et al., 2011; Friederici, 2011; Hiersche et al., 2024; Ozernov-Palchik et al., 2024; Price, 2010).”
Saalasti, S., ALHO, J. S., Lahnakoski, J. M., Bacha‐Trams, M., Glerean, E., Jääskeläinen, I. P., Hasson, U., & Sams, M. E. (2022) · Brain and Behavior · doi:10.1002/brb3.2869 · Cited by 8
Li, J., Kean, H. H., Fedorenko, E., & Saygin, Z. (2022) · Cognitive Neuropsychology · doi:10.1080/02643294.2023.2164923 · Cited by 4
“This network consists of left lateral frontal and lateral temporal areas and selectively supports language comprehension and production (e.g., Fedorenko et al., 2011).”
BackgroundSelf-citationLin, C., Jhan, S.-R., Lee, W., Chen, P., Chen, J., Chen, H.-C., & Chen, T. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.747536 · Cited by 5
Generalizable predictive modeling of semantic processing ability from functional brain connectivity
Meng, D., Wang, S., Wong, P. C. M., & Feng, G. (2022) · Human Brain Mapping · doi:10.1002/hbm.25953 · Cited by 5
“These regions are the main constitutes of 657 the canonical language system that is more activated by language tasks compared with 658 control tasks (Fedorenko et al., 2011; Fedorenko & Thompson-Schill, 2014).”
BackgroundMusz, E., Loiotile, R. E., Chen, J., et al. (2021) · bioRxiv · doi:10.1101/2021.04.01.438106 · Cited by 4
“These areas are thought to support linguistic, semantic and social processes (Buckner et al., 2008; Hassabis et al., 2007; Binder et al., 2009; Fedorenko et al., 2011).”
BackgroundConsidering Learning Ability in Language Rehabilitation Plans
Rohter, S. V. (2017) · Perspectives of the ASHA Special Interest Groups · doi:10.1044/persp2.sig2.23 · Cited by 8
Distinct and Shared Neural Basis Underlying Music and Language:A Perspective from Meta-analysis
Lai, H., Xu, M., Song, Y., et al. (2014) · doi:10.3724/sp.j.1041.2014.00285 · Cited by 4
From reaction to reflection: A recursive framework for the evolution and structure of intelligence
Trukovich, J. J. (2025) · Biosystems · doi:10.1016/j.biosystems.2025.105549 · Cited by 4
Information-making processes in the speaker’s brain drive human conversationsPreprint
Goldstein, A., Wang, H., Sheffer, T., Lioubashevski, D., Schain, M., Zada, Z., Niekerken, L., Aubrey, B., Nastase, S. A., Taubenfeld, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.27.609946 · Cited by 2
“For a full description of the procedure, see (10).”
3M-CPSEED, An EEG-based Dataset for Chinese Pinyin Production in Overt, Mouthed, and Imagined Speech
Ma, X., Jiang, Y., & Jiang, N. (2025) · Scientific Data · doi:10.1038/s41597-025-06346-1 · Cited by 4
“Conversely, Sub2’s activation primarily clustered in: (1) Broca’s area within the inferior frontal gyrus, which coordinates speech motor planning and production 47 ; and (2) Wernicke’s area in the posterior superior temporal gyrus, supporting language comprehension.”
Luo, H., Yu, T., Li, Q., & Sheng, L. (2025) · Neurophotonics · doi:10.1117/1.nph.12.3.035012 · Cited by 3
“6,48 Conversely, the MD fCOI was identified as the channel showing the maximum contrast between the hard and easy conditions of the cognitive control task.”
Distinct neural correlates of morphosyntactic and thematic comprehension processes in aphasia
Beber, S., Capasso, R., Maffei, C., Tettamanti, M., & Miceli, G. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf093 · Cited by 2
Evidence for a compensatory relationship between left- and right-lateralized brain networks
Peterson, M., Braga, R. M., Floris, D. L., & Nielsen, J. A. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00437 · Cited by 5
“…Gorno-Tempini et al., 2004; Mesulam et al., 2014, 2015), task-based fMRI ( Badzakova-Trajkov et al., 2010; Binder et al., 2011; Bishop et al., 2024; Fedorenko et al., 2010, 2011; Friederici et al., 2000; Gerrits et al., 2020; Ivanova et al., 2021; Labache et al., 2020; Lipkin et al., 2022; Mazoyer…”
Pragmatic grammar in genus homo
Hillert, D. G., & Fujita, K. (2023) · Biolinguistics · doi:10.5964/bioling.11911 · Cited by 3
“, anterior BA 44) which do not overlap with non-linguistic processes (e.g., Campbell & Tyler, 2018; Fedorenko et al., 2011; Jouravlev et al., 2019; Papitto et al., 2020).”
BackgroundInter‐individual variability in dorsal stream dynamics during word production
Liégeois‐Chauvel, C., Dubarry, A., Wang, Z. I., Chauvel, P., Gonzalez-Martinez, J. A., & Alario, F. ‐. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15807 · Cited by 4
“It has also been harnessed to distinguish a “core” part of the language network from regions recruited across other cognitive domains (Fedorenko et al. 2011; 2012), or to explore how a given task (e.”
BackgroundNeural underpinning of Japanese particle processing in non-native speakers
Kasai, C., Sumiya, M., Koike, T., Yoshimoto, T., Maki, H., & Sadato, N. (2022) · Scientific Reports · doi:10.1038/s41598-022-23382-8 · Cited by 4
Discourse fluency modulates spoken word recognition in monolingual and L2 speakers
Botezatu, M. R., Kroll, J. F., Trachsel, M. I., & Guo, T. (2021) · Bilingualism Language and Cognition · doi:10.1017/s1366728921001024 · Cited by 6
Frimu, R., & Dekydtspotter, L. (2021) · Lingua · doi:10.1016/j.lingua.2021.103216 · Cited by 2
A computational examination of the two-streams hypothesis: which pathway needs a longer memory?
Alipour, A., Beggs, J. M., Brown, J. W., & James, T. W. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09703-z · Cited by 5
Language networks in aphasia and health: a 1000 participant Activation Likelihood Estimate analysis
Stefaniak, J., Alyahya, R. S. W., & Ralph, M. L. (2020) · bioRxiv · doi:10.1101/2020.06.30.179655 · Cited by 3
“For instance, the language network is often described as unilateral (Mazoyer et al., 2014) but several lines of evidence suggest it is at least partially bilateral but asymmetric (Lambon Ralph et al., 2001; Fedorenko et al., 2011).”
BackgroundEfficient Communication in Written and Performed Music
Bonnasse‐Gahot, L. (2020) · Cognitive Science · doi:10.1111/cogs.12826 · Cited by 3
“First, some authors have proposed music as a protolanguage (Darwin, 1871, and see Fitch, 2006, for a discussion), and neural bases of language and music possibly overlap substantially (Patel, 2003; Koelsch, 2005; Fedorenko et al., 2009; Peretz et al., 2015, but see Fedorenko et al., 2011, 2012).”
BackgroundBlank, I., & Fedorenko, E. G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/712372 · Cited by 5
“To identify fROIs in this network we relied on its profile of deactivation during tasks that tax executive functions for the processing of external stimuli, and used a visuo-spatial working-memory localizer that included a “hard” condition requiring the memorization of 8 locations on a 3×4 grid (Fedorenko et al., 2011…”
BackgroundMethodSelf-citationThe Neural Basis of Language Faculties
Prat, C. S. (2012) · Handbook of Psychology, Second Edition · doi:10.1002/9781118133880.hop203022 · Cited by 3
Vision-Language Models Align with Human Neural Representations in Concept ProcessingPreprint
Bavaresco, A., Kloots, M. D. H., Pezzelle, S., & Fernández, R. (2024) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.2407.17914 · Cited by 2
“While responses were recorded for the whole brain, we focus on two specific regions, involved in either linguistic or visual processing: the left-hemisphere Language network (Fedorenko et al., 2011) and the Visual network (Power et al., 2011; Buckner et al., 2008).”
Manini, B., Vinogradova, V., Woll, B., Cameron, D., Martin Eimer, A., & Cardin, V. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.08.430248 · Cited by 3
Distributed networks for auditory memory differentially contribute to recall precisionPreprint
Lim, S.-J., Thiel, C. M., Sehm, B., Deserno, L., Lepsien, J., & Obleser, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.18.427143 · Cited by 1
“…(e.g., the inferior frontal gyrus and inferior parietal areas), and premotor cortex (Baddeley, 2003; Hickok and Poeppel, 2007; Koelsch et al., 2009; Fedorenko et al., 2011; Buchsbaum et al., 2005), as well as the bilateral auditory cortices for retaining stimulus-specific features of auditory…”
Lurie, L. A., Gruhn, M. A., Garrisi, K., McLaughlin, K. A., Humphreys, K. L., Zeanah, C. H., Fox, N. A., Nelson, C. A., & Sheridan, M. A. (2025) · Developmental Science · doi:10.1111/desc.70043 · Cited by 2
The compositionality continuum as a principle for studying the neural basis of intelligence
Riveland, R., Pouget, A., & Driscoll, L. N. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02382-1 · Cited by 3
“https://doi.org/10.1038/s41593-026-02382-1 the frontotemporal network, which is anatomically separate from the medial prefrontal cortex, hippocampus and entorhinal cortex—regions central to the compositional abilities discussed earlier 73–75 .”
InfluentialNeuReasoner: Theory-grounded Mapping of Reasoning Elicitation Boundaries
Javadov, A., Aitkazinov, S., Hoesli, T., et al. (2026) · arXiv.org · doi:10.48550/arxiv.2606.29971 · Cited by 2
“Following their decomposition, our design mirrors four canonical networks: the language network (Fedorenko et al., 2011), multiple-demand network (Duncan, 2010), theory-of-mind network (Saxe and Kanwisher, 2003), and default-mode network (Gus-nard et al., 2001).”
Metadata
- Authors
- Fedorenko, E., Behr, M. K., & Kanwisher, N.
- Affiliations
- Massachusetts Institute of Technology (US)
- Brain and Cognitive Sciences Department and
- McGovern Institute for Brain Research (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-09
- Volume
- 108
- Issue
- 39
- Pages
- 16428-16433
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3182706
- OpenAlex
- W2096462008
- Semantic Scholar
- b62b60be4fe51188d90a27dd9fc695c924a55b0b
- Cited by
- 595
- References
- 0
- Citations by year
- 2026: 43 · 2025: 52 · 2024: 75 · 2023: 42 · 2022: 47 · 2021: 61 · 2020: 38 · 2019: 32 · 2018: 38 · 2017: 33 · 2016: 34 · 2015: 35 · 2014: 22 · 2013: 24 · 2012: 17
- FWCI
- 10.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Massachusetts Institute of Technology
- National Institutes of Health (5k99hd057522-02)
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K99 HD-057522)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Keywords
- language lateralization, functional specificity, language regions, arithmetic, working memory, cognitive control, music processing, domain specificity
- MeSH terms
- Brain, Female, Humans, Linguistics, Magnetic Resonance Imaging, Male, Speech
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.
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