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.
- 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
“[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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- 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
“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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- Load-bearing: passes — The paper’s conclusions depend on it.
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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.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether 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 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.
- Checkable: passes — It can be checked against evidence.
- 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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- 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. 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.
- Checkable: passes — It can be checked against evidence.
- 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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- 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. 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.
- Based: passes — The paper gives a reason why it holds.
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.
- Based: passes — The paper gives a reason why it holds.
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.
- 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
“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 measuredIn 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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“Faces (Kanwisher et al., 1999), body parts (Downing et al., 2001), scenes (Epstein and Kanwisher, 1998), language (Fedorenko et al., 2011), and letters (Cohen and Dehaene, 2004) are further examples of stimuli that were claimed to be selectively processed by a specific brain region.”
BackgroundInfluentialChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding
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“These regions neatly matched the language network [48,49] with the absence of the triangular part of the IFG and the 55b region in the premotor cortex [63] (see the overlap in S5 Fig), in line with previous studies suggesting these two dorsal regions play a non-semantic role in language processing…”
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“, 2017; for a review, see Fedorenko, 2014) but not to nonlinguistic stimuli such as music, mathematical expressions, or computer code (Fedorenko et al., 2011; Ivanova et al., 2020; Monti et al., 2012).”
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Zhang, H., Eppes, A., Beatty‐Martínez, A. L., Navarro‐Torres, C. A., & Diaz, M. T. (2018) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-018-0616-2 · Cited by 27
Regional amplitude of the low-frequency fluctuations at rest predicts word-reading skill
Xu, M., Beuckelaer, A. D., Wang, X., Liu, L., Song, Y., & Liu, J. (2015) · Neuroscience · doi:10.1016/j.neuroscience.2015.04.025 · Cited by 22
Wende, K. C., Nagels, A., Blos, J., Stratmann, M., Chatterjee, A. K., Kircher, T. T. J., & Straube, B. (2013) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.07.027 · Cited by 17
The standard ontological framework of cognitive neuroscience: Some lessons from Broca’s area
Viola, M., & Zanin, E. (2017) · Philosophical Psychology · doi:10.1080/09515089.2017.1322193 · Cited by 28
“…words, while two different cognitive functions may activate neighboring but distinct cortical regions within Broca’s area in every subject individually, group analyses might blur them into overlapping activations of a broadly defined Broca’s area, as shown by Fedorenko, Behr, and Kanwisher (2011).”
BackgroundOzernov‐Palchik, O., O’Brien, A. M., Lee, E. J., Richardson, H., Romeo, R. R., Poliak, M., Lipkin, B., Small, H., Capella, J., Nieto-Castañón, A., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72916-5 · Cited by 28
“In particular, the fronto-temporal language network should be more similar between the two hemispheres in its responses to language during early development compared to later childhood and adulthood.”
Self-citationShain, C., & Fedorenko, E. (2025) · bioRxiv · doi:10.1101/2025.03.29.646067 · Cited by 21
“Last, the functional data were smoothed spatially using spatial convolution with a 4 mm FWHM Gaussian kernel.”
Self-citationCan Construction Grammar Be Proven Wrong?
Cappelle, B. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009343213 · Cited by 22
Klaus, J., Schutter, D. J. L. G., & Piai, V. (2019) · Human Brain Mapping · doi:10.1002/hbm.24860 · Cited by 25
McCormick, K., Lacey, S., Stilla, R., et al. (2018) · bioRxiv · doi:10.1101/478347 · Cited by 21
“Therefore, it is also possible that the evaluation of pseudoword-shape associations might activate regions in the left hemisphere lexical-semantic network, e.g. as identified by Fedorenko et al. (2010, 2011) using language localizers contrasting sentences with strings of pseudowords.”
BackgroundMethodResultInfluentialBlank, I., Kiran, S., & Fedorenko, E. G. (2017) · Cognitive Neuropsychology · doi:10.1080/02643294.2017.1402756 · Cited by 25
“The former regions, identified with a passive reading task, show a stronger response to sentences than lists of nonwords (Fedorenko et al., 2011); the latter regions, identified with a working-memory task that requires keeping track of several spatial locations, are more engaged during trials that are harder (8 locati…”
BackgroundSelf-citationZhao, W., Guo, J., Deng, Y., et al. (2025) · Neural Information Processing Systems · doi:10.48550/arxiv.2505.15257 · Cited by 20
“Our key hypothesis is that reasoning and language processing can be explicitly disentangled , allowing reasoning abili-ties—once acquired in a high-resource language—to transfer more broadly across languages.”
Abdallah, M., Zanitti, G., Iovene, V., et al. (2022) · bioRxiv · doi:10.1101/2022.01.21.477198 · Cited by 20
“The networks are the dorsal and ventral attention networks, default mode network, salience network, and most importantly the frontoparietal cognitive control network ( Cole et al., 2014 ; Dixon et al., 2018 ; Fedorenko et al., 2011 ; Marek and Dosenbach, 2018 ; Yeo et al., 2011 ).”
InfluentialLu, J., Yang, H., He, H., Jeon, S., Hou, C., Evans, A. C., & Yao, D. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00695 · Cited by 22
“Occasionally, activity can also be seen in the rostrolateral prefrontal cortex (RPFC) (Duncan, 2010; Fedorenko et al., 2011).”
BackgroundInfinitely productive language can arise from chance under communicative pressure
Piantadosi, S. T., & Fedorenko, E. G. (2017) · Journal of Language Evolution · doi:10.1093/jole/lzw013 · Cited by 22
“Linguistic knowledge is most likely stored within the fronto-temporal language network (Fedorenko et al. 2011), and these are exactly the regions that became massively expanded in humans, along with some additional association zones in the parietal lobes (Buckner and Krienen 2013).”
BackgroundSelf-citationA taste for words and sounds: a case of lexical-gustatory and sound-gustatory synesthesia
Colizoli, O., Murre, J. M. J., & Rouw, R. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00775 · Cited by 41
“…activation related to language (both written and spoken) was localized successfully compared to the known literature on language related activation (Fedorenko et al., 2010, 2011, 2012), evoking activity in well-known language related structures, such as bilateral middle temporal gyri, inferior…”
Multidisciplinary approaches in evolutionary linguistics
Gong, T., Shuai, L., & Wu, Y. (2012) · Language Sciences · doi:10.1016/j.langsci.2012.09.002 · Cited by 20
“…mathematical calculation, spatial working memory, stroop effects and music processing) do not overlap those in linguistic tasks (Saxe et al., 2006; Fedorenko et al., 2011), whereas others illustrate that frontal or prefrontal brain regions become activated in both linguistic and nonlinguistic…”
BackgroundMalik-Moraleda, S., Jouravlev, O., Taliaferro, M., Mineroff, Z., Cucu, T., Mahowald, K., Blank, I., & Fedorenko, E. (2023) · bioRxiv · doi:10.1101/2023.01.19.524657 · Cited by 18
“And second, many studies have relied on paradigms that conflate language processing and general task demands, which recruit distinct brain networks: the language-selective network (Fedorenko et al., 2011) and the domain-general Multiple Demand network (e.g., Duncan, 2010, 2013), respectively.”
Self-citationPhysical distance to sensory-motor landmarks predicts language function
Wang, X.-Y., Krieger-Redwood, K., Zhang, M.-C., Cui, Z., Wang, X.-K., Karapanagiotidis, T., Du, Y.-J., Leech, R., Bernhardt, B., Margulies, D., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac344 · Cited by 20
“the patterns of letters and sounds that capture words) and semantic information about word meaning (Binder and Desai 2011; Fedorenko et al. 2011; Albouy et al. 2020; Braga et al. 2020; Hamilton et al. 2021).”
BackgroundMethodPleyer, M., & Hartmann, S. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02537 · Cited by 28
Varley, R. A. (2014) · Language Sciences · doi:10.1016/j.langsci.2014.06.012 · Cited by 44
Natural language processing models reveal neural dynamics of human conversation
Cai, J., Hadjinicolaou, A. E., Paulk, A. C., Soper, D. J., Xia, T., Wang, A. F., Rolston, J. D., Richardson, R. M., Williams, Z. M., & Cash, S. S. (2025) · Nature Communications · doi:10.1038/s41467-025-58620-w · Cited by 47
A social-semantic working-memory account for two canonical language areas
Zhang, G., Xu, Y., Wang, X., Li, J., Shi, W., Bi, Y., & Lin, N. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01704-8 · Cited by 30
“It has been used for localizing the language network 24,26,29,41 , examining the linguistic functions of the brain networks defined by resting-state fMRI data processing 64 and revealing the recruitment of the occipital cortex of congenitally blind individuals in linguistic processing 65 .”
BackgroundMethodGilmore, N., Yücel, M. A., Li, X., Boas, D. A., & Kiran, S. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.728151 · Cited by 27
Wu, K. M., Anderson, J., Townsend, J. D., Frazier, T., Brandt, A., & Karmonik, C. (2019) · International Journal of Neuroscience · doi:10.1080/00207454.2019.1581189 · Cited by 21
“Bach’s compositions, in particular, are associated with high-level linguistic processing in the brain and rhythmic processing, indicating the remarkable overlap between language and nonlinguistic tasks [21].”
BackgroundFedorenko, E., Mineroff, Z., Siegelman, M., & Blank, I. (2018) · bioRxiv · doi:10.1101/477851 · Cited by 18
Self-citationNeural Evidence of Cross-domain Structural Interaction between Language and Arithmetic
Nakai, T., & Okanoya, K. (2018) · Scientific Reports · doi:10.1038/s41598-018-31279-8 · Cited by 20
Just above chance: is it harder to decode information from human prefrontal cortex BOLD signals?Preprint
Bhandari, A., Gagne, C., & Badre, D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/127324 · Cited by 10
Enhanced music sensitivity in 9-month-old bilingual infants
Liu, L., & Kager, R. (2016) · Cognitive Processing · doi:10.1007/s10339-016-0780-7 · Cited by 36
“Neural studies have pinpointed specific areas involved in language and music processing (Fedorenko et al. 2011).”
InfluentialLow functional robustness in mesial temporal lobe epilepsy
Garcia‐Ramos, C., Song, J., Hermann, B. P., & Prabhakaran, V. (2016) · Epilepsy Research · doi:10.1016/j.eplepsyres.2016.04.001 · Cited by 19
Daikhin, L., & Ahissar, M. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00786 · Cited by 18
What can atypical language hemispheric specialization tell us about cognitive functions?
Cai, Q., & der Haegen, L. V. (2015) · Neuroscience Bulletin · doi:10.1007/s12264-014-1505-5 · Cited by 21
How Special Is Language Production? Perspectives From Monitoring and Control
Nozari, N. (2018) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/bs.plm.2018.08.006 · Cited by 26
Meier, E. L., Kelly, C. R., & Hillis, A. E. (2022) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2022.108270 · Cited by 21
Caution Ahead: Numerical Reasoning and Look‐Ahead Bias in AI Models
LEVY, B. (2026) · Journal of Accounting Research · doi:10.1111/1475-679x.70058 · Cited by 20
Training Large Language Models to Reason in a Continuous Latent Space
Hao, S., Sukhbaatar, S., Su, D., Li, X., Hu, Z., Weston, J., & Tian, Y. (2024) · arXiv · doi:10.48550/arxiv.2412.06769 · Cited by 18
“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 (Amalric and Dehaene, 2019; Monti et al., 2012, 2007, 2009; Fedorenko et al., 2011).”
What Are Large Language Models Mapping to in the Brain? A Case Against Over-Reliance on Brain ScoresPreprint
Feghhi, E., Hadidi, N., Song, B., Blank, I., & Kao, J. C. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2406.01538 · Cited by 15
“These include the Pereira fMRI dataset, where participants read short passages [8]; the Fedorenko electrocorticography (ECoG) dataset, where participants read isolated sentences [19]; and the Blank fMRI dataset, where participants listened to short stories [20].”
MethodSun, J., & Moens, M. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/577 · Cited by 15
“However, the human brain does not need to reformulate an entire functional network to learn a single new task [ Cole et al. , 2013; Fedorenko et al. , 2011 ] .”
BackgroundMethodPreliminary evidence for selective cortical responses to music in one‐month‐old infants
Kosakowski, H. L., Norman-Haignere, S., Mynick, A., Takahashi, A., Saxe, R. R., & Kanwisher, N. (2023) · Developmental Science · doi:10.1111/desc.13387 · Cited by 18
Self-citationDissociating language and thought in large language models
Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2023) · arXiv · doi:10.48550/arxiv.2301.06627 · Cited by 80
“…like facial expressions or gestures [e.g., Amalric and Dehaene, 2019, Benn et al., 2021, Blank et al., 2014, Chen et al., 2021, Deen et al., 2015, Fedorenko et al., 2011, Ivanova et al., 2020, Jouravlev et al., 2019, Liu et al., 2020, Monti et al., 2007, 2009] [Monti et al., 2012, Paunov et…”
Self-citationLipkin, B., Tuckute, G., Affourtit, J., Small, H., Mineroff, Z., Kean, H., Jouravlev, O., Rakocevic, L. I., Pritchett, B., Siegelman, M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.06.483177 · Cited by 14
“This localizer identifies the fronto-temporal language network that selectively (Fedorenko et al., 2011) supports high-level language comprehension and production, including the processing of word meanings and combinatorial syntactic/semantic processing (Bautista & Wilson, 2016; Fedorenko et al.,…”
Self-citationMoving beyond domain-specific versus domain-general options in cognitive neuroscience.
Asano, R., Boeckx, C., & Fujita, K. (2021) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.31234/osf.io/euxy9 · Cited by 17
Cognitive control regions are recruited in bilinguals' silent reading of mixed-language paragraphs
Stasenko, A., Hays, C. C., Wierenga, C. E., & Gollan, T. H. (2020) · Brain and Language · doi:10.1016/j.bandl.2020.104754 · Cited by 18
Scale-Free Amplitude Modulation of Neuronal Oscillations Tracks Comprehension of Accelerated Speech
Borges, A. F. T., Giraud, A., Mansvelder, H. D., & Linkenkaer‐Hansen, K. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1515-17.2017 · Cited by 25
“Although temporal, frontal, and parietal regions encompass language-dedicated areas (Hickok and Poeppel, 2007; Fedorenko et al., 2011; Silbert et al., 2014), occipital and cerebellar regions are selectively involved when comprehension is challenging (Erb et al., 2013; Guediche et al., 2014).”
BackgroundA surgical approach to the anatomo-functional structure of language
Mandonnet, E. (2017) · Neurochirurgie · doi:10.1016/j.neuchi.2016.10.004 · Cited by 16
Zhang, K., Xiao, W., & Peng, G. (2017) · The Journal of the Acoustical Society of America · doi:10.1121/1.4973414 · Cited by 18
“Fedorenko et al. (2011) localized the brain regions involved in linguistic and nonlinguistic tasks, and found that regions activated by linguistic tasks (e.g., left inferior frontal gyrus, left temporal structures) showed little response to nonlinguistic functions.”
BackgroundAbd‐El‐Barr, M. M., Saleh, E., Huang, R. Y., & Golby, A. J. (2013) · Imaging in Medicine · doi:10.2217/iim.13.40 · Cited by 15
Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations
Mitra, C., Luo, Y., Saravanan, R., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2511.22697 · Cited by 16
“In contrast, we take inspiration from functional specificity in neuroscience, which suggests that certain brain regions are specialized for particular tasks [20, 37].”
TopoLM: brain-like spatio-functional organization in a topographic language modelPreprint
Rathi, N., Mehrer, J., AlKhamissi, B., Binhuraib, T. O. A., Blauch, N. M., & Schrimpf, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.11516 · Cited by 9
“Due to anatomical differences between individuals, the language system is defined via a functional localizer that contrasts syntactically and semantically valid sentences against a perceptually matched control, such as strings of nonwords (Fedorenko et al., 2011).”
InfluentialChen, L., Gao, C., Li, Z., Zaccarella, E., Friederici, A. D., & Feng, L. (2023) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2023.101127 · Cited by 20
Neural network bases of thematic semantic processing in language production
Blackett, D. S., Varkey, J., Wilmskoetter, J., Roth, R., Andrews, K., Busby, N., Gleichgerrcht, E. L., Desai, R. H., Riccardi, N., Basilakos, A., et al. (2022) · Cortex · doi:10.1016/j.cortex.2022.08.007 · Cited by 20
Spatiotemporal dynamics of abstract and concrete semantic representations
Vignali, L., Xu, Y., Turini, J., Collignon, O., Crepaldi, D., & Bottini, R. (2023) · Brain and Language · doi:10.1016/j.bandl.2023.105298 · Cited by 14
Wolna, A., Szewczyk, J., Diaz, M. T., Domagalik, A., Marcin Szwed, A., & Wodniecka, Z. (2024) · Scientific Reports · doi:10.1038/s41598-023-49375-9 · Cited by 19
“…is sensitive to integration across a broad array of tasks, it is not strongly engaged by basic language processes (e.g., sentence comprehension 23,31 ) Since L2 is typically less automatized than L1, the sensitivity of the MD network to task automatization, makes it a suitable candidate for…”
Temperley, D. (2022) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-031220-121126 · Cited by 15
Subject‐specific segregation of functional territories based on deep phenotyping
Pinho, A. L., Amadon, A., Fabre, M., Dohmatob, E., Denghien, I., Torre, J. J., Ginisty, C., Becuwe-Desmidt, S., Roger, S., Laurier, L., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25189 · Cited by 17
“Plus, an individualized data integration approach is herein developed, keeping in mind that an average brain may not be similar to an individual brain (Fedorenko et al., 2011; Fedorenko, Duncan, & Kanwisher, 2012).”
MethodHierarchical structure priming from mathematics to two- and three-site relative clause attachment.
Scheepers, C., Galkina, A., Shtyrov, Y., & Myachykov, A. (2019) · Cognition · doi:10.1016/j.cognition.2019.03.021 · Cited by 16
Contour interpolation: A case study in Modularity of Mind
Keane, B. P. (2018) · Cognition · doi:10.1016/j.cognition.2018.01.008 · Cited by 38
Blank, I., Duff, M. C., Brown‐Schmidt, S., & Fedorenko, E. G. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/091900 · Cited by 21
“Bearing such differences in mind, a particularly interesting result is the deactivation observed during the hard version of the verbal working memory task, which mirrors the patterns previously observed for the neocortical language regions (55).”
BackgroundMethodResultInfluentialSelf-citationDjokic, V. G., Maillard, J., Bulat, L., & Shutova, E. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00307 · Cited by 15
“It preferentially responds to language tasks when compared to non-linguistic tasks of similar complexity (Fedorenko et al., 2011).”
High-level language brain regions process sublexical regularities
Regev, T. I., Kim, H. S., Chen, X. J., Affourtit, J., Schipper, A. E., Bergen, L., Mahowald, K., & Fedorenko, E. G. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae077 · Cited by 26
Self-citationLeft-Hemisphere Cortical Language Regions Respond Equally to Observed Dialogue and Monologue
Olson, H. A., Chen, E. M., Lydic, K. O., & Saxe, R. R. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00123 · Cited by 16
“…of left-hemisphere frontal and temporal regions are involved in processing language (Bates et al., 2001; Binder et al., 1997; Dronkers et al., 2004; Fedorenko et al., 2010, 2011; Friederici, 2011; Friederici & Gierhan, 2013; Price, 2010, 2012), robustly responding to language whether it is spoken…”
BackgroundKauf, C., Tuckute, G., Lévy, R., Andreas, J., & Fedorenko, E. G. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00116 · Cited by 48
“…has shown that the human language network — a set of brain areas that are selectively and robustly activated when humans process language (e.g., Fedorenko et al., 2011; Fedorenko & Thompson-Schill, 2014; Lipkin et al., 2022; Regev et al., 2013) — exhibits the same amount of activation to…”
BackgroundSelf-citationKarlsson, E., Hugdahl, K., Hirnstein, M., et al. (2023) · Cerebral Cortex Communications · doi:10.1093/texcom/tgad009 · Cited by 14
“Nevertheless, cognitive neuroscientists have documented rather distinct neural circuits for different components of language, for example speech perception and speech comprehension (contrast Norman-Haignere et al. 2015; Overath et al. 2015; Guenther 2016; with Fedorenko et al. 2010, 2011).”
BackgroundA functional MRI investigation of crossmodal interference in an audiovisual Stroop task
Fitzhugh, M. C., Whitehead, P. S., Johnson, L., Cai, J., Baxter, L. C., & Rogalsky, C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210736 · Cited by 14
The language network is not engaged in object categorization
Benn, Y., Ivanova, A. A., Clark, O., Mineroff, Z., Seikus, C., Silva, J. S., Varley, R., & Fedorenko, E. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad289 · Cited by 14
Self-citationMacroscale brain states support the control of semantic cognition
Wang, X., Krieger‐Redwood, K., Cui, Y., Smallwood, J., Du, Y., & Jefferies, E. (2024) · Communications Biology · doi:10.1038/s42003-024-06630-7 · Cited by 11
Meisler, S. L., Gabrieli, J. D. E., & Christodoulou, J. A. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00108 · Cited by 13
“This system is typically left-lateralized ( Murphy et al., 2019), consistent with the frequent left-lateralization of language processing ( Enge et al., 2020; Fedorenko et al., 2011; Vigneau et al., 2006).”
BackgroundThe Language Network Reliably “Tracks” Naturalistic Meaningful Nonverbal Stimuli
Sueoka, Y., Paunov, A. M., Ivanova, A. A., Tanner, A., Blank, I., & Fedorenko, E. G. (2024) · Neurobiology of Language · doi:10.1162/nol_a_00135 · Cited by 17
“For example, language-responsive regions show little or no response to arithmetic (e.g., Amalric & Dehaene, 2019; Fedorenko et al., 2011; Monti et al., 2012), music (e.g., Chen et al., 2023; Fedorenko et al., 2011; Rogalsky et al., 2011), logic (e.g., Monti et al., 2009), executive control (e.g.,…”
BackgroundInfluentialSelf-citationMixture of Cognitive Reasoners: Modular Reasoning with Brain-Like SpecializationPreprint
AlKhamissi, B., Sabbata, C. N. D., Tuckute, G., Chen, Z.-M., Schrimpf, M., & Bosselut, A. (2025) · International Conference on Learning Representations · doi:10.48550/arxiv.2506.13331 · Cited by 12
“…each layer of a pretrained language model into four experts, each designed to mirror a major cognitive network in the human brain: language (Fedorenko et al., 2011), logic (multiple demand; Duncan, 2010), social reasoning (theory of mind; Saxe & Kanwisher, 2003), and world knowledge…”
Revisiting human language and speech production network: A meta-analytic connectivity modeling study
Hsu, C., Huang, C., Hsu, C. H., Bi, Y., Tzeng, O. J. L., & Lin, C. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121008 · Cited by 12
“Taken together with previous evidence, our findings suggest that Broca ’ s area is implicated in multifunctional roles within high-level language (Fedorenko et al., 2011b) and domain-general related cognitions (Duncan, 2010), but does not participate in speech output.”
Individual Brain Charting dataset extension, third release for movie watching and retinotopy data
Pinho, A. L., Richard, H., Ponce, A. F., Eickenberg, M., Amadon, A., Dohmatob, E., Denghien, I., Torre, J. J., Shankar, S., Aggarwal, H., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03390-1 · Cited by 13
When Abstract Becomes Concrete: Naturalistic Encoding of Concepts in the Brain
Kewenig, V., Vigliocco, G., & Skipper, J. I. (2024) · bioRxiv · doi:10.1101/2022.09.08.506944 · Cited by 12
Efficient functional localization of language regions in the brain
Lee, J. J., Scott, T. L., & Perrachione, T. K. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120489 · Cited by 12
“…avenue for understanding the functional architecture of the cortical language network, where traditional group analyses have led to conflicting conclusions about the functional specialization or selectivity of classic language areas (Fedorenko et al., 2011; Nieto-Casta ˜ n ´ on & Fedorenko, 2012).”
BackgroundInfluentialConstructed languages are processed by the same brain mechanisms as natural languages
Malik-Moraleda, S., Taliaferro, M., Shannon, S., Jhingan, N., Swords, S., Peterson, D. J., Frommer, P., Okrand, M., Peterson, J., Cardwell, R. L., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313473122 · Cited by 15
“29 and 70) and their role in lexico - semantic and combinatorial processing during language comprehension and production (e.g., refs.”
Self-citationPersichetti, A. S., Shao, J., Gotts, S., et al. (2022) · Journal of Neuroscience · doi:10.1101/2022.06.21.22276730 · Cited by 12
“We used this conglomeration of language and social processing regions (Geschwind, 1972; Binder et al., 1997; Frith and Frith, 2007; Olson et al., 2007; Adolphs, 2009; Mitchell, 2009; Fedorenko et al., 2011) as the primary network-of-interest (NOI) for further analyses.”
MethodHigh-level language brain regions are sensitive to sub-lexical regularitiesPreprint
Regev, T. I., Affourtit, J., Chen, X. J., Schipper, A. E., Bergen, L., Mahowald, K., & Fedorenko, E. G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.11.447786 · Cited by 8
“…et al., 2013; Scott et al., 2017) across tasks (e.g., Fedorenko et al., 2010; Cheung et al., 2020; Diachek et al., 2020), but show little or no response to diverse non-linguistic functions (e.g., Fedorenko et al., 2011; Monti et al., 2012; Fedorenko and Blank, 2020; Ivanova et al., 2020, 2021).”
BackgroundSelf-citationHamdan, H., Hashmi, S. K., Lazarus, H. M., Gale, R. P., Qu, W., & Fakih, R. O. E. (2020) · Blood Reviews · doi:10.1016/j.blre.2020.100742 · Cited by 16
Benn, Y., Wilkinson, I. D., Zheng, Y., Kadosh, K. C., Romanowski, C. A. J., Siegal, M., & Varley, R. A. (2013) · Brain and Cognition · doi:10.1016/j.bandc.2013.04.012 · Cited by 9
“Furthermore, Fedorenko et al. (2011) were able to identify distinct language- versus calculation-related activation within the traditional language zones such as Broca’s area (BA 44/45).”
Background(2023) · doi:10.1101/2023.05.05.539646 · Cited by 11
Self-citationThe cerebellar components of the human language network
Casto, C., Poliak, M., Tuckute, G., Small, H., Sherlock, P., Wolna, A., Lipkin, B., D’Mello, A. M., & Fedorenko, E. G. (2026) · Neuron · doi:10.1016/j.neuron.2025.12.030 · Cited by 30
Self-citationBrainWavLM: Fine-tuning Speech Representations with Brain Responses to Language
Vattikonda, N., Vaidya, A. R., Antonello, R. J., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2502.08866 · Cited by 11
“The model fine-tuned on AC has higher performance in language-selective areas in the temporal and frontal lobes [Fedorenko et al., 2011, Lipkin et al., 2022].”
Tang, X., Turesky, T. K., Escalante, E. S., Loh, M., Xia, M., Yu, X., & Gaab, N. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101405 · Cited by 14
“It has recently been demonstrated that this language network exhibits selective activation for high-level linguistic processes in adults (Fedorenko et al., 2011) across 45 languages and 12 language families (Malik--Moraleda et al., 2022), thus representing a universal neural template for language…”
Wolna, A., Szewczyk, J., Diaz, M. T., Domagalik, A., Marcin Szwed, A., & Wodniecka, Z. (2023) · Neurobiology of Language · doi:10.1162/nol_a_00128 · Cited by 37
“The design of the task followed the one used by Fedorenko et al. (2011), in which participants completed blocks of the easy and hard versions of the task.”
BackgroundMethodInfluentialLandsiedel, J., & Koldewyn, K. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00003 · Cited by 13
From deep brain phenotyping to functional atlasing
Thirion, B., Thual, A., & Pinho, A. L. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.05.004 · Cited by 11
Decoding the silence: Neural bases of zero pronoun resolution in Chinese
Zhang, S., Li, J., Yang, Y., et al. (2021) · bioRxiv · doi:10.1101/2021.05.06.442989 · Cited by 11
“…we identify a network that includes the Precuneus and Parahippocampal Gyrus, which are regarded as part of the “extended” language network, compared to the “core” language network including brain regions such as the temporal lobe (Fedorenko et al., 2011; Ferstl et al., 2008; Xiong & Newman, 2021).”
BackgroundJin, L., Li, C., Zhang, Y., Yuan, T., Ying, J., Zuo, Z., & Gui, S. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.617179 · Cited by 9
“Together, these findings indicate that the language system is best considered a core-periphery model regulated by a domain-general and domain-specific subnetwork (8), an approach that has been proven to be constructive in understanding the functional architecture of language (9).”
BackgroundBrain network reconfiguration for narrative and argumentative thought
Xu, Y., Vignali, L., Collignon, O., Crepaldi, D., & Bottini, R. (2021) · Communications Biology · doi:10.1038/s42003-021-02151-9 · Cited by 16
“Second, the fine-resolution version also separates the language network (Fedorenko et al., 2011) and the DMN (Buckner et al., 2008).”
Bourguignon, N. J., & Gracco, V. L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.043 · Cited by 13
“The separability between language and cognitive control is suggested from the observation that most regions within the language system are not activated by tasks involving the multiple-demand system (Fedorenko et al., 2011).”
Bansal, R., & Peterson, B. S. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.01.004 · Cited by 30
Verbal and visual cognition: Individual differences in the lab, in the brain, and in the classroom
Alfred, K. L., & Kraemer, D. J. M. (2017) · Developmental Neuropsychology · doi:10.1080/87565641.2017.1401075 · Cited by 11
Navarro‐Torres, C. A., Dussias, P. E., & Kroll, J. F. (2022) · Language Cognition and Neuroscience · doi:10.1080/23273798.2022.2105915 · Cited by 10
“Others have found little-to-no overlap (e.g. Acheson & Hagoort, 2014; Engelhardt et al., 2017; Fedorenko et al., 2011; Van Dyke et al., 2014) and argue for a strong division between the two (Fedorenko, 2014).”
BackgroundEvidence from Formal Logical Reasoning Reveals that the Language of Thought is not Natural Language
Kean, H. H., Fung, A., Jaggers, P., et al. (2026) · bioRxiv · doi:10.1101/2025.07.26.666979 · Cited by 10
“…studies have examined the relationship between language processing and these types of reasoning and found no overlap: e.g., mathematical reasoning (Fedorenko et al., 2011; Amalric & Dehaene, 2016; Amalric et al., 2019; for evidence from aphasia, see Varley et al., 2005), computer code…”
InfluentialSelf-citationLanguage models align with brain regions that represent concepts across modalities
Ryskina, M., Tuckute, G., Fung, A., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2508.11536 · Cited by 10
“The “sentences > non-words” contrast has been shown to reliably identify areas of the brain that are engaged in linguistic processing but not other functions (Fedorenko et al., 2011; Benn et al., 2023; Chen et al., 2023; see Fedorenko et al., 2024 for a review).”
Self-citationAuditory areas are recruited for naturalistic visual meaning in early deaf people
Zimmermann, M., Cusack, R., Bedny, M., & Marcin Szwed, A. (2024) · Nature Communications · doi:10.1038/s41467-024-52383-6 · Cited by 10
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.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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