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.
Overview
AI snapshot
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.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
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.
- 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
“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.
- 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
“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.
- 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.
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.
- 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.
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.
- 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 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.
- 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
“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.
- 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. 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.
- 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. 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.
- 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.
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
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.
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.
- 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.
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
- Not checked yetcites [7] Dehaene S, Spelke E, Pinel P, Stanescu R, Tsivkin S. Sources of mathematical thinking: Behavioral and brain-imaging evidence. Science. 1999;284:970–974. doi: 10.1126/science.284.5416.970. [DOI] [PubMed] [Google Scholar] source
- Not checked yetcites [8] Novick JM, Trueswell JC, Thompson-Schill SL. Cognitive control and parsing: Reexamining the role of Broca's area in sentence comprehension. Cogn Affect Behav Neurosci. 2005;5:263–281. doi: 10.3758/cabn.5.3.263. [DOI] [PubMed] [Google Scholar] source
- Not checked yetcites [9] Levitin DJ, Menon V. Musical structure is processed in “language” areas of the brain: A possible role for Brodmann Area 47 in temporal coherence. Neuroimage. 2003;20:2142–2152. doi: 10.1016/j.neuroimage.2003.08.016. [DOI] [PubMed] [Google Scholar] source
- Not checked yetcites [10] Awh E, et al. Dissociation of storage and rehearsal in verbal working memory: Evidence from positron emission tomography. Psych Science. 1996;7:25–31. [Google Scholar] source
- Not checked yetcites [11] Badre D, Poldrack RA, Paré-Blagoev EJ, Insler RZ, Wagner AD. Dissociable controlled retrieval and generalized selection mechanisms in ventrolateral prefrontal cortex. Neuron. 2005;47:907–918. doi: 10.1016/j.neuron.2005.07.023. [DOI] [PubMed] [Google Scholar] source
- Not checked yetcites [12] Blumstein S. Reflections on the cognitive neuroscience of language. In: Gazzaniga M, editor. The Cognitive Neurosciences. Cambridge, MA: MIT Press; 2009. pp. 1235–1240. [Google Scholar]
- Not checked yetcites [13] Braver TS, et al. A parametric study of prefrontal cortex involvement in human working memory. Neuroimage. 1997;5:49–62. doi: 10.1006/nimg.1996.0247. [DOI] [PubMed] [Google Scholar] source
- Not checked yetcites [14] Bunge SA, Klingberg T, Jacobsen RB, Gabrieli JDE. A resource model of the neural basis of executive working memory. Proc Natl Acad Sci USA. 2000;97:3573–3578. doi: 10.1073/pnas.050583797. [DOI] [PMC free article] [PubMed] [Google Scholar] source
- Not checked yetcites [15] Cohen JD, et al. Activation of the prefrontal cortex in a nonspatial working memory task with functional MRI. Hum Brain Mapp. 1994;1:293–304. doi: 10.1002/hbm.460010407. [DOI] [PubMed] [Google Scholar] source
- Not checked yetcites [16] Fadiga L, Craighero L, D'Ausilio A. Broca's area in language, action, and music. Ann N Y Acad Sci. 2009;1169:448–458. doi: 10.1111/j.1749-6632.2009.04582.x. [DOI] [PubMed] [Google Scholar] source
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
Cites
An Integrative Theory of Prefrontal Cortex Function
Miller, E. K., & Cohen, J. D. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.167 · Cited by 12,803
Common regions of the human frontal lobe recruited by diverse cognitive demands
Duncan, J., & Owen, A. M. (2000) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01633-7 · Cited by 2,498
Towards a neural basis of auditory sentence processing
Friederici, A. D. (2002) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01839-8 · Cited by 2,231
Hickok, G., & Poeppel, D. (2004) · Cognition · doi:10.1016/j.cognition.2003.10.011 · Cited by 2,212
The Architecture of Cognitive Control in the Human Prefrontal Cortex
Koechlin, É., Ody, C., & Kouneiher, F. (2003) · Science · doi:10.1126/science.1088545 · Cited by 1,939
Contributions of memory circuits to language: the declarative/procedural model
Ullman, M. T. (2004) · Cognition · doi:10.1016/j.cognition.2003.10.008 · Cited by 1,893
The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour
Duncan, J. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.004 · Cited by 1,846
Sources of Mathematical Thinking: Behavioral and Brain-Imaging Evidence
Dehaene, S., Spelke, E., Pinel, P., Stanescu, R., & Tsivkin, S. (1999) · Science · doi:10.1126/science.284.5416.970 · Cited by 1,739
A Parametric Study of Prefrontal Cortex Involvement in Human Working Memory
Braver, T. S., Cohen, J. D., Nystrom, L. E., Jonides, J., Smith, E. E., & Noll, D. C. (1997) · NeuroImage · doi:10.1006/nimg.1996.0247 · Cited by 1,694
Human Brain Language Areas Identified by Functional Magnetic Resonance Imaging
Binder, J. R., Frost, J. A., Hammeke, T. A., Cox, R. W., Rao, S. M., & Prieto, T. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-01-00353.1997 · Cited by 1,424
Research works printed but not linked: 1
- P Broca, [Notes on the seat of the faculty of articulate language, followed by an observation of aphemia. Bull Soc Anat Paris 6, 330–357, French. (1861).
Cited by
Sorted by citations
Items 401–500 of 517
Modular Cognitive Architecture Emerges in Large Language Models
Han, P.-R., Andreas, J., Fedorenko, E., et al. (2026) · doi:10.48550/arxiv.2608.13567 · Cited by 2
“…patching across 46 tasks spanning four cognitive domains (Figure 1A), each corresponding to a distinct network in the human brain: language (Fedorenko et al., 2011, 2024a), multiple demand (Duncan, 2010; Assem et al., 2020), physical reasoning (Fischer et al., 2016), and theory of mind…”
InfluentialSelf-citationDo Language Models Align with Brains? Prediction Scores Are Not Enough
Jia, X. (2026) · arXiv.org · doi:10.48550/arxiv.2605.14025 · Cited by 2
“Studies now routinely compare model features with fMRI, ECoG, MEG, eye tracking, and behavioral data in order to ask whether model representations capture brain-relevant structure [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18].”
Lizcano-Cortés, F., Ripollés, P., Barrios, F. A., & Assaneo, M. F. (2026) · Communications Psychology · doi:10.1038/s44271-026-00444-5 · Cited by 4
Li, X. (2026) · doi:10.48550/arxiv.2603.15412 · Cited by 2
“This could explain why language processing recruits substantially different circuitry [Fedorenko et al., 2011].”
Wang, X., Lyu, B., Krieger‐Redwood, K., Souter, N. E., Shafiei, G., Lin, N., Smallwood, J., Jefferies, E., & Du, Y. (2025) · Communications Biology · doi:10.1038/s42003-025-09165-7 · Cited by 2
What does it mean to understand language?
Casto, C., Ivanova, A. A., Fedorenko, E., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2511.19757 · Cited by 2
“…left-hemisphere regions, this system responds when we listen to or read language [27-28] or when we speak [29], but not when we engage in apparently similar but nonlinguistic processes, such as arithmetic, music perception, understanding gestures, or reasoning [30-32] (for reviews, see [10,33]).”
Self-citationDuricy, E., Durisko, C., & Fiez, J. A. (2025) · Cortex · doi:10.1016/j.cortex.2025.10.008 · Cited by 1
“…regions during easy and hard math localizer trials (adding three consecutive addends to a two-digit number to compute a final sum) relative to rest, as well as a whole brain analysis that showed frontoparietal activation in a hard > easy arithmetic contrast (Fedorenko, Behr, & Kanwisher, 2011).”
Distinct neural pathway and its information flow for blind individual's Braille reading
Wang, R., Gong, J., Zhao, C., Xu, Y., & Hong, B. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120852 · Cited by 2
“Future studies using nonsensical Braille words as tactile controls, as done in previous research (Baldassano et al., 2018; Fedorenko et al., 2011; Kim et al., 2017; Pallier et al., 2011; Reich et al., 2011) are warranted.”
ResultVision-Language Models Align with Human Neural Representations in Concept Processing
Bavaresco, A., Kloots, M. D. H., Pezzelle, S., & Fernández, R. (2026) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2026.eacl-long.150 · Cited by 13
“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).”
Meier, E. L., Sheppard, S. M., Sebastian, R., Berube, S. K., Goldberg, E. B., Shea, J., Stein, C. M., & Hillis, A. E. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1288801 · Cited by 2
Wang, X., Krieger‐Redwood, K., Lyu, B., Lowndes, R., Wu, G., Souter, N. E., Wang, X., Kong, R., Shafiei, G., Bernhardt, B. C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.06.556465 · Cited by 5
Arvidsson, C., Torubarova, E., Pereira, A., & Uddén, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.05.547796 · Cited by 1
“It is known to be crucial to higher-level linguistic processing, e.g., syntactic and semantic processing on the sentence level (Fedorenko et al., 2011; Hickok, 2022; Mollica et al., 2020; Stockert et al., 2023; Tyler et al., 2010; Vlooswijk et al., 2010; Malik-Moraleda et al., 2022).”
BackgroundPresentation format affects the behavioural and neural processing costs of sentence reinterpretation
Blott, L. M., Rodd, J. M., & Warren, J. E. (2023) · Language Cognition and Neuroscience · doi:10.1080/23273798.2022.2159992 · Cited by 3
“…particular, therefore, have often relied on alternative visual presentation techniques which standardise temporal exposure to written sentence content across participants, such as phrase-by-phrase or word-by-word presentation (e.g. Fedorenko et al., 2011; Hoenig & Scheef, 2009; Zhu et al., 2012).”
Supramodal Shape Representation in the Human BrainPreprint
Xu, Y., Vignali, L., Sigismondi, F., Crepaldi, D., Bottini, R., & Collignon, O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.22.517472
“These regions were in accord with the high-level 276 linguistic network (Fedorenko et al., 2011; Friederici, 2011) and are considered to underly language277 supported conceptual processing (Xu et al.”
ResultLi, J., Kean, H. H., Fedorenko, E., & Saygin, Z. (2021) · bioRxiv · doi:10.1101/2021.09.15.460550 · Cited by 2
“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-citationEmergentism in neuroscience and beyond
Tachihara, K., & Goldberg, A. E. (2018) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2018.04.009 · Cited by 4
Domain-general brain regions do not track linguistic input as closely as language-selective regionsPreprint
Blank, I., & Fedorenko, E. G. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/076240 · Cited by 7
“However, this network shows virtually no engagement in non-linguistic tasks [11-14] and is therefore languageselective.”
BackgroundMethodSelf-citationNeurobiology of Sign Languages
Corina, D. P., & Blau, S. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-407794-2.00036-5 · Cited by 3
Evidence from formal logical reasoning reveals that the language of thought is not natural language
Kean, H., Fung, A., Jaggers, P., Chen, J., Rule, J. S., Benn, Y., Tenenbaum, J. B., Piantadosi, S. T., Varley, R. A., & Fedorenko, E. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2520095123 · Cited by 2
Self-citationLearning language with the wrong neural scaffolding: the cost of neural commitment to sounds
Finn, A. S., Kam, C. L. H., Ettlinger, M., Vytlacil, J. J., & D’Esposito, M. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2013.00085 · Cited by 24
“Much recent work aims to characterize the functional specificity of sub-regions both within in the IFG (Fiebach et al., 2006; Fedorenko et al., 2011) and the STG (Indefrey and Levelt, 2004) and to more carefully specify the functional anatomy of language (Poeppel and Hickok, 2004).”
Background(2024) · doi:10.1101/2024.11.25.625212 · Cited by 1
Self-citationA two-dimensional space of linguistic representations shared across individualsPreprint
Tuckute, G., Lee, E. J., Ou, Y., Fedorenko, E. G., & Kay, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.21.655330 · Cited by 1
“The language network responds in a highly selective manner to linguistic inputs (e.g., Fedorenko et al. 2011; Monti et al. 2012; Chen et al. 2023; see Fedorenko et al. 2024 for a recent review) and shows sensitivity to linguistic structure at different levels, from sound patterns to sentences…”
Self-citationVinogradova, V., Manini, B., Woll, B., Eimer, M., & Cardin, V. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf332 · Cited by 2
“The visuospatial working memory experiment w as adopted from Fedorenko et al. (2011), with an added perceptual contr ol (color) condition (Fig.”
Billot, A., Jhingan, N., Varkanitsa, M., Wolna, A., Shain, C., Blank, I., Ryskin, R. A., Kiran, S., & Fedorenko, E. G. (2026) · Nature Communications · doi:10.1038/s41467-026-76598-x · Cited by 6
Self-citationCognitive maps as a medium for thought
Yousif, S. R., & Epstein, R. A. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.07.010 · Cited by 2
Monosynaptic connections link functionally similar regions in human cortexPreprint
Xu, R., Bush, A., Takahashi, A., Sisterson, N. D., Walton, A. E., Hutchinson, S., Kammen, A., Neudorfer, C., Kokkinos, V., Nanda, P., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.19.726279 · Cited by 2
“…52 , and several aspects of cognition (executive control supported by the multiple demand network or MD, intuitive physical reasoning, language, theory of mind or TOM, episodic memory and prospection or episodic projection, and functions supported by the default mode network or DMN) 3–5,53– 56 .”
Self-citationMassaroni, V., Donne, V. D., Arcangeli, V., Moriconi, F., & Chieffo, D. P. R. (2026) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2026.101314 · Cited by 1
Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems
Ramezani, P., Kinfe, T., Maier, A. K., et al. (2026) · Frontiers in Language Sciences · doi:10.48550/arxiv.2603.29617 · Cited by 1
“In neurolinguistics, growing evidence suggests that both syntactic and semantic combinatorial processes recruit a left-lateralized fronto-temporal network, including inferior frontal and superior temporal regions [FBK11].”
Huang, Y., Qu, C., Gazzola, V., Garofalo, S., Starita, F., Krebs, R. M., Degni, L., Wei, J., Finotti, G., di Pellegrino, G., et al. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121874 · Cited by 1
“Subject-specific fROIs were defined using a functionally constrained, anatomically guided approach (Fedorenko et al., 2011; Nieto-Casta ˜ n ´ on and Fedorenko, 2012; Abassi and Papeo, 2024; Munin et al., 2025).”
Zhang, Z., Qian, X., Zhang, H., Zhao, Z., Wang, W., & Wei, J. (2026) · Neuroimage Reports · doi:10.1016/j.ynirp.2025.100312 · Cited by 2
“…factors may explain this: First, commonly employed cognitive assessments (e.g., verbal fluency, naming tests) engage hybrid processes involving both executive control and semantic retrieval — the latter typically reliant on the LAN (Benn et al., 2023; Fedorenko et al., 2011; Blank et al., 2014).”
InfluentialCrossroads in the Learning Brain: The Neural Overlap Between Arithmetic and Phonological Processing
Alvarez-Rivero, A., Peters, L., Joanisse, M. F., Gaab, N., & Ansari, D. (2026) · Human Brain Mapping · doi:10.1002/hbm.70446 · Cited by 1
“…directly comparing the network that supports phonological processing of written words and arithmetic concurrently has been severely lacking, with the exception of only a handful of studies (Andin et al. 2015; Evans et al. 2016; Fedorenko et al. 2011; Prado et al. 2011; Simon et al. 2002).”
EEG-Based Decoding of Neural Mechanisms Underlying Impersonal Pronoun Resolution
Zhao, M.-Y., Wang, H.-Q., Qiu, Y.-Y., et al. (2025) · Algorithms · doi:10.3390/a18120778 · Cited by 1
Modality-Agnostic Decoding of Vision and Language from fMRIPreprint
Nikolaus, M., Mozafari, M., Berry, I., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.08.658221
Garcia-de-Soria, M. C., Mathias, B., Keitel, A., et al. (2025) · bioRxiv · doi:10.1101/2025.09.05.674218 · Cited by 1
“As children become more proficient readers, brain responses to complex language processing within the bilateral frontotemporal language network become increasingly more left-lateralized which is thought to reflect increasing specialisation to language-related tasks 7,8,9,10,11 .”
Gao, C., Guo, J., Zhang, L. L., Li, Z., Chen, L., & Feng, L. (2025) · Bilingualism Language and Cognition · doi:10.1017/s1366728925100138
“…memory tasks Bilingualism: Language and Cognition 13 https://doi.org/10.1017/S1366728925100138 Published online by Cambridge University Press (Fedorenko et al., 2011), particularly in representing and maintaining the complex structures (see also Hickok & Poeppel, 2007), the connection from…”
InfluentialDo Language Models Know Who Did What to Whom?
Denning, J. M., Guo, X., Snefjella, B., & Blank, I. (2026) · Open Mind · doi:10.1162/opmi.a.365 · Cited by 4
“First, LLMs can be directly trained on the specific task of thematic role assignment (also called “semantic role labeling”) via supervised learning, wherein training sentences are paired with correct role labels for different words.”
A Cross-Language Study of Tonal Variants in Mandarin in Different Attentional Conditions
Chen, X., Wang, J., & Lu, J. (2025) · Behavioral Science · doi:10.3390/bs15030304 · Cited by 1
“These tasks include the ability to differentiate between the pitch of two neighboring piano keys and appreciate whether a melody is melodious when processing external auditory stim‑ uli (Giuliano et al., 2011; Fedorenko et al., 2011).”
Lei, W., & Wang, B. (2025) · Poznań Studies in Contemporary Linguistics · doi:10.1515/psicl-2023-0071 · Cited by 3
On Representational Dissociation of Language and Arithmetic in Large Language Models
Kisako, R., Kuribayashi, T., & Sasano, R. (2025) · arXiv.org · doi:10.48550/arxiv.2502.11932 · Cited by 1
“The neuroscience field has recently provided some clues to answer this question of language-thought dissociation (Fedorenko et al., 2011; Blank et al., 2014; Fedorenko et al., 2024a); specifically, brain activities during processing linguistic and non-linguistic reasoning stimuli is different…”
Wang, F., Lu, X., Chen, X., Wang, Q., Li, Q., & Li, H. (2024) · Tomography · doi:10.3390/tomography10070082
“Functional connectivity studies have shown that other language-related brain regions are predominantly located in the left temporal lobe, left inferior frontal gyrus, and left middle frontal gyrus, as well as areas extending into the angular gyrus [4,5].”
BackgroundMethodHuybrechts, M., Bruffaerts, R., Pongos, A. L. A., Shain, C., Lipkin, B., Siegelman, M., Wens, V., Sjøgård, M., Blank, I., Goldman, S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.23.533424 · Cited by 1
“Furthermore, this network has been shown to be strongly selective for linguistic input (Fedorenko et al., 2011)(see Fedorenko and Blank (2020) for a review).”
BackgroundSelf-citationOota, S., Arora, J., Gupta, M., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2204.09564 · Cited by 1
“…et al., 2008; Binder et al., 2009), Language Network (related to language processing, understanding, word meaning, and sentence comprehension) (Fedorenko et al., 2011), Task Positive Network (related to attention, salience information) (Binder et al., 2009; Duncan, 2010; Power et al., 2011),…”
BackgroundModulating the left inferior frontal cortex by task domain, cognitive challenge and tDCSPreprint
Nardo, D., Pappa, K., Duncan, J., Zeidman, P., Callaghan, M. F., Leff, A. P., & Crinion, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.05.429968 · Cited by 2
“…consistent results, showing that other nodes within the language network (e.g., superior temporal and inferior parietal cortices) do not show any sensitivity to cognitive demand/difficulty (Fedorenko et al., 2011), whereas nodes within the MDS do exhibit such a sensitivity (Fedorenko et al., 2013).”
ResultStory reading with incidental comprehension and memory: left hemisphere dominance
Vigliecca, N. S. (2021) · Arquivos de Neuro-Psiquiatria · doi:10.1590/0004-282x-anp-2020-0489 · Cited by 1
Asymmetric binarity as a cognitive universal: The rhythm of syntactic structures
Fahey, D., & den Ouden, D. (2020) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2020.100889 · Cited by 2
Extending research on language foundations and evolution
Gong, T., Shuai, L., & Wu, Y. (2018) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.09.001 · Cited by 1
Amalric, M. (2017) · doi:10.70675/a7b02a17z0be9z4e28z82fbzc1cc98cd1040 · Cited by 1
“To further investigate the relation of math statements to language, we performed a sensitive analysis in 7 regions of interest associated with syntactical processing in previous studies: temporal pole (TP), anterior and posterior superior temporal sulcus (aSTS and pSTS), temporo-parietal junction (TPJ), inferior front…”
MethodThe Role of Cognitive Dissonance in Social NetworksPreprint
Akiba, H. T., Dias, Á. M., Ferracioli-Oda, E., Ikeda, R. H., & de Oliveira, M. A. (2011) · Nature Precedings · doi:10.1038/npre.2011.6682.1 · Cited by 1
Corrêa, L. M. S. (2020) · Studies in theoretical psycholinguistics · doi:10.1007/978-94-024-1932-0_11 · Cited by 1
Neural Reuse and the Modularity of Mind: Where to Next for Modularity?
Zerilli, J. (2018) · Biological Theory · doi:10.1007/s13752-018-0309-7 · Cited by 30
Naranjo, N. P., Grande, D. D. R., & Alted, C. G. (2018) · doi:10.1080/02687038.2017.1362686
Liu, X., & Wu, S. (2026) · Brain Sciences · doi:10.3390/brainsci16030251
Roussel, P., Bocquelet, F., Chabardès, S., & Yvert, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.21.586130
Cognitive Expert Language Models Better Align with the Corresponding Brain Systems
Sourati, Z., Wu, M.-H., Ghazizadeh, N., et al. (2026) · doi:10.48550/arxiv.2609.36239
Kean, H. H., & Boix-Adserà, E. (2026) · doi:10.48550/arxiv.2609.32907
“These include word recall; a sentence-versus-pseudoword-list task adapted from the human language localizer (Fedorenko et al., 2011); multi-step deduction over invented categories; MMLU questions (Hendrycks et al., 2020); a Stroop variant (Stroop, 1935) and an adaptation of the Multi-Source…”
The cerebellum specializes for language even in the absence of contralateral neocortical inputs
Wang, B.-J., Tuckute, G., Kean, H. H., et al. (2026) · bioRxiv · doi:10.64898/2026.08.19.745757
“The spatial WM task was introduced in Fedorenko et al. (2011) and has been used in many subsequent studies as a localizer for the multiple demand system (Blank et al., 2014; Shashidhara et al., 2019, 2020, 2024; Diachek et al., 2020; Malik-556 Moraleda et al., 2022).”
Self-citationContent-Sensitive Linguistic Representations in the Human Multiple-Demand NetworkPreprint
Havin, M., Meshulam, M., Karidi, T., Tikochinski, R., Hasson, U., & Goldstein, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.14.749455
“In particular, the multiple-demand (MD) network has been linked to domain-general executive control (Duncan, 2010; Duncan & Owen, 2000; Duncan, 2013), while the language network has been associated with linguistic processing (Fedorenko et al., 2011; Fedorenko et al., 2012).”
The language network does not support music perception across a range of music experiencePreprint
Olson, H. A., Chen, X. J., Boebinger, D., Richardson, H., & Fedorenko, E. G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.03.749155
“We first examined the adults’ responses to these conditions, expecting to find higher responses to language than music or foreign speech in individually-defined language regions, as demonstrated previously (Chen et al., 2023; Deen et al., 2015; Fedorenko et al., 2011).”
Self-citationHeredia, A. A. C., Johnson, M. A., Cremins, D., McGee, T., Elliott, J. L., Chiang, J. N., & Monti, M. M. (2026) · Cognitive Science · doi:10.1111/cogs.70257
The interacting language network: A framework for neuromodulation and clinical translation
Preisig, B. C., Proix, T., Borghesani, V., Brem, S., Marchesotti, S., & Guggisberg, A. G. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106972
Neural and behavioural correlates of theory of mind reasoning in five-year-old children born preterm
Abel, S., Thye, M. D., Mckinnon, K., et al. (2026) · Imaging neuroscience · doi:10.1162/imag.a.1347
“In individual participants, this network is functionally distinct from brain regions that support language ( Fedorenko et al., 2011; Paunov et al., 2022), attention ( Mars et al., 2012; Scholz et al., 2009), and executive functions ( Saxe, Schulz, et al., 2006).”
Schwartz, A. M., Gilmore, N. M., & Meier, E. L. (2026) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2026.109567
Scaling laws for group-constrained, subject-specific task fMRI analysesPreprint
Gao, R. S., & Ivanova, A. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.22.740076
“…the HCP dataset, the ‘language’ contrast, story-math, likely recruits two different functional networks: the language network, which is sensitive to sentence-level meaning (Fedorenko et al., 2011) and the default network, which tracks story-level meanings (Braga et al., 2020; Simony et al., 2016).”
Chen, S.-H. K., Nagano, M., Gonnerman, L. M., Liu, H. T., & Meier, E. L. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1847242
“Furthermore, work by Fedorenko et al. indicates that the MD network is not highly engaged during language processing when task demands remain low and are linguistic in nature (Diachek et al., 2020; Fedorenko et al., 2011, 2024; MacGregor et al., 2022), a distinction we return to in the section.”
Tao, C., Cui, S., Shen, Y., Cheng, Y., Zhu, J., & Yu, Y. (2026) · iScience · doi:10.1016/j.isci.2026.116903
Selectivity for high-level language processing is highly localized in individual brains
Belisle, R. M., Scott, T. L., & Perrachione, T. K. (2026) · bioRxiv · doi:10.64898/2026.07.16.738967
“Participants completed three in-scanner tasks: a language localizer (Scott et al., 2017), a verbal working memory task (Fedorenko et al, 2011), and a spatial working memory task (Fedorenko et al, 2011; Lee et al., 2024).”
InfluentialNeural Coding of Multiple Linguistic Components During Reading Comprehension Across Development
Chen, J., Feng, X., Zhang, J. I.-X., Tang, C., Liu, L., Fan, S., Zhao, N., Lei, Y., Yang, X., Meng, X., et al. (2026) · Neurobiology of Language · doi:10.1162/nol.a.286
“In adults, language processing is characterized by a high degree of functional specialization and neural efficiency, primarily localized within a left-lateralized fronto-temporal network (Fedorenko et al., 2011; Mesulam, 2016; Vigneau et al., 2006).”
Chen, J., Zhao, N., Yang, Z., Fu, C., Yu, X., & Yang, X. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122120
How visual attention underpins reading: Converging evidence from a meta-analysis of fMRI studies
Gao, Y., Zhu, B., & Zhou, W. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122114
Abbott, N. T., Beasley, L., & Cler, G. J. (2026) · Neurobiology of Language · doi:10.1162/nol.a.277
“…recruit left-hemisphere (and, to a lesser extent, right-hemisphere) temporal regions, as well as cerebellar regions associated with language, more robustly during higher-level speech processing, consistent with models of language processing (Fedorenko et al., 2011; Malik-Moraleda et al., 2022).”
Muès, M., Cochell, L. M., Mathur, A., & Booth, J. R. (2026) · Brain and Language · doi:10.1016/j.bandl.2026.105787
Karpychev, V., Александровна, М. С., & Dragoy, O. (2026) · Brain Structure and Function · doi:10.1007/s00429-026-03116-6
“Language comprehension and production are predominantly supported by the core left-lateralized language-related network (LRN) , which encompasses the lateral frontal and temporal brain regions (Tyler et al. 2011; Fedorenko et al. 2011; for review, see Price 2012; Fedorenko et al. 2024).”
Brain-LLM Alignment Tracks Training Data, Not TypologyPreprint
Guo, D., Wu, J., & Yiu, S. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.23032
“We operationalize this as: the IFG (associated with syntactic processing; Fedorenko et al. 2011) should show a steeper alignment gradient with typological distance than the PTL (associated with lexico-semantic processing; Hickok and Poeppel 2007).”
Neurotransmitter circuits of the human language network and their disruption in post-stroke aphasiaPreprint
Alves, P. N., Martins, I. P., Rorden, C., Berthier, M. L., Dávila, G., Leff, A. P., de Schotten, M. T., & Forkel, S. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.14.725241
Ladwig, Z., Kermani, K. Z., Park, Y., Housteau, E., Dworetsky, A., Labora, N., Hernandez, J. J., Dorn, M., Smith, D. M., Nee, D. E., et al. (2026) · Neuron · doi:10.1016/j.neuron.2026.04.011 · Cited by 18
“Evidence suggests that contrasting Sentence > Nonwords blocks reliably identifies high-level language processing regions, including in the LPFC. 37,52 Auditory language task: The auditory language task was adapted from Scott et al. 86 using publicly available code and stimuli…”
InfluentialPamplona, G. S., Stettler, S., Vieira, B. H., et al. (2026) · bioRxiv · doi:10.64898/2026.04.17.719130
“Encompassing Broca’s area, the left IFG is involved in semantic processing of word reading (Fedorenko et al., 2011; Sahin et al., 2009), as well as language comprehension, production, and complex higher-order linguistic processes (Bitan et al., 2009; Friederici, 2011; Pollack et al., 2015).”
Cui, Y., Sun, J., Sun, Y., Wang, Y., Zhang, Y., Li, J., Wang, S., Zhou, H., Hale, J., Zong, C., et al. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.10627
“This normalization ensures that the LPI captures relative language specificity rather than absolute effect size differences [34].”
InfluentialHodapp, A., & Meyniel, F. (2025) · eLife · doi:10.1101/2025.07.31.667879
Distributed cortical network dynamics of binocular convergent eye movements in humans
Hajebrahimi, F., Gohel, S., Cole, M. W., & Alvarez, T. L. (2026) · Network Neuroscience · doi:10.1162/netn.a.549
“Through stimulus-evoked or task-evoked functional magnetic resonance imaging (fMRI), many studies have characterized localized neural responses across domains such as visual, language, and motor controls (Brett et al., 2002; Fedorenko et al., 2011; Genon et al., 2018; Tootell et al., 1995).”
Zarger, P., & Rajimehr, R. (2026) · bioRxiv · doi:10.64898/2026.02.17.706260
“The human language network (LN) comprises a set of brain regions that collectively support the core components of language processing, including comprehension, production, and integration of linguistic information (Binder et al., 1997; Fedorenko et al., 2011).”
Hierarchical Signatures of Language in the Human BrainPreprint
Eichert, N., Favre, C., Cabalo, D. G., Ngo, A., DeKraker, J., Hwang, Y., Smith, M., Crooks, V., Bautin, P., Rodríguez‐Cruces, R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.15.705992
“Language is one of the most extensively studied brain functions, in both clinical (Bookheimer 2002; Saur and Hartwigsen 2012) and basic research settings (Fedorenko et al. 2011; Hickok 2012), owing to its central importance for the human experience (Tomasello 2009; Fitch 2010).”
Fender, H. E., McClure, K. L., & HAMRICK, P. J. J. (2026) · Journal of Cognition and Development · doi:10.1080/15248372.2026.2632278
Sun, W., DiNicola, L. M., & Buckner, R. L. (2025) · eLife · doi:10.64898/2025.12.15.694432
“We were especially struck by the hemispheric dissociation of the LANG network in both studies, as this network is the most consistently anatomically left-lateralized association network (e.g., Braga et al., 2020; Fedorenko, Behr, and Kanwisher, 2011; Lipkin et al., 2022).”
Anurova, I., Cieśla, K., & Amedi, A. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121638
“Dorsolateral prefrontal cortex (DLPFC) has been suggested to be an additional substrate for semantic processing (Bottini et al., 1994; Fedorenko et al., 2011), in a subregion partially distinct from the one serving general executive functions (Fedorenko et al., 2011).”
A Tragedy of Errors: The State of Psychedelic Research in the Treatment of Alcohol Use Disorder
Srivastava, A. B., & Gold, M. S. (2025) · Brain Sciences · doi:10.3390/brainsci15111190 · Cited by 3
“Other networks potentially involved underlie language (language network) [124,125] and momentary task control (the fronto-parietal network) [126–129], the latter of which may be engaged, on a fast timescale, when confronted with alcohol “in the moment”.”
Hu, Z., Chen, J., Yang, J., Xie, H., Liu, J., Zhang, P., Wu, Q., Doosti, M.-H., Rong, C., Wang, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.29.679140
“Functional mapping methods demonstrate that language-specific regions exhibit minimal activation during tasks such as mental arithmetic, working-memory maintenance, or cognitive control 72–74 .”
Distributed Cortical Network Dynamics of Binocular Convergent Eye Movements in Humans
Hajebrahimi, F., Gohel, S., Cole, M., et al. (2025) · bioRxiv · doi:10.1101/2025.08.15.670412
Tuning the brain: musical expertise modulation on the shared mechanism of language and music
Wang, X., & Zeng, T. (2025) · Language Cognition and Neuroscience · doi:10.1080/23273798.2025.2533873 · Cited by 1
“Neuroimaging studies corroborate this dissociation, showing limited fMRI activation in core language-processing regions (e.g. Broca ’ s area) in response to musical stimuli (X. Chen et al., 2023; Fedorenko et al., 2011).”
Imagined Speech Reconstruction with 3D Neural Metabolism and Large Language Model IntegrationPreprint
Rajagopal, G., Chaudhari, A., LaRocco, J., Xue, J., Zachariah, E., Varanasi, S., & Rothman, D. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.30.667805
“For instance, studies utilizing fMRI have shown synchronous activation patterns between Broca’s and Wernicke’s areas, thus highlighting a functionally interconnected network essential for fluency and comprehension (Tripathi, 2024; Fedorenko et al., 2011).”
The neural mechanisms underlying semantic unification and semanteme integration
Zhang, W., Dong, J., Feng, G., Wang, G., Gu, Z., Gao, X., Liu, X., & Yan, H. J. (2025) · Brain and Language · doi:10.1016/j.bandl.2025.105629
Domain-General Brain Networks Support Language DevelopmentPreprint
Zhou, Z., Yang, X. H., Ju, P., Rong, M., Jefferies, E., & Zuo, X. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.10.663607
“Language processing recruits a domain-specific language-selective network, which includes a set of left-lateralized frontal and temporal regions (Binder et al, 1997; Fedorenko et al, 2010, 2011; Fedorenko and Thompson Schill, 2014; Friederici, 2011; Scott et al, 2017; Vigneau et al, 2006).”
Give or Take: Semantic Priming from Sentences to Two-Digit Operations
Ayala-Cuesta, M., Castro, S., & Macizo, P. (2025) · Brain Sciences · doi:10.3390/brainsci15060662
“For example, the bilateral intraparietal cortex (IPS) appears to be sensitive to the processing of numerical (e.g., magnitude) and linguistic content [12] (however, see [13,14] for data against a potential overlap between the language network and mathematics).”
Mustofa, M. I., Martina, F., & Eliya, I. (2024) · Studia Neophilologica · doi:10.1080/00393274.2024.2417928
“Asp explains that, for instance, Evelina Fedorenko’s work on linguistic processing in humans (Fedorenko et al. 2011) suggests a domain-specific language network with modality-independent responses, which challenges traditional domain-general cognitive models.”
Revisiting neuroplasticity mechanisms in aphasia recovery: Commentary on Billot and Kiran
Meier, E. L., & Johnson, J. P. (2024) · Brain and Language · doi:10.1016/j.bandl.2024.105480
Tang, X., Turesky, T. K., Escalante, E. S., et al. (2024) · bioRxiv · doi:10.1101/2023.06.22.546194
“It has recently been demonstrated that this language network exhibits selective activation for high-level linguistic processes in adults (Fedorenko et al., 2011) across 12 45 languages and 12 language families (Malik-Moraleda et al., 2022), thus representing a universal neural template for language…”
Kawasaki, A., Matsuzaki, Y., & Kawada, T. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13082399
Avesani, M., Graziella, B., Francesco, A., Lucilla, F., Vianello, C., Assunta, Z., Cristiana, T., Cinzia, S., Nicola, S., Manuela, C., et al. (2024) · Archives of Case Reports · doi:10.29328/journal.acr.1001086
Botch, T. L., & Finn, E. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.07.556725
“These studies have revealed neural territory specific to language 5,73 that closely interacts with other networks involved in cognitive control and theory of mind 4,74,75 .”
Timecourse and source localization of abstract and concrete semantic representationsPreprint
Vignali, L., Xu, Y., Turini, J., Collignon, O., Crepaldi, D., & Bottini, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.23.546231
“The dATL belongs to the high-level linguistic system in the left perisylvian network, encompassing the inferior frontal gyrus, the lateral temporal cortex, and the inferior parietal cortex (for instance, Fedorenko et al., 2011; Friederici, 2011).”
Background
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review