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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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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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that the brain regions investigated here are surely not the only ones engaged in or necessary for language processing.”
boosted- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The order of conditions was counterbalanced; each condition was equally likely to appear in the earlier vs. later parts of each run and was as likely to follow every other condition as it was to precede it.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The resulting piece had component sounds similar to the intact music but lacked musical structure.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These data support a clear distinction between language and other cognitive processes, resolving the prior conflict between the neuropsychological and neuroimaging literatures.”
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, ROI analyses can obscure functional heterogeneity within an ROI or miss important overlapping regions outside the borders of the ROI.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- 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
- 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.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ROI-based analyses afford great statistical power and reduce the blurring of functional activations inevitable in group analyses.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“(here and throughout the study, responses were estimated using data from left-out runs not used for defining the ROIs).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
The individual-subject overlap analysis strategy has almost never been applied in neuroimaging investigations of high-level linguistic processing.
“a data analysis strategy that has almost never been applied in neuroimaging investigations of high-level linguistic processing”
The paper relies on this without support.
Most claims about the overlap between linguistic and nonlinguistic processes have concerned syntactic processing.
“However, most claims about the overlap between linguistic and nonlinguistic processes have concerned (i) syntactic processing, which is included in our functional contrast, and (ii) brain regions, which are robustly activated by our localizer (e.g., Broca's area).”
The paper relies on this without support.
Most claims about the overlap between linguistic and nonlinguistic processes have concerned brain regions robustly activated by the language localizer.
“However, most claims about the overlap between linguistic and nonlinguistic processes have concerned (i) syntactic processing, which is included in our functional contrast, and (ii) brain regions, which are robustly activated by our localizer (e.g., Broca's area).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The hard > easy contrast has been found to robustly activate regions of the MD network (Fedorenko et al., 2011(Fedorenko et al., , 2013)).”
From Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation (2018) · cites this paper as Fedorenko et al., 2011
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Neuroimaging studies have consistently shown that the language network -a set of brain regions responsible for language comprehension and production -remains largely inactive during various reasoning tasks”
From Training Large Language Models to Reason in a Continuous Latent Space (2024) · cites this paper as Fedorenko et al., 2011
Experimental — accuracy not yet 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).
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“Note that these tasks were not designed to be directly comparable to one another, even though they have all been shown to produce behavioral difficulty effects with the hard condition, leading to lower accuracies and longer reaction times (2, 27).”
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“(This data set partially overlaps with the data set reported in [13].”
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“and bodies (Kanwisher 2010), and high-level regions specialized for understanding the meaning of sentences (Binder et al. 1997; Fedorenko et al. 2011) and the contents of other people’s thoughts (Saxe and Kanwisher 2003; Saxe and Powell 2006).”
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Self-citationDissociating language and thought in large language models
Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.011 · Cited by 345
“…from phonological/sub-lexical, to word level, to phrase/sentence level (Bautista and Wilson, 2016; Blank and Fedorenko, 2020; Blank et al., 2016; Fedorenko et al., 2011, 2012, 2020; Regev et al., 2021); and supports linguistic operations that are related to both the processing of word meanings…”
Self-citationNeural overlap in processing music and speech
Peretz, I., Vuvan, D. T., Lagrois, M.-É., & Armony, J. L. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0090 · Cited by 310
“Similarly, the so-called languageselective regions in the left posterior temporal lobe show a significant, albeit weaker, response to music [26].”
BackgroundThe language network as a natural kind within the broader landscape of the human brain
Fedorenko, E. G., Ivanova, A. A., & Regev, T. I. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00802-4 · Cited by 374
“…stimulus 91,100–102 — and show little or no response when participants perform demanding cognitive tasks, including solving arithmetic problems 22,91,103,104 and logic puzzles 105,106 , understanding computer code 107,108 and carrying out working memory and cognitive control tasks…”
BackgroundInfluentialSelf-citationBroca’s Area Is Not a Natural Kind
Fedorenko, E. G., & Blank, I. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.001 · Cited by 272
“These tasks include music perception [16], arithmetic processing [16,60,61], executive function tasks [16,49], thinking about others’ mental states (A.”
BackgroundSelf-citationThe revolution will not be controlled: natural stimuli in speech neuroscience
Hamilton, L. S., & Huth, A. G. (2018) · Language Cognition and Neuroscience · doi:10.1080/23273798.2018.1499946 · Cited by 322
The neurobiology of language beyond single-word processing
Peter Hagoort, A. (2019) · Science · doi:10.1126/science.aax0289 · Cited by 256
A Domain-General Cognitive Core Defined in Multimodally Parcellated Human Cortex
Assem, M., Glasser, M. F., Essen, D. C. V., & Duncan, J. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa023 · Cited by 340
“The location of these regions midway between higher visual areas, auditory areas and language and semantic areas (Fedorenko et al., 2011; Pobric et al., 2007; Visser et al., 2010) suggests a genuine MD region, situated to integrate higher visual, auditory and semantic/language processing.”
BackgroundFedorenko, E., & Varley, R. (2016) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13046 · Cited by 247
Self-citationThe role of domain-general cognitive control in language comprehension
Fedorenko, E. G. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00335 · Cited by 246
“In Fedorenko et al. (2011) we tried to ameliorate this concern by performing a whole-brain search—at liberal thresholds—for overlap between the responses to the language contrast (sentences > pseudoword lists) and the hard > easy contrast in each of the non-linguistic demanding tasks.”
BackgroundResultInfluentialSelf-citationNieto-Castañón, A., & Fedorenko, E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.065 · Cited by 247
“Indeed, we have recently shown for the case of high-level language processing that fROI-based methods reveal much greater functional specificity than was apparent in prior work using traditional methods (Fedorenko et al., 2011).”
Self-citationfMRI reveals language-specific predictive coding during naturalistic sentence comprehension
Shain, C., Blank, I. A., van Schijndel, M., Schuler, W., & Fedorenko, E. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107307 · Cited by 215
Self-citationInfants’ brain responses to speech suggest Analysis by Synthesis
Kuhl, P. K., Ramírez, R. R., Bosseler, A. N., Lin, J.-F. L., & Imada, T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1410963111 · Cited by 244
Neural correlate of the construction of sentence meaning
Fedorenko, E., Scott, T. L., Brunner, P., Coon, W. G., Pritchett, B., Schalk, G., & Kanwisher, N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1612132113 · Cited by 199
Self-citationBraga, R. M., DiNicola, L. M., & Buckner, R. L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.11.873174 · Cited by 23
“2 in Fedorenko et al. 2011; Fedorenko et al. 2010; 2012).”
fMRI reveals language-specific predictive coding during naturalistic sentence comprehensionPreprint
Shain, C., Blank, I., van Schijndel, M., Schuler, W., & Fedorenko, E. G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/717512 · Cited by 26
“, Smith & Levy, 2013) – is primarily evident during naturalistic sentence processing in (1) the domain-specific, fronto-temporal language (LANG) network (Fedorenko et al., 2011), or (2) the domain-general, fronto-parietal multiple demand (MD) network (Duncan, 2010).”
BackgroundSelf-citationAn investigation across 45 languages and 12 language families reveals a universal language network
Malik-Moraleda, S., Ayyash, D., Gallée, J., Affourtit, J., Hoffmann, M., Mineroff, Z., Jouravlev, O., & Fedorenko, E. G. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01114-5 · Cited by 261
Self-citationLanguage is primarily a tool for communication rather than thought
Fedorenko, E., Piantadosi, S. T., & Gibson, E. A. F. (2024) · Nature · doi:10.1038/s41586-024-07522-w · Cited by 190
“…could be symbolic but non-linguistic (for example, ‘9’), and the use of symbolic non-linguistic representations does not engage linguistic resources (for example, mathematical reasoning elicits no response in the language brain areas and is preserved in individuals with severe aphasia 51,77–79 ).”
BackgroundInfluentialSelf-citationLack of selectivity for syntax relative to word meanings throughout the language network
Fedorenko, E., Blank, I. A., Siegelman, M., & Mineroff, Z. (2020) · Cognition · doi:10.1016/j.cognition.2020.104348 · Cited by 170
“As expected, and replicating prior work (e.g., Fedorenko et al., 2010; Fedorenko et al., 2011; Blank et al., 2016; Mahowald & Fedorenko, 2016), the language fROIs showed a robust sentences > nonwords effect (ts(48)>8.”
BackgroundResultSelf-citationThought Beyond Language: Neural Dissociation of Algebra and Natural Language
Monti, M. M., Parsons, L. M., & Osherson, D. N. (2012) · Psychological Science · doi:10.1177/0956797612437427 · Cited by 171
Syntactic processing is distributed across the language system
Blank, I., Balewski, Z. Z., Mahowald, K., & Fedorenko, E. G. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.069 · Cited by 170
“Specifically, language regions respond at or below a lowlevel baseline to tasks that have general demands similar to those of the sentence–picture matching task (see Fedorenko et al., 2011, 2012a).”
InfluentialSelf-citationBlank, I., Kanwisher, N., & Fedorenko, E. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00884.2013 · Cited by 181
Self-citationArchitecture and organizational principles of Broca's region
Amunts, K., & Zilles, K. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.06.005 · Cited by 182
Hasson, U., Egidi, G., Marelli, M., & Willems, R. M. (2018) · Cognition · doi:10.1016/j.cognition.2018.06.018 · Cited by 157
Functional Network Dynamics of the Language System
Chai, L. R., Mattar, M. G., Blank, I., Fedorenko, E. G., & Bassett, D. S. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw238 · Cited by 169
“High-level language processing recruits an extended set of cortical regions in the human brain (Blank et al. 2014; Tie et al. 2014) that are distinct from those recruited for other cognitive functions (Fedorenko et al. 2011; Blank et al. 2014).”
BackgroundSelf-citationBrain Functional and Structural Predictors of Language Performance
Skeide, M. A., Bräuer, J., & Friederici, A. D. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv042 · Cited by 186
“…unit of sentence processing across varying experimental designs and task demands (Caplan et al. 2008) that can be spatially disentangled from surrounding domain-general areas serving working memory, cognitive control and music processing (Makuuchi et al. 2009; Fedorenko et al. 2011, 2012).”
BackgroundA new fun and robust version of an fMRI localizer for the frontotemporal language system
Scott, T. L., Gallée, J., & Fedorenko, E. (2017) · Cognitive Neuroscience · doi:10.1080/17588928.2016.1201466 · Cited by 125
“This localizer has been successfully used in a number of studies (Axelrod, Bar, Rees, & Yovel, 2015; Blank, Kanwisher, & Fedorenko, 2014; Fedorenko et al., 2011, 2012; Koster-Hale & Saxe, 2011; Maruyama, Pallier, Jobert, Sigman, & Dehaene, 2012; Xu et al., 2015).”
Self-citationProbabilistic atlas for the language network based on precision fMRI data from >800 individuals
Lipkin, B., Tuckute, G., Affourtit, J., Small, H., Mineroff, Z., Kean, H., Jouravlev, O., Rakocevic, L. I., Pritchett, B., Siegelman, M., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01645-3 · Cited by 160
Self-citationMahowald, K., & Fedorenko, E. G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.073 · Cited by 113
Self-citationMatchin, W., Hammerly, C. M., & Lau, E. F. (2016) · Cortex · doi:10.1016/j.cortex.2016.12.010 · Cited by 175
“Therefore these regions might not always activate during sentence comprehension, and damage will not necessarily result in sentence comprehension deficits.”
BackgroundResultThe language skeleton after dissecting meaning: A functional segregation within Broca's Area
Goucha, T., & Friederici, A. D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.011 · Cited by 171
“Previous studies (e.g. Fedorenko et al., 2011) claimed that no brain area was more sensitive to syntactic information than to lexical information, therefore we also tested whether part of Broca's area responds more to the jabberwocky condition (JP) than to the scrambled real words (RWO).”
Diachek, E., Blank, I., Siegelman, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/744094 · Cited by 55
“…tasks, like arithmetic calculations, executive function tasks, music perception, action/gesture observation, and non-verbal social information (Fedorenko et al., 2011; Monti et al., 2012; Pritchett et al., 2018; Jouravlev et al., 2019; Paunov, 2019; see Fedorenko & Varley, 2016, for a review).”
BackgroundMethodSelf-citationPrediction during language comprehension: what is next?
Ryskin, R. A., & Nieuwland, M. S. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.08.003 · Cited by 205
“Unpredictable linguistic stimuli are known to evoke their largest responses in the language network – a set of frontal and temporal brain regions which respondmost strongly and selectively during language comprehension [127,128].”
BackgroundThe neural and neurocomputational bases of recovery from post-stroke aphasia
Stefaniak, J. D., Halai, A. D., & Ralph, M. A. L. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-019-0282-1 · Cited by 176
Vul, E., & Pashler, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.027 · Cited by 142
“Some initial attempts have been made to address some of these challenges (Fedorenko et al., 2011; Xu et al., 2009; Yarkoni et al., 2011), but much remains to be done.”
BackgroundGeranmayeh, F., Chau, T. W., Wise, R. G., Leech, R., & Hampshire, A. (2017) · Brain · doi:10.1093/brain/awx134 · Cited by 151
“…such as phonological, semantic, and syntactic processing (Indefrey and Levelt, 2004; Hickok and Poeppel, 2007; Rauschecker and Scott, 2009; Fedorenko et al., 2011; Price, 2012), but also on widely distributed and often overlapping brain regions that make broader, or ‘domain-general’,…”
BackgroundIntrinsic functional network architecture of human semantic processing: Modules and hubs
Xu, Y., Lin, Q., Han, Z., Yong He, A., & Bi, Y. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.004 · Cited by 139
“…Thompson-Schill, 2014), which is more strongly activated by language tasks contrasted with math, working memory, cognitive control, or music tasks (Fedorenko et al., 2011) and is distinguished from the peripheral language-related sensorimotor systems such as the visual word form area (Dehaene and…”
BackgroundResultInfluentialThe neural architecture of language: Integrative modeling converges on predictive processing
(2020) · doi:10.1101/2020.06.26.174482 · Cited by 129
Self-citationWhat exactly is Universal Grammar, and has anyone seen it?
Dąbrowska, E. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00852 · Cited by 177
“…functions are not strongly localized: many other areas outside the classical “language areas” are active during language processing; and, conversely, the language areas may also be activated during non-linguistic processing (Stowe et al., 2005; Anderson, 2010; see, however, Fedorenko et al., 2011).”
BackgroundFluid intelligence is supported by the multiple-demand system not the language system
Woolgar, A., Duncan, J., Manes, F. F., & Fedorenko, E. G. (2018) · Nature Human Behaviour · doi:10.1038/s41562-017-0282-3 · Cited by 106
Self-citationFrom task structures to world models: what do LLMs know?
Yildirim, I., & Paul, L. A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.02.008 · Cited by 138
Beyond Functional Connectivity: Investigating Networks of Multivariate Representations
Anzellotti, S., & Coutanche, M. N. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.12.002 · Cited by 130
“Within the language network [51], NL-MVPD individuated four subnetworks reliably across two different tasks performed by the participants.”
MethodChild first language and adult second language are both tied to general-purpose learning systems
Hamrick, P., Lum, J. A. G., & Ullman, M. T. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1713975115 · Cited by 120
“Domain generality does not, however, preclude the concomitant existence of domain-specific substrates for language, either due to ontogenetic (developmental) or phylogenetic (evolutionary) specialization within these systems, or to additional specialized circuitry (1, 5, 7, 9, 13, 37, 59)—although the actual demonstra…”
BackgroundSystematic, balancing gradients in neuron density and number across the primate isocortex
Cahalane, D. J., Charvet, C. J., & Finlay, B. L. (2012) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2012.00028 · Cited by 112
“, 1997), for judging others’ actions (Saxe and Kanwisher, 2003), for linguistic functions (Fedorenko et al., 2011) and so on.”
BackgroundVerbal Working Memory as Emergent from Language Comprehension and Production
Schwering, S. C., & MacDonald, M. C. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00068 · Cited by 128
“, 2016; Van de Cavey and Hartsuiker, 2016; Hepner and Nozari, 2019), and interest in how distinct brain networks must coordinate to accomplish language comprehension and other complex cognitive processes (Fedorenko et al., 2011; Fedorenko, 2014).”
BackgroundMusic and Language Syntax Interact in Broca’s Area: An fMRI Study
Kunert, R., Willems, R. M., Casasanto, D. J., Patel, A. D., & Peter Hagoort, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141069 · Cited by 138
“[42,51] found different activated brain regions when comparing a music-localizer based on a scrambling manipulation to a languagelocalizer based on the reading of sentences versus lists of non-words.”
BackgroundIndividual Brain Charting, a high-resolution fMRI dataset for cognitive mapping
Pinho, A. L., Amadon, A., Ruest, T., Fabre, M., Dohmatob, E., Denghien, I., Ginisty, C., Becuwe-Desmidt, S., Roger, S., Laurier, L., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.105 · Cited by 158
An architecture for encoding sentence meaning in left mid-superior temporal cortex
Frankland, S., & Greene, J. D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421236112 · Cited by 168
“However, lmSTC is by no means the only region consistently implicated in higher-order semantic processing, as research has reliably documented the involvement of the anterior regions of the temporal lobe (20, 22), left inferior parietal lobe (12, 20), and left inferior frontal cortices (13, 14).”
BackgroundMonitoring and Control in Language Production
Nozari, N., & Novick, J. M. (2017) · Current Directions in Psychological Science · doi:10.1177/0963721417702419 · Cited by 152
A distinct cortical network for mathematical knowledge in the human brain
Amalric, M., & Dehaene, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.001 · Cited by 126
Composition is the Core Driver of the Language-selective Network
Mollica, F., Diachek, E., Mineroff, Z., Kean, H., Siegelman, M., Piantadosi, S. T., Futrell, R., Qian, P., & Fedorenko, E. G. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00005 · Cited by 115
“In addition, each participant completed a spatial working memory task (Fedorenko et al., 2011), used in some control analyses to characterize brain regions sensitive to sentence scrambling, as described below.”
MethodSelf-citation“Visual” Cortex of Congenitally Blind Adults Responds to Syntactic Movement
Lane, C., Kanjlia, S., Omaki, A., & Bedny, M. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1256-15.2015 · Cited by 120
“Previous studies have shown that a subset of areas within prefrontal and lateral temporal cortex are sensitive to linguistic con- tent, but not to difficulty of working memory tasks (Fedorenko et al., 2011).”
BackgroundHsu, N. S., Jaeggi, S. M., & Novick, J. M. (2017) · Brain and Language · doi:10.1016/j.bandl.2016.12.006 · Cited by 100
Paunov, A. M., Blank, I., & Fedorenko, E. G. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00619.2018 · Cited by 101
“1244 0022-3077/19 Copyright © 2019 the American Physiological Society www.jn.org Downloaded from www.physiology.org/journal/jn at (174.063.084.192) on April 5, 2019. memory, inhibitory control, and music (Fedorenko et al. 2011, 2012; Monti et al. 2012; see Fedorenko and Varley 2016 for a review).”
BackgroundInfluentialSelf-citationHow fMRI Can Inform Cognitive Theories
Mather, M., Cacioppo, J. T., & Kanwisher, N. (2013) · Perspectives on Psychological Science · doi:10.1177/1745691612469037 · Cited by 109
Self-citationMineroff, Z., Blank, I., Mahowald, K., & Fedorenko, E. G. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.09.011 · Cited by 91
“Replicating prior work (Fedorenko et al., 2011), we find no response to the spatial WM task in the language fROIs.”
Self-citationDomain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions
Blank, I., & Fedorenko, E. G. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3642-16.2017 · Cited by 93
“, 2016) but, critically, shows virtually no engagement in non-linguistic tasks and is therefore language-selective (Fedorenko et al., 2011; for a review, see: Fedorenko and Varley, 2016).”
BackgroundSelf-citationThe Conceptual mind: new directions in the study of concepts
Margolis, E., & Laurence, S. (2016) · Choice Reviews Online · doi:10.5860/choice.193092 · Cited by 234
Language-related domain-specific and domain-general systems in the human brain
Campbell, K. L., & TYLER, L. K. (2018) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.04.008 · Cited by 103
Disrupted dynamic network reconfiguration of the language system in temporal lobe epilepsy
He, X., Bassett, D. S., Chaitanya, G., Sperling, M. R., Kozlowski, L., & Tracy, J. I. (2018) · Brain · doi:10.1093/brain/awy042 · Cited by 93
“As the left middle frontal gyrus has been implicated in verbal working memory (Fedorenko et al., 2011), this reduced recruitment in TLE may also reflect some insufficiency in verbal working memory.”
BackgroundFedorenko, E. G. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.023 · Cited by 84
Self-citation“Visual” Cortex Responds to Spoken Language in Blind Children
Bedny, M., Richardson, H., & Saxe, R. R. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0634-15.2015 · Cited by 112
“Furthermore, language responses in the frontal lobe are known to vary in their anatomical location across individuals (Fedorenko et al., 2011).”
BackgroundFunctional neuroanatomy of language without speech: An ALE meta‐analysis of sign language
Trettenbrein, P. C., Papitto, G., Friederici, A. D., & Zaccarella, E. (2020) · Human Brain Mapping · doi:10.1002/hbm.25254 · Cited by 83
Kim, J. S., Kanjlia, S., Merabet, L. B., & Bedny, M. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0997-17.2017 · Cited by 76
“To examine responses to sentences in the inferior frontal gyrus (IFG), we defined individual ROIs as top 5% of vertices responsive in the BWs TCs or VWs FF strings contrasts within a published IFG mask (Fedorenko et al., 2011).”
Pandora's growing box: Inferring the evolution and development of hominin brains from endocasts
Zollikofer, C. P. E., & León, M. S. P. D. (2013) · Evolutionary Anthropology Issues News and Reviews · doi:10.1002/evan.21333 · Cited by 94
Wang, X.-Y., Margulies, D., Smallwood, J., et al. (2020) · bioRxiv · doi:10.1101/2020.01.16.908327 · Cited by 81
“The contrast between these conditions has been shown to reveal DMN (easy > hard) and MDN (hard > easy) (Fedorenko et al., 2011).”
BackgroundMethodInfluentialNo evidence for differences among language regions in their temporal receptive windows
Blank, I., & Fedorenko, E. G. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116925 · Cited by 74
“nonwords that might engage such non-linguistic processes, the identified regions exhibit robust language selectivity in their responses, showing little or no response to nonlinguistic tasks (Fedorenko et al., 2011; Fedorenko et al., 2012a; Pritchett et al., 2018; Ivanova et al., 2019; Jouravlev et al., 2019; For a rev…”
BackgroundSelf-citationLanguage Development across the Life Span: A Neuropsychological/Neuroimaging Perspective
Rosselli, M., Ardila, A., Matute, E., & Vélez-Uribe, I. (2014) · Neuroscience Journal · doi:10.1155/2014/585237 · Cited by 118
“Taken together, all these neuroimaging studies contribute to a better understanding of the neurological bases of language development across the life span [105], particularly the development of word recall as measured by verbal fluency and confrontation naming tasks.”
BackgroundOrganizational Principles of Abstract Words in the Human Brain
Wang, X., Wu, W. H., Ling, Z.-H., Xu, Y., Fang, Y., Wang, X., Binder, J. R., Men, W., Gao, J., & Bi, Y. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx283 · Cited by 107
“2010) and has been found to show functional specificity for high-level linguistic processing, but not for a variety of non-linguistic functions including arithmetic, cognitive control, music and working memory (Fedorenko et al. 2011).”
BackgroundHu, J., Small, H., Kean, H. H., et al. (2021) · bioRxiv · doi:10.1101/2021.09.10.459596 · Cited by 77
“46 Discussion We examined neural responses to cognitive demands associated with high-level language production—lexical access and phrase-structure building—in the fronto-temporal languageselective network (Fedorenko et al., 2011) using a robust precision fMRI approach.”
MethodSelf-citationTask-Dependent Recruitment of Modality-Specific and Multimodal Regions during Conceptual Processing
Kuhnke, P., Kiefer, M., & Hartwigsen, G. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa010 · Cited by 114
“…approach, using half of the data of each subject (blocks 1–3) for fROI definition and the other half (blocks 4–6) for response estimation (cf. Fedorenko et al. 2011, 2013). fROI definition followed the GSS procedure (Julian et al. 2012): For each fROI type, subject-specific activation maps…”
Choi, W., Desai, R. H., & Henderson, J. M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.01024 · Cited by 112
“…paragraph reading rather than single-word reading, the words have typically been presented one at a time in serial visual presentation (SVP, e.g., Martin-Loeches et al., 2008; Fedorenko et al., 2011; Pallier et al., 2011), with participants often asked to do a secondary task such as probe matching.”
BackgroundDriving and suppressing the human language network using large language models
Tuckute, G., Sathe, A., Srikant, S., Taliaferro, M., Wang, M., Schrimpf, M., Kay, K., & Fedorenko, E. G. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01783-7 · Cited by 106
“26; see Fedorenko and Blank 9 for a review).”
Self-citationProcessing structure in language and music: a case for shared reliance on cognitive control
Slevc, L. R., & Okada, B. M. (2014) · Psychonomic Bulletin & Review · doi:10.3758/s13423-014-0712-4 · Cited by 115
“…et al., 2006) and in music (Koelsch et al., 2009; Schulze et al., 2011; Williamson et al., 2010), and is associated with the inferior frontal regions that are implicated in both domains (Koelsch et al., 2009; Schulze, Zysset, Mueller, Friederici, & Koelsch, 2011; but see Fedorenko et al., 2011).”
BackgroundInfluentialComprehension of computer code relies primarily on domain-general executive brain regions
Ivanova, A. A., Srikant, S., Sueoka, Y., Kean, H., Dhamala, R., O’Reilly, U.-M., Bers, M. U., & Fedorenko, E. G. (2020) · eLife · doi:10.7554/elife.58906 · Cited by 120
“Studies of math and logic, domains that also make extensive use of symbols, show that those domains do not rely on the language system (Amalric and Dehaene, 2019; Cohen et al., 2000; Fedorenko et al., 2011; Monti et al., 2009; Monti et al., 2012; Pinel and Dehaene, 2010; Varley et al., 2005), a conclusion consistent w…”
BackgroundMethodResultInfluentialSelf-citationCo-localizing linguistic and musical syntax with intracranial EEG
Sammler, D., Koelsch, S., Tonio Ball, A., Brandt, A., Grigutsch, M., Huppertz, H., Knösche, T. R., Wellmer, J., Widman, G., Elger, C. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.035 · Cited by 80
“Fiebach and Schubotz, 2006; Gelfand and Bookheimer, 2003; Koelsch, 2005; Patel, 2003; Tettamanti and Weniger, 2006) though to date this is not unequivocally proven (Fedorenko et al., 2011; Rogalsky et al., 2011).”
BackgroundHu, J., Small, H., Kean, H., Takahashi, A., Zekelman, L., Kleinman, D., Ryan, E., Nieto-Castañón, A., Ferreira, V., & Fedorenko, E. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhac350 · Cited by 73
Self-citationBaldo, J. V., Paulraj, S. R., Curran, B. C., & Dronkers, N. F. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01523 · Cited by 82
Can, D. D., Richards, T. L., & Kuhl, P. K. (2012) · Brain and Language · doi:10.1016/j.bandl.2012.10.007 · Cited by 81
“Recent research also suggests that cortical regions sensitive to language can be functionally defined in individual adults (Fedorenko et al., 2010, 2011).”
BackgroundThe dual origin of semantic errors in access deficit: activation vs. inhibition deficit
Nozari, N. (2019) · Cognitive Neuropsychology · doi:10.1080/02643294.2019.1587397 · Cited by 71
Wehbe, L., Blank, I., Shain, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.15.043844 · Cited by 11
Self-citationRomeo, R. R., Flournoy, J. C., McLaughlin, K. A., & Lengua, L. J. (2022) · Developmental Science · doi:10.1111/desc.13227 · Cited by 76
“While these domains appear to be neurobiologically distinct in adults (Fedorenko et al., 2011), it remains an open question whether language and EF rely on distinct neural processes in childhood, or if these become differentiated throughout development.”
BackgroundShain, C., Blank, I., Fedorenko, E., et al. (2021) · Journal of Neuroscience · doi:10.1101/2021.09.18.460917 · Cited by 69
“…comprehension, we examine two candidate brain networks, each functionally localized in individual participants: the language-selective network (Fedorenko et al., 2011) and the domain-general multiple-demand (MD) network implicated in executive functions, including working memory (Duncan et…”
BackgroundSelf-citationModelling language evolution: Examples and predictions
Gong, T., Shuai, L., & Zhang, M. (2013) · Physics of Life Reviews · doi:10.1016/j.plrev.2013.11.009 · Cited by 78
A Tri-network Model of Human Semantic Processing
Xu, Y., Yong He, A., & Bi, Y. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01538 · Cited by 79
“…they together fit well with the language network ( Figure 2B , middle) (Friederici, 2011), which shows selective activation by sentences in contrast to nonword lists and not by multiple non-linguistic tasks, such as, arithmetic, working memory, cognitive control, or music (Fedorenko et al., 2011).”
BackgroundLiu, Y.-F., Kim, J., Wilson, C., & Bedny, M. (2020) · eLife · doi:10.7554/elife.59340 · Cited by 64
“…math and language were compared to each other, the networks observed for each domain are similar to those previously identified with other control conditions (e.g. lists of non-words for language and hard vs. easy contrast in a logic task) (e.g. Coetzee and Monti, 2018 ; Fedorenko et al., 2011 ).”
BackgroundResultShared functional specialization in transformer-based language models and the human brain
Kumar, S., Sumers, T. R., Yamakoshi, T., Goldstein, A., Hasson, U., Norman, K. A., Griffiths, T. L., Hawkins, R. D., & Nastase, S. A. (2024) · Nature Communications · doi:10.1038/s41467-024-49173-5 · Cited by 108
“In the case of MFG, this is consistent with prior work implicating MFG in both language comprehension and more general cognitive demand (e.g. working memory; Fedorenko et al., 2011; Mineroff et al., 2018).”
ResultVoxelwise Encoding Models Show That Cerebellar Language Representations Are Highly Conceptual
LeBel, A., Jain, S., & Huth, A. G. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0118-21.2021 · Cited by 68
“The word-level semantic model predicts cortical voxels across regions in the frontal, parietal, and temporal lobes beyond core language-specific regions(Fedorenko et al., 2011, 2013).”
BackgroundInfluentialThe right inferior frontal gyrus processes nested non-local dependencies in music
Cheung, V. K. M., Meyer, L., Friederici, A. D., & Koelsch, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-22144-9 · Cited by 67
Language in Brains, Minds, and Machines
Tuckute, G., Kanwisher, N., & Fedorenko, E. G. (2024) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-120623-101142 · Cited by 78
“Diverse nonlinguistic inputs and tasks elicit little or no response in the language areas in spite of strongly activating other brain areas (e.g., see Monti et al. 2009, 2012; Fedorenko et al. 2011; Ivanova et al. 2020; Chen et al. 2023).”
InfluentialSelf-citationThe Language Network Is Recruited but Not Required for Nonverbal Event Semantics
Ivanova, A. A., Mineroff, Z., Zimmerer, V., Kanwisher, N., Varley, R., & Fedorenko, E. (2021) · Neurobiology of Language · doi:10.1162/nol_a_00030 · Cited by 65
“…but not to mental arithmetic, music perception, executive function tasks, action/gesture perception, or computer programming (Amalric & Dehaene, 2019; Fedorenko et al., 2011; Ivanova et al., 2020; Jouravlev et al., 2019; Liu et al., 2020; Monti et al., 2009, 2012; Pritchett et al., 2018).”
BackgroundSelf-citationSemantic encoding during language comprehension at single-cell resolution
Jamali, M., Grannan, B. L., Cai, J., Khanna, A. R., Muñoz, W., Caprara, I., Paulk, A. C., Cash, S. S., Fedorenko, E. G., & Williams, Z. M. (2024) · Nature · doi:10.1038/s41586-024-07643-2 · Cited by 76
Self-citationA Connectomic Hypothesis for the Hominization of the Brain
Changeux, J., Goulas, A., & Hilgetag, C. C. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa365 · Cited by 80
“processing, aware of the fact that multiple (more than 10) brain areas may contribute to language processing in adults, including the left frontal lobe, left temporal/parietal lobes, right temporal lobe, cerebellum hippocampus basal ganglia (Fedorenko et al. 2011; Deniz Can et al. 2013), and especially the prefrontal…”
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
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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