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Broad domain generality in focal regions of frontal and parietal cortex
Open accessAbstract
Significance: One of the oldest debates in cognitive neuroscience concerns the degree of functional specialization present in the human brain. Prior work has discovered several highly specialized components dedicated to particular mental functions, like face recognition or motion perception. However, our cognitive versatility suggests the additional existence of more general-purpose machinery. Building on prior neuroimaging evidence, along with neurophysiological evidence from non-human primates, we searched for such domain-general brain regions in humans. Seven diverse demanding cognitive tasks produced overlapping activation at the individual-subject level in a number of frontal and parietal brain regions. Thus, human cognition arises from hardware that includes not only specialized components, but also very general-purpose ones that plausibly enable us to solve novel problems.
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“The FPN and CON are functionally separable brain networks that enable flexible adaptive control and sustained task-set maintenance [142]; together, they form the MD network [143], a set of brain regions that is active during a range of tasks that require goal-directed behavior [139].”
From The role of prefrontal cortex in cognitive control and executive function (2022) · cites this paper as [139]
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“Conjunction maps of activation during different tasks (e.g., during inhibiting, updating, and shifting tasks), either with data estimated within one study [135, 138, 139] or with meta-analytic techniques [137, 140, 141], suggest that both children and adults recruit a common set of regions within the FPN and CON during diverse EF tasks.”
From The role of prefrontal cortex in cognitive control and executive function (2022) · cites this paper as [139]
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“Our findings show that brain regions that are engaged in a verbal WM task, which are associated with the operation of the MD network (Fedorenko et al., 2013;Duncan, 2010), are also engaged during spatial and imitative conflict resolution.”
From Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation (2018) · cites this paper as Fedorenko et al., 2013
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“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., 2013
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“In addition to the connectional anatomy, it has been noted that these same domain-independent regions are activated during a variety of different cognitive and motor control tasks [70, 71].”
From Neurocomputational mechanisms underlying subjective valuation of effort costs (2017) · cites this paper as [71]
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Cites
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
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
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Conflict monitoring and cognitive control
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.624 · Cited by 7,351
Functional Network Organization of the Human Brain
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.006 · Cited by 4,124
Distributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940
The reorienting system of the human brain: from environment to theory of mind
Corbetta, M., Patel, G., & Shulman, G. L. (2008) · Neuron · doi:10.1016/j.neuron.2008.04.017 · Cited by 3,889
Conflict monitoring and anterior cingulate cortex: an update
Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.10.003 · Cited by 3,623
Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies
Cabeza, R., & Nyberg, L. (2000) · Journal of Cognitive Neuroscience · doi:10.1162/08989290051137585 · Cited by 3,598
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Meaningful inhibition: Exploring the role of meaning and modality in response inhibition
Alam, T. R. G., Murphy, C., Smallwood, J., & Jefferies, E. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.074 · Cited by 55
Assem, M., Shashidhara, S., Glasser, M. F., & Duncan, J. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhad537 · Cited by 49
Self-citationShared neural processes support semantic control and action understanding
Davey, J., Rueschemeyer, S., Costigan, A. G., Murphy, N., Krieger‐Redwood, K., Hallam, G., & Jefferies, E. (2015) · Brain and Language · doi:10.1016/j.bandl.2015.01.002 · Cited by 48
Evidence for a Functional Hierarchy of Association Networks
Choi, E. Y., Drayna, G. K., & Badre, D. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01229 · Cited by 43
Chen, P.-Y., Chen, C., Hsu, Y., & Tseng, W. I. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116576 · Cited by 46
Hierarchical Representation of Multistep Tasks in Multiple-Demand and Default Mode Networks
Wen, T., Duncan, J., & Mitchell, D. J. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0594-20.2020 · Cited by 48
Self-citationPrecise Topology of Adjacent Domain-General and Sensory-Biased Regions in the Human Brain
Assem, M., Shashidhara, S., Glasser, M. F., & Duncan, J. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab362 · Cited by 47
Self-citationGraves, W. W., Purcell, J. J., Rothlein, D., Bolger, D. J., Rosenberg‐Lee, M., & Staples, R. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02590-y · Cited by 49
Catena, A., Muñoz-Machicao, J. A., Torres-Espínola, F. J., Martínez-Zaldívar, C., Diaz-Piedra, C., Gil, A., Haile, G., Györei, E., Molloy, A. M., Decsi, T., et al. (2016) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.109108 · Cited by 46
Network mechanisms of intentional learning
Hampshire, A., Hellyer, P. J., Parkin, B., Hiebert, N. M., MacDonald, P. A., Owen, A. M., Leech, R., & Rowe, J. B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.060 · Cited by 44
Kellermann, T. S., Bonilha, L., Eskandari, R., Garcia‐Ramos, C., Lin, J. J., & Hermann, B. P. (2016) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2016.07.030 · Cited by 45
High-level language processing regions are not engaged in action observation or imitation
Pritchett, B., Hoeflin, C., Koldewyn, K., Dechter, E., & Fedorenko, E. G. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00222.2018 · Cited by 46
Self-citationMandonnet, E. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.08.071 · Cited by 45
Madole, J. W., Ritchie, S. J., Cox, S. R., Buchanan, C. R., Hernández, M. V., Maniega, S. M., Wardlaw, J. M., Harris, M. A., Bastin, M. E., Deary, I. J., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.010 · Cited by 45
Fedorenko, E. G., & Shain, C. (2021) · Current Directions in Psychological Science · doi:10.1177/09637214211046955 · Cited by 48
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Murphy, E., Forseth, K. J., Donos, C., Snyder, K. M., Rollo, P. S., & Tandon, N. (2023) · Nature Communications · doi:10.1038/s41467-023-42087-8 · Cited by 44
Deen, B., Saxe, R. R., & Bedny, M. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00807 · Cited by 49
Jiang, X., Liu, T., Li, X., Lv, J., Zhao, S., Zhang, S., Zhang, W., Zhang, T., Han, J., & Guo, L. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2598728 · Cited by 44
Processes of believing: Where do they come from? What are they good for?
Seitz, R. J., Paloutzian, R. F., & Angel, H. (2016) · F1000Research · doi:10.12688/f1000research.9773.2 · Cited by 44
Neural substrates of sublexical processing for spelling
DeMarco, A. T., Wilson, S. M., Rising, K., Rapcsak, S. Z., & Beeson, P. M. (2016) · Brain and Language · doi:10.1016/j.bandl.2016.10.001 · Cited by 45
Designing and Interpreting Psychophysical Investigations of Cognition
Waskom, M., Okazawa, G., & Kiani, R. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.016 · Cited by 49
Josephs, E., & Konkle, T. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912333117 · Cited by 45
Cerebral Activations Related to Writing and Drawing with Each Hand
Potgieser, A. R. E., van der Hoorn, A., & de Jong, B. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126723 · Cited by 49
Dynamic Network Mechanisms of Relational Integration
Parkin, B., Hellyer, P. J., Leech, R., & Hampshire, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4956-14.2015 · Cited by 44
Isolating response inhibition in the brain: Parietal versus frontal contribution
Kolodny, T., Mevorach, C., & Shalev, L. (2016) · Cortex · doi:10.1016/j.cortex.2016.12.012 · Cited by 47
Kievit, R., Fuhrmann, D., Borgeest, G. S., Simpson-Kent, I. L., & Henson, R. N. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14241.2 · Cited by 42
Common cortical architectures for phonological working memory identified in individual brains
Scott, T. L., & Perrachione, T. K. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116096 · Cited by 40
Shashidhara, S., Spronkers, F. S., & Erez, Y. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01554 · Cited by 44
The interplay of top-down focal attention and the cortical tracking of speech
Lesenfants, D., & Francart, T. (2020) · Scientific Reports · doi:10.1038/s41598-020-63587-3 · Cited by 45
Zhang, M., Varga, D., Wang, X., Krieger‐Redwood, K., Gouws, A. D., Smallwood, J., & Jefferies, E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117405 · Cited by 46
Involvement of the default mode network under varying levels of cognitive effort
Weber, S., Aleman, A., & Hugdahl, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-10289-7 · Cited by 48
In need of constraint: Understanding the role of the cingulate cortex in the impulsive mind
Golchert, J., Smallwood, J., Jefferies, E., Liem, F., Huntenburg, J. M., Falkiewicz, M., Lauckner, M. E., Oligschläger, S., Villringer, A., & Margulies, D. S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.041 · Cited by 45
Meta-analytic evidence for a core problem solving network across multiple representational domains
Bartley, J. E., Boeving, E. R., Riedel, M. C., Bottenhorn, K. L., Salo, T., Eickhoff, S. B., Brewe, E. T., Sutherland, M. T., & Laird, A. R. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.06.009 · Cited by 49
Processing communicative facial and vocal cues in the superior temporal sulcus
Deen, B., Saxe, R. R., & Kanwisher, N. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117191 · Cited by 42
Self-citationYan, J., Chen, Y., Xiao, Z., Zhang, S., Jiang, M., Wang, T., Zhang, T., Lv, J., Becker, B., Zhang, R., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102518 · Cited by 43
The Microstructure of Attentional Control in the Dorsal Attention Network
Rajan, A., Meyyappan, S., Liu, Y., Samuel, I. B. H., Nandi, B., Mangun, G. R., & Ding, M. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01710 · Cited by 44
Vafaii, H., Mandino, F., Desrosiers-Grégoire, G., O’Connor, D. A., Markicevic, M., Shen, X., Ge, X., Hermán, P., Hyder, F., Papademetris, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44363-z · Cited by 45
Demeter, D. V., Engelhardt, L. E., Mallett, R., Gordon, E. M., Nugiel, T., Harden, K. P., Tucker–Drob, E. M., Lewis‐Peacock, J. A., & Church, J. A. (2019) · iScience · doi:10.1016/j.isci.2019.100801 · Cited by 42
Johnson, J. P., Meier, E. L., Pan, Y., & Kiran, S. (2020) · Brain and Language · doi:10.1016/j.bandl.2020.104809 · Cited by 41
Ramsey, R., & Ward, R. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620904972 · Cited by 40
Castaldi, E., Vignaud, A., & Eger, E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117210 · Cited by 43
A Synergistic Workspace for Human Consciousness Revealed by Integrated Information DecompositionPreprint
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Carhart-Harris, R. L., Williams, G. B., Craig, M. M., Finoia, P., Owen, A. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.25.398081 · Cited by 43
Brain mechanisms underlying the inhibitory control of thought
Anderson, M. C., Crespo‐García, M., & Subbulakshmi, S. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00929-y · Cited by 52
Meier, E. L., Kapse, K. J., & Kiran, S. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00109 · Cited by 42
Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder
Zhao, Y., Dong, Q., Chen, H., Iraji, A., Li, Y., Makkie, M., Kou, Z., & Liu, T. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.08.005 · Cited by 40
State-dependent modulation of functional connectivity in early blind individuals
Pelland, M., Orban, P., Dansereau, C. L., Leporé, F., Bellec, P., & Collignon, O. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.053 · Cited by 42
Evans, M., Krieger‐Redwood, K., Alam, T. R. G., Smallwood, J., & Jefferies, E. (2020) · Cortex · doi:10.1016/j.cortex.2020.04.032 · Cited by 42
Distinct Neural Correlates of Linguistic and Non-Linguistic Demand
Quillen, I. A., Yen, M., & Wilson, S. M. (2021) · Neurobiology of Language · doi:10.1162/nol_a_00031 · Cited by 39
Hauptman, M., Blank, I., & Fedorenko, E. (2023) · Cortex · doi:10.1016/j.cortex.2023.01.013 · Cited by 44
Self-citationSpeech-accompanying gestures are not processed by the language-processing mechanisms
Jouravlev, O., Zheng, D., Balewski, Z., Le Arnz Pongos, A., Levan, Z., Goldin-Meadow, S., & Fedorenko, E. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107132 · Cited by 39
Self-citationGoghari, V. M., Sanford, N., Spilka, M. J., & Woodward, T. S. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx004 · Cited by 38
The impact of spelling regularity on handwriting production: A coupled fMRI and kinematics study
Palmis, S., Velay, J.-L., Fabiani, E., Nazarian, B., Anton, J.-L., Habib, M., Kandel, S., & Longcamp, M. (2019) · Cortex · doi:10.1016/j.cortex.2018.11.024 · Cited by 40
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Noyce, A. L., Lefco, R. W., Brissenden, J. A., Tobyne, S. M., Shinn‐Cunningham, B. G., & Somers, D. C. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab249 · Cited by 40
Saadon‐Grosman, N., Du, J., Kosakowski, H. L., Angeli, P. A., DiNicola, L. M., Eldaief, M. C., & Buckner, R. L. (2024) · Science Advances · doi:10.1126/sciadv.adq4037 · Cited by 41
Tobyne, S. M., Osher, D. E., Michalka, S., & Somers, D. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.020 · Cited by 42
Chen, X. J., Affourtit, J., Ryskin, R. A., Regev, T. I., Norman-Haignere, S., Jouravlev, O., Malik-Moraleda, S., Kean, H., Varley, R. A., & Fedorenko, E. G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.01.446439 · Cited by 39
Self-citationSensory experience modulates the reorganization of auditory regions for executive processing
Manini, B., Vinogradova, V., Woll, B., Cameron, D., Eimer, M., & Cardin, V. (2022) · Brain · doi:10.1093/brain/awac205 · Cited by 43
Paunov, A. M., Blank, I., Jouravlev, O., Mineroff, Z., Gallée, J., & Fedorenko, E. G. (2022) · Neurobiology of Language · doi:10.1162/nol_a_00071 · Cited by 39
Self-citationAnderson, E. D., & Barbey, A. K. (2022) · Human Brain Mapping · doi:10.1002/hbm.26164 · Cited by 42
Central attention is serial, but midlevel and peripheral attention are parallel—A hypothesis
Tamber-Rosenau, B. J., & Marois, R. (2016) · Attention Perception & Psychophysics · doi:10.3758/s13414-016-1171-y · Cited by 44
Neural basis of the crossmodal correspondence between auditory pitch and visuospatial elevation
McCormick, K., Lacey, S., Stilla, R. F., Nygaard, L. C., & Sathian, K. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.02.029 · Cited by 38
Identifying Brain Networks at Multiple Time Scales via Deep Recurrent Neural Network
Cui, Y., Zhao, S., Wang, H., Xie, L. Y., Chen, Y., Han, J., Guo, L., Zhou, F., & Liu, T. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2882885 · Cited by 39
Matchin, W., & Wood, E. A. (2020) · Cerebral Cortex Communications · doi:10.1093/texcom/tgaa029 · Cited by 37
Overarching Principles and Dimensions of the Functional Organization in the Inferior Parietal Cortex
Humphreys, G. F., Jackson, R. L., & Ralph, M. A. L. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa133 · Cited by 36
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Calzavarini, F. (2021) · The British Journal for the Philosophy of Science · doi:10.1086/717564 · Cited by 39
Westphal, A. J., Reggente, N., Ito, K. L., & Rissman, J. (2015) · Human Brain Mapping · doi:10.1002/hbm.23074 · Cited by 36
The language of music: Common neural codes for structured sequences in music and natural language
Chiang, J. N., Rosenberg, M., Bufford, C. A., Stephens, D., Lysy, A., & Monti, M. M. (2018) · Brain and Language · doi:10.1016/j.bandl.2018.07.003 · Cited by 40
Johnson, J. P., Meier, E. L., Pan, Y., & Kiran, S. (2019) · Cortex · doi:10.1016/j.cortex.2019.08.016 · Cited by 35
A Putative Multiple-Demand System in the Macaque Brain.
Mitchell, D. J., Bell, A. H., Buckley, M. J., Mitchell, A. S., Sallet, J., & Duncan, J. (2016) · UCL Discovery (University College London) · doi:10.17863/cam.24734 · Cited by 38
Self-citationHumphreys, G. F., & Tibon, R. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02510-0 · Cited by 40
Prefrontal cortex supports speech perception in listeners with cochlear implants
Sherafati, A., Dwyer, N. Y., Bajracharya, A., Hassanpour, M. S., Eggebrecht, A. T., Firszt, J. B., Culver, J. P., & Peelle, J. E. (2022) · eLife · doi:10.7554/elife.75323 · Cited by 42
A cognitive neurogenetic approach to uncovering the structure of executive functions
Feng, J., Zhang, L., Chen, C., Sheng, J., Ye, Z., Feng, K., Liu, J., Cai, Y., Zhu, B., Yu, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32383-0 · Cited by 37
Hodgson, V. J., Ralph, M. A. L., & Jackson, R. L. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac394 · Cited by 32
Roles of the Default Mode and Multiple-Demand Networks in Naturalistic versus Symbolic Decisions
Smith, V., Duncan, J., & Mitchell, D. J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1888-20.2020 · Cited by 36
Self-citationDiveica, V., Riedel, M. C., Salo, T., Laird, A. R., Jackson, R. L., & Binney, R. J. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad373 · Cited by 38
Liu, W., Peeters, N., Fernández, G., & Kohn, N. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa073 · Cited by 35
Decreasing Alertness Modulates Perceptual Decision-Making
Jagannathan, S. R., Bareham, C. A., & Bekinschtein, T. A. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0182-21.2021 · Cited by 36
Ulanov, M., & Юрьевич, Ш. Ю. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.940845 · Cited by 36
The Contribution of Human Superior Intraparietal Sulcus to Visual Short-Term Memory and Perception
Yaoda Xu, A., & Jeong, S. K. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801371-7.00004-1 · Cited by 33
Brain activity during arithmetic in symbolic and non-symbolic formats in 9–12 year old children
Peters, L., Polspoel, B., de Beeck, H. O., & Smedt, B. D. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.04.001 · Cited by 41
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Zhang, Z., Chen, Y., Yu, Q., Li, J., Zou, L., Mavilidi, M. F., Green, C. S., Owen, N. G., Hallgren, M. Å., Raichlen, D. A., et al. (2025) · Trends in Neurosciences · doi:10.1016/j.tins.2025.09.002 · Cited by 35
Jackson, J. B., & Woolgar, A. (2018) · Cortex · doi:10.1016/j.cortex.2018.07.006 · Cited by 35
Meier, E. L., Johnson, J. P., & Kiran, S. (2018) · Cortex · doi:10.1016/j.cortex.2018.08.006 · Cited by 33
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Fuhrmann, D., Simpson-Kent, I. L., Bathelt, J., The CALM Team, Kievit, R., Holmes, J., Gathercole, S. E., Astle, D. E., Manly, T., & Kievit, R. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz091 · Cited by 37
Hugdahl, K., Bereśniewicz, J., Kazimierczak, K., Kompus, K., Westerhausen, R., Ersland, L., Grüner, R., & Specht, K. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218358 · Cited by 31
Frontoparietal action-oriented codes support novel instruction implementation
González‐García, C., Formica, S., Wisniewski, D., & Braß, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117608 · Cited by 32
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“Two sets of fROIs were defined (Figure 3): MD network fROIs that have been known to exhibit activity for a variety of cognitive control tasks (Fedorenko et al., 2013; Duncan, 2010) and ToM network fROIs that support mentalizing and have been specifically implicated for imitation inhibition (Brass et al.”
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Metadata
- Authors
- Fedorenko, E., Duncan, J., & Kanwisher, N.
- Affiliations
- McGovern Institute for Brain Research (US)
- MRC Cognition and Brain Sciences Unit (GB)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-09-23
- Volume
- 110
- Issue
- 41
- Pages
- 16616-16621
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3799302
- OpenAlex
- W2001896589
- Cited by
- 952
- References
- 99
- Citations by year
- 2026: 79 · 2025: 101 · 2024: 93 · 2023: 63 · 2022: 82 · 2021: 94 · 2020: 90 · 2019: 85 · 2018: 62 · 2017: 75 · 2016: 61 · 2015: 42 · 2014: 23 · 2013: 2
- FWCI
- 23.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Science Foundation (057522, 0904625)
- Ellison Medical Foundation
- Massachusetts Institute of Technology
- National Institutes of Health (5k99hd057522-02)
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Medical Research Council
- National Eye Institute (R01EY013455)
- 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 › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- domain generality, domain specificity, frontal cortex, parietal cortex, cognitive flexibility, arithmetic, working memory, cognitive inhibition, single-subject analysis, functional topography
- MeSH terms
- Adult, Brain Mapping, Cognition, Female, Frontal Lobe, Humans, Magnetic Resonance Imaging, Male, Massachusetts, Middle Aged, Neuropsychological Tests, Parietal Lobe
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