Brain network dynamics during spontaneous strategy shifts and incremental task optimization
Open accessPublished version of a preprint
Abstract
With practice, humans improve their performance in a task by either optimizing a known strategy or discovering a novel, potentially more fruitful strategy. We investigated the neural processes underlying these two fundamental abilities by applying fMRI in a task with two possible alternative strategies. For analysis we combined time-resolved network analysis with Coherence Density Peak Clustering (Allegra et al., 2017), univariate GLM, and multivariate pattern classification. Converging evidence showed that the posterior portion of the default network, i.e. the precuneus and the angular gyrus bilaterally, has a central role in the optimization of the current strategy. These regions encoded the relevant spatial information, increased the strength of local connectivity as well as the long-distance connectivity with other relevant regions in the brain (e.g., visual cortex, dorsal attention network). The connectivity increase was proportional to performance optimization. By contrast, the anterior portion of the default network (i.e. medial prefrontal cortex) and the rostral portion of the fronto-parietal network were associated with new strategy discovery: an early increase of local and long-range connectivity centered on these regions was only observed in the subjects who would later shift to a new strategy. Overall, our findings shed light on the dynamic interactions between regions related to attention and with cognitive control, underlying the balance between strategy exploration and exploitation. Results suggest that the default network, far from being "shut-down" during task performance, has a pivotal role in the background exploration and monitoring of potential alternative courses of action.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Differences between individuals in task performance are related to patterns of dynamic brain organization.
“Several recent studies have further shown how individual differences in task performance are related to patterns of dynamic brain organization54,55.”
From Cognitive and behavioural flexibility: Neural mechanisms and clinical considerations (2021) · cites this paper as [54]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182 · see citing works
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304 · see citing works
Clustering by fast search and find of density peaks
Rodríguez, Á., & Laio, A. (2014) · Science · doi:10.1126/science.1242072 · Cited by 4,963 · see citing works
Self-citationAston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472 · see citing works
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 · see citing works
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106 · see citing works
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748 · see citing works
Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962 · see citing works
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747 · see citing works
Circular analysis in systems neuroscience: the dangers of double dipping
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009) · Nature Neuroscience · doi:10.1038/nn.2303 · Cited by 2,696 · see citing works
Situating the default-mode network along a principal gradient of macroscale cortical organization
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608282113 · Cited by 2,321 · see citing works
Beyond mind-reading: multi-voxel pattern analysis of fMRI data
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.07.005 · Cited by 2,271 · see citing works
EXPERT AND EXCEPTIONAL PERFORMANCE: Evidence of Maximal Adaptation to Task Constraints
Ericsson, K. A., & Lehmann, A. C. (1996) · Annual Review of Psychology · doi:10.1146/annurev.psych.47.1.273 · Cited by 2,139 · see citing works
Information-based functional brain mapping
Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600244103 · Cited by 2,113 · see citing works
Dynamic reconfiguration of human brain networks during learning
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705 · see citing works
Multi-task connectivity reveals flexible hubs for adaptive task control
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013) · Nature Neuroscience · doi:10.1038/nn.3470 · Cited by 1,704 · see citing works
Logothetis, N. K., & Wandell, B. A. (2004) · Annual Review of Physiology · doi:10.1146/annurev.physiol.66.082602.092845 · Cited by 1,568 · see citing works
Sporns, O., & Betzel, R. F. (2016) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033634 · Cited by 1,334 · see citing works
The proactive brain: using analogies and associations to generate predictions
Bar, M. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.05.005 · Cited by 1,144 · see citing works
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2098 · Cited by 1,121 · see citing works
Stimulus onset quenches neural variability: A widespread cortical phenomenon
Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S., Newsome, W. T., Clark, A. M., Hosseini, P., Scott, B. B., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2501 · Cited by 1,084 · see citing works
Organizing conceptual knowledge in humans with a gridlike code
Constantinescu, A. O., O’Reilly, J. X., & Behrens, T. E. J. (2016) · Science · doi:10.1126/science.aaf0941 · Cited by 937 · see citing works
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.018 · Cited by 886 · see citing works
Orbitofrontal Cortex as a Cognitive Map of Task Space
Wilson, R. C., Takahashi, Y. K., Schoenbaum, G., & Niv, Y. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.005 · Cited by 885 · see citing works
Robust prediction of individual creative ability from brain functional connectivity
Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., Fink, A., Qiu, J., Kwapil, T. R., Kane, M. J., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1713532115 · Cited by 835 · see citing works
The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition
Cohen, J. R., & D’Esposito, M. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2965-15.2016 · Cited by 767 · see citing works
The power law repealed: The case for an exponential law of practice
Heathcote, A., Brown, S. D., & Mewhort, D. J. K. (2000) · Psychonomic Bulletin & Review · doi:10.3758/bf03212979 · Cited by 735 · see citing works
Differential attention-dependent response modulation across cell classes in macaque visual area V4
Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2007) · Neuron · doi:10.1016/j.neuron.2007.06.018 · Cited by 665 · see citing works
Sakoğlu, Ü., Pearlson, G. D., Kiehl, K. A., Wang, Y. M., Michael, A. M., & Calhoun, V. D. (2010) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-010-0197-8 · Cited by 620 · see citing works
Frontal Cortex and the Hierarchical Control of Behavior
Badre, D., & Nee, D. E. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.11.005 · Cited by 611 · see citing works
Learning-induced autonomy of sensorimotor systems
Bassett, D. S., Yang, M., Wymbs, N. F., & Grafton, S. T. (2015) · Nature Neuroscience · doi:10.1038/nn.3993 · Cited by 603 · see citing works
Human orbitofrontal cortex represents a cognitive map of state space
Schuck, N. W., Cai, M. B., Wilson, R. C., & Niv, Y. (2016) · Neuron · doi:10.1016/j.neuron.2016.08.019 · Cited by 602 · see citing works
Self-citationNeuronal basis of sequential foraging decisions in a patchy environment
Hayden, B. Y., Pearson, J. M., & Platt, M. L. (2011) · Nature Neuroscience · doi:10.1038/nn.2856 · Cited by 513 · see citing works
Chein, J. M., & Schneider, W. (2005) · Cognitive Brain Research · doi:10.1016/j.cogbrainres.2005.08.013 · Cited by 446 · see citing works
Cognitive control over learning: Creating, clustering, and generalizing task-set structure.
Collins, A. G. E., & Frank, M. J. (2013) · Psychological Review · doi:10.1037/a0030852 · Cited by 421 · see citing works
Foundations of human reasoning in the prefrontal cortex
Donoso, M., Collins, A. G. E., & Koechlin, É. (2014) · Science · doi:10.1126/science.1252254 · Cited by 407 · see citing works
Sun, F. T., Miller, L. M., & D’Esposito, M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.09.056 · Cited by 406 · see citing works
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives
Haynes, J.-D. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.025 · Cited by 402 · see citing works
The effects of neural gain on attention and learning
Eldar, E., Cohen, J. D., & Niv, Y. (2013) · Nature Neuroscience · doi:10.1038/nn.3428 · Cited by 402 · see citing works
Frontal cortex and the discovery of abstract action rules
Badre, D., Kayser, A. S., & D'Esposito, M. (2010) · Neuron · doi:10.1016/j.neuron.2010.03.025 · Cited by 337 · see citing works
Default Mode Dynamics for Global Functional Integration
Vatansever, D., Menon, D. K., Manktelow, A. E., Sahakian, B. J., & Stamatakis, E. A. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2135-15.2015 · Cited by 333 · see citing works
Jiang, L., & Zuo, X. (2015) · The Neuroscientist · doi:10.1177/1073858415595004 · Cited by 331 · see citing works
Karahanoğlu, F. I., & Ville, D. V. D. (2015) · Nature Communications · doi:10.1038/ncomms8751 · Cited by 320 · see citing works
The fallacy of a “task‐negative” network
Spreng, R. N. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00145 · Cited by 320 · see citing works
Default mode contributions to automated information processing
Vatansever, D., Menon, D. K., & Stamatakis, E. A. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710521114 · Cited by 281 · see citing works
Deco, G., & Kringelbach, M. L. (2016) · Trends in Neurosciences · doi:10.1016/j.tins.2016.01.001 · Cited by 235 · see citing works
Principles of dynamic network reconfiguration across diverse brain states
Shine, J. M., & Poldrack, R. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.010 · Cited by 235 · see citing works
Executive control and decision-making in the prefrontal cortex
Domenech, P., & Koechlin, É. (2014) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2014.10.007 · Cited by 233 · see citing works
Dynamic reorganization of human resting-state networks during visuospatial attention
Spadone, S., Penna, S. D., Sestieri, C., Betti, V., Tosoni, A., Perrucci, M. G., Romani, G. L., & Corbetta, M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1415439112 · Cited by 182 · see citing works
Top-down attention switches coupling between low-level and high-level areas of human visual cortex
Al-Aidroos, N., Said, C. P., & Turk-Browne, N. B. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1202095109 · Cited by 180 · see citing works
Recruitment of the default mode network during a demanding act of executive control
Crittenden, B. M., Mitchell, D. J., & Duncan, J. (2015) · eLife · doi:10.7554/elife.06481 · Cited by 176 · see citing works
A Strong Interactive Link between Sensory Discriminations and Intelligence
Melnick, M. D., Harrison, B. R., Park, S., Bennetto, L., & Tadin, D. (2013) · Current Biology · doi:10.1016/j.cub.2013.04.053 · Cited by 159 · see citing works
Cortical activity is more stable when sensory stimuli are consciously perceived
Schurger, A., Sarigiannidis, I., Naccache, L., Sitt, J. D., & Dehaene, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418730112 · Cited by 155 · see citing works
Default mode network connectivity during task execution
Vatansever, D., Menon, D. K., Manktelow, A. E., Sahakian, B. J., & Stamatakis, E. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.053 · Cited by 141 · see citing works
Medial prefrontal cortex predicts internally driven strategy shifts
Schuck, N. W., Gaschler, R., Wenke, D., Heinzle, J., Frensch, P., Haynes, J., & Reverberi, C. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.015 · Cited by 119 · see citing works
Self-citationNormalization and the Cholinergic Microcircuit: A Unified Basis for Attention
Schmitz, T. W., & Duncan, J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.02.011 · Cited by 116 · see citing works
Bassett, D. S., & Mattar, M. G. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.01.010 · Cited by 102 · see citing works
Functional Brain Changes Following Cognitive and Motor Skills Training
Patel, R. R., Spreng, R. N., & Turner, G. R. (2012) · Neurorehabilitation and neural repair · doi:10.1177/1545968312461718 · Cited by 95 · see citing works
Hussar, C. R., & Pasternak, T. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1009956107 · Cited by 94 · see citing works
Neural Variability Quenching Predicts Individual Perceptual Abilities
Arazi, A., Censor, N., & Dinstein, I. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1671-16.2016 · Cited by 92 · see citing works
Cole, M. W., Braver, T. S., & Meiran, N. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.02.009 · Cited by 63 · see citing works
Variability of Prefrontal Neuronal Discharges before and after Training in a Working Memory Task
Qi, X.-L., & Constantinidis, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041053 · Cited by 54 · see citing works
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 · see citing works
Reverberi, C., Kuhlen, A. K., Seyed‐Allaei, S., Greulich, R. S., Costa, A., Abutalebi, J., & Haynes, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.025 · Cited by 37 · see citing works
Self-citationWenke, D., Houwer, J. D., Winne, J. D., & Liefooghe, B. (2014) · Psychological Research · doi:10.1007/s00426-014-0621-1 · Cited by 19 · see citing works
Quenching of spontaneous fluctuations by attention in human visual cortex
Broday-Dvir, R., Grossman, S., Furman‐Haran, E., & Malach, R. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.089 · Cited by 18 · see citing works
What is automatized during perceptual categorization?
Roeder, J. L., & Ashby, F. G. (2016) · Cognition · doi:10.1016/j.cognition.2016.04.005 · Cited by 14 · see citing works
Incidental covariation learning leading to strategy change
Gaschler, R., Schuck, N. W., Reverberi, C., Frensch, P. A., & Wenke, D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210597 · Cited by 14 · see citing works
Self-citationLearning the meaning of new stimuli increases the cross-correlated activity of prefrontal neurons
Nougaret, S., & Genovesio, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-29862-0 · Cited by 12 · see citing works
Dark Control: Towards a Unified Account of Default Mode Function by Markov Decision ProcessesPreprint
Dohmatob, E., Dumas, G., & Bzdok, D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/148890 · Cited by 11 · see citing works
fMRI single trial discovery of spatio‐temporal brain activity patterns
Allegra, M., Seyed‐Allaei, S., Pizzagalli, F., Baftizadeh, F., Maieron, M., Reverberi, C., Laio, A., & Amati, D. (2016) · Human Brain Mapping · doi:10.1002/hbm.23463 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Schuck, N.W., Wenke, D., Ay, D.S., Loewe, A., Gaschler, R., Shing, Y.L., 2019. Early Development of Self-Guided Strategy Improvements in Children. PsyArXiv Available at: https://osf.io/bcpwj. (Accessed 14 February 2019).
Cited by
Sorted by citations
Cognitive and behavioural flexibility: Neural mechanisms and clinical considerations
Uddin, L. Q. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00428-w · Cited by 698 · see citing works
Recent progress in artificial synaptic devices: materials, processing and applications
Chen, F., Zhou, Y., Zhu, Y., Zhu, R., Guan, P., Fan, J., Zhou, L., Nagarajan, V., von Wegner, F., Saribatir, E., et al. (2021) · Journal of Materials Chemistry C · doi:10.1039/d1tc01211h · Cited by 88 · see citing works
From exploration to exploitation: a shifting mental mode in late life development
Spreng, R. N., & Turner, G. R. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.09.001 · Cited by 61 · see citing works
Rhoads, S. A., Cutler, J., & Marsh, A. A. (2021) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsab079 · Cited by 51 · see citing works
Improving Inclusive Education through Universal Design for Learning
Galkienė, A., & Monkevičienė, O. (2021) · Inclusive learning and educational equity · doi:10.1007/978-3-030-80658-3 · Cited by 54 · see citing works
Wang, Y., Royer, J., Park, B., de Wael, R. V., Larivière, S., Tavakol, S., Rodríguez‐Cruces, R., Paquola, C., Hong, S., Margulies, D. S., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac172 · Cited by 51 · see citing works
Wyatt, L. E., Hewan, P., Hogeveen, J., Spreng, R. N., & Turner, G. R. (2023) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2023.108740 · Cited by 33 · see citing works
Sorella, S., Vellani, V., Šiugždaitė, R., Feraco, P., & Grecucci, A. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15537 · Cited by 27 · see citing works
The Goal of the Universal Design for Learning: Development of All to Expert Learners
Navaitienė, J., & Stasiūnaitienė, E. (2021) · Inclusive learning and educational equity · doi:10.1007/978-3-030-80658-3_2 · Cited by 35 · see citing works
Grecucci, A., Sorella, S., & Consolini, J. (2022) · Behavioural Brain Research · doi:10.1016/j.bbr.2022.114245 · Cited by 20 · see citing works
Abrupt and spontaneous strategy switches emerge in simple regularised neural networks
Löwe, A. T., Touzo, L., Muhle-Karbe, P. S., Saxe, A., Summerfield, C., & Schuck, N. W. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012505 · Cited by 15 · see citing works
Self-citationChildren’s Spontaneous Additive Strategy Relates to Multiplicative Reasoning
Tzur, R., Johnson, H. L., Norton, A., Davis, A., Wang, X., Ferrara, M., Harrington, C., & Hodkowski, N. M. (2021) · Cognition and Instruction · doi:10.1080/07370008.2021.1896521 · Cited by 10 · see citing works
Spontaneous discovery of novel task solutions in children
Schuck, N. W., Li, A. X., Wenke, D., Ay-Bryson, D. S., Löwe, A. T., Gaschler, R., & Shing, Y. L. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266253 · Cited by 10 · see citing works
Self-citationWang, Y., Royer, J., Park, B., de Wael, R. V., Larivière, S., Tavakol, S., Rodríguez‐Cruces, R., Paquola, C., Hong, S., Margulies, D. S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.25.465692 · Cited by 10 · see citing works
Neuronal correlates of eyeblinks are an expression of primary consciousness phenomena
Callara, A. L., Greco, A., Scilingo, E. P., & Bonfiglio, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-39500-z · Cited by 9 · see citing works
N2 sleep promotes the occurrence of ‘aha’ moments in a perceptual insight task
Löwe, A. T., Petzka, M., Tzegka, M. M., & Schuck, N. W. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003185 · Cited by 11 · see citing works
Self-citationN2 Sleep Inspires InsightPreprint
Löwe, A. T., Petzka, M., Tzegka, M. M., & Schuck, N. W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.24.600359 · Cited by 5 · see citing works
Self-citationStrategic complexity and cognitive skills affect brain response in interactive decision-making
Reverberi, C., Pischedda, D., Mantovani, M., Haynes, J., & Rustichini, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-17951-0 · Cited by 4 · see citing works
Self-citationLin, Y.-T., & Leber, A. B. (2024) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0001398 · Cited by 4 · see citing works
Grecucci, A., Sorella, S., & Consolini, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4118485 · Cited by 3 · see citing works
Yin, Z., Wang, Y., Dong, M., Ren, S., Hu, H., Yin, K., & Liang, J. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.652920 · Cited by 2 · see citing works
Data segmentation based on the local intrinsic dimension
Allegra, M., Facco, E., Denti, F., Laio, A., & Mira, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-72222-0 · Cited by 1 · see citing works
Self-citationZuo, W., Fang, S., Xu, T., Li, Y., Zhao, J., Xie, X., Wang, T., Hou, W., & Wang, M. (2025) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70271 · Cited by 1 · see citing works
Flexible decision-strategy improvements in childrenPreprint
Schuck, N. W., Li, A. X., Wenke, D., Ay-Bryson, D. S., Löwe, A. T., Gaschler, R., & Shing, Y. L. (2019) · doi:10.31234/osf.io/bcpwj · Cited by 1 · see citing works
Self-citationLo sviluppo urbanistico e l’assetto della città. Le scelte urbanistiche
Comoli, V. (1998) · PORTO@Iris
Neuroscience Methods for Investigating Brain Plasticity
Verra, L., Renz, F. M., & Schuck, N. W. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197677131.013.24
Self-citation
- Authors
- Allegra, M., Seyed-Allaei, S., Schuck, N. W., Amati, D., Laio, A., & Reverberi, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Max Planck Institute for Human Development (DE)
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- The Abdus Salam International Centre for Theoretical Physics (ICTP) (IT)
- University of Milano-Bicocca (IT)
- Corresponding author
- Allegra, Michele
- Reverberi, Carlo
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 217
- Pages
- 116854
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W3018038647
- Cited by
- 27
- References
- 73
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 2 · 2022: 5 · 2021: 10 · 2020: 2 · 2019: 1
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- SDL (submittedVersion, cc-by)
- HAL AMU (submittedVersion, other-oa)
- BOA (University of Milano-Bicocca) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Horizon 2020
- European Commission (ANR-17-HBPR-0001-02)
- Ministero dell’Istruzione, dell’Università e della Ricerca (PRIN 2010RP5RNM_001)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- strategy optimization, strategy discovery, strategy switching, default mode network, precuneus, angular gyrus, medial prefrontal cortex, frontoparietal network, dorsal attention network, brain network dynamics, exploration-exploitation balance, cognitive control, attention, multivariate pattern classification
- MeSH terms
- Adult, Algorithms, Attention, Brain, Brain Mapping, Cognition, Decision Making, Exploratory Behavior, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Neural Pathways, Parietal Lobe, Psychomotor Performance, Prefrontal Cortex, Young Adult, Neuroimaging
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.
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