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9 - Predictive Intelligence for Learning and Optimization

Multidisciplinary Perspectives from Social, Cognitive, and Affective Neuroscience

from Part II - Theories, Models, and Hypotheses

Published online by Cambridge University Press:  11 June 2021

Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
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Summary

Many different definitions of intelligence exist but, in the end, they all converge on the brain. In this chapter, we explore the implications of the simple idea that, ultimately, intelligence must help optimize the survival of the individual and of the species. Central to this evolutionary argument, intelligence must offer superior abilities to learn and flexibly adapt to new challenges in the environment. To enhance the possibility of survival, the brain must thus learn to make accurate predictions that optimize the amount of time and energy spent on choosing appropriate actions in a given situation. Such predictive models have a number of parameters, like speed, complexity, and flexibility, that ensure the correct balance and usefulness to solve a given problem (Deary, Penke, & Johnson, 2010; Friedman et al., 2008; Fuster, 2005; Houde, 2010; Johnson-Laird, 2001; Kringelbach & Rolls, 2004; Roth & Dicke, 2005). These parameters come from a variety of cognitive, affective, and social factors, but a main requirement is one of motivation to initiate and sustain the learning process. Finally, one thing is to survive, another is to flourish, and so we discuss whether the intelligent brain is also optimal in terms of wellbeing given that spending too much time predicting something that may never come to pass could be counterproductive to flourishing. Thus, in this perspective, intelligence can be thought of as the process of balancing and optimizing the parameters that allow animals to survive as individuals and as a species, while still maintaining the motivation to do so. Improving the predictive, intelligent brain is a lifelong process where there are important shifts throughout the lifespan in how different aspects and parameters are prioritized.

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Publisher: Cambridge University Press
Print publication year: 2021

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References

Anokhin, A. P., Muller, V., Lindenberger, U., Heath, A. C., & Myers, E. (2006). Genetic influences on dynamic complexity of brain oscillations. Neuroscience Letters, 397(1–2), 9398.Google Scholar
Anticevic, A., Cole, M. W., Murray, J. D., Corlett, P. R., Wang, X. J., & Krystal, J. H. (2012). The role of default network deactivation in cognition and disease. Trends in Cognitive Science, 16, 584592.Google Scholar
Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press.Google Scholar
Baars, B. J., & Franklin, S. (2007). An architectural model of conscious and unconscious brain functions: Global Workspace Theory and IDA. Neural Networks, 20(9), 955961.Google Scholar
Baars, B. J., Franklin, S., & Ramsoy, T. Z. (2013). Global workspace dynamics: Cortical “binding and propagation” enables conscious contents. Frontiers in Psychology, 4, 200.Google Scholar
Bar, M., Kassam, K. S., Ghuman, A. S., Boshyan, J., Schmid, A. M., Dale, A. M., … Halgren, E. (2006). Top-down facilitation of visual recognition. Proceedings of the National Academy of Sciences USA, 103(2), 449454.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Science, 22(1), 820.Google Scholar
Beaty, R. E., Benedek, M., Kaufman, S. B., & Silvia, P. J. (2015). Default and executive network coupling supports creative idea production. Science Reports, 5, 10964.Google Scholar
Berridge, K. C., & Kringelbach, M. L. (2008). Affective neuroscience of pleasure: Reward in humans and animals. Psychopharmacology, 199(3), 457480.Google Scholar
Berridge, K. C., & Robinson, T. E. (2003). Parsing reward. Trends in Neurosciences, 26(9), 507513.Google Scholar
Cabral, J., Hugues, E., Sporns, O., & Deco, G. (2011). Role of local network oscillations in resting-state functional connectivity. Neuroimage, 57(1), 130139.Google Scholar
Cabral, J., Luckhoo, H., Woolrich, M., Joensson, M., Mohseni, H., Baker, A., … Deco, G. (2014). Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations. Neuroimage, 90, 423435.Google Scholar
Cabral, J., Vidaurre, D., Marques, P., Magalhaes, R., Silva Moreira, P., Miguel Soares, J., … Kringelbach, M. L. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Science Reports, 7, 5135.Google Scholar
Carhart-Harris, R. L. (2018). The entropic brain – Revisited. Neuropharmacology, 142, 167178.CrossRefGoogle ScholarPubMed
Carhart-Harris, R. L., Leech, R., Hellyer, P., Shanahan, M., Feilding, A., Tagliazucchi, E., … Nutt, D. (2014). The entropic brain: A theory of conscious states informed by neuroimaging research with psychedelic drugs. Frontiers in Human Neuroscience, 8, 20.Google Scholar
Chanes, L., & Barrett, L. F. (2016). Redefining the role of limbic areas in cortical processing. Trends in Cognitive Sciences, 20(2), 96106.Google Scholar
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181204.Google Scholar
Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen, P. C. (2004). Working memory is (almost) perfectly predicted by g. Intelligence, 32(3), 277296.Google Scholar
Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences, 7(12), 547552.Google Scholar
Dayan, P., & Balleine, B. W. (2002). Reward, motivation, and reinforcement learning. Neuron, 36(2), 285298.Google Scholar
De Vincenzo, I., Giannoccaro, I., Carbone, G., & Grigolini, P. (2017). Criticality triggers the emergence of collective intelligence in groups. Physical Review E, 96(2–1), 022309.CrossRefGoogle ScholarPubMed
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201211.Google Scholar
Deco, G., & Jirsa, V. K. (2012). Ongoing cortical activity at rest: Criticality, multistability, and ghost attractors. Journal of Neuroscience, 32(10), 33663375.Google Scholar
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews Neuroscience, 12(1), 4356.Google Scholar
Deco, G., Jirsa, V. K., McIntosh, A. R., Sporns, O., & Kotter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences USA, 106(25), 1030210307.Google Scholar
Deco, G., & Kringelbach, M. L. (2014). Great expectations: Using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron, 84(5), 892905.Google Scholar
Deco, G., & Kringelbach, M. L. (2016). Metastability and coherence: Extending the communication through coherence hypothesis using a whole-brain computational perspective. Trends in Neuroscience, 39(3), 125135.Google Scholar
Deco, G., Kringelbach, M. L., Jirsa, V. K., & Ritter, P. (2017). The dynamics of resting fluctuations in the brain: Metastability and its dynamical cortical core. Science Reports, 7(1), 3095.Google Scholar
Deco, G., Ponce-Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014). How local excitation-inhibition ratio impacts the whole brain dynamics. Journal of Neuroscience, 34(23), 78867898.Google Scholar
Dehaene, S., & Changeux, J.-P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200227.Google Scholar
Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: A testable taxonomy. Trends in Cognitive Sciences, 10(5), 204211.Google Scholar
Dehaene, S., Kerszberg, M., & Changeux, J.-P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. Proceedings of the National Academy of Sciences, 95(24), 14529.Google Scholar
Dehaene, S., & Naccache, L. (2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition, 79(1), 137.Google Scholar
Dennis, M., Francis, D. J., Cirino, P. T., Schachar, R., Barnes, M. A., & Fletcher, J. M. (2009). Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. Journal of the International Neuropsychological Society, 15(3), 331343.CrossRefGoogle Scholar
Dimitriadis, S. I., Laskaris, N. A., Simos, P. G., Micheloyannis, S., Fletcher, J. M., Rezaie, R., & Papanicolaou, A. C. (2013). Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG. Neuroimage, 83, 307317.Google Scholar
Duncan, J. (2013). The structure of cognition: Attentional episodes in mind and brain. Neuron, 80(1), 3550.Google Scholar
Ferguson, M. A., Anderson, J. S., & Spreng, R. N. (2017). Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture. Network Neuroscience, 1(2), 192207.CrossRefGoogle ScholarPubMed
Fingelkurts, A. A., Fingelkurts, A. A., Rytsala, H., Suominen, K., Isometsa, E., & Kahkonen, S. (2007). Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Human Brain Mapping, 28(3), 247261.Google Scholar
Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008). Individual differences in executive functions are almost entirely genetic in origin. Journal of Experimental Psychology: General, 137(2), 201225.Google Scholar
Fries, P. (2005). A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence. Trends in Cognitive Science, 9(10), 474480.Google Scholar
Friston, K. (1997). Transients, metastability, and neuronal dynamics. Neuroimage, 5(2), 164171.Google Scholar
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127138.Google Scholar
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364(1521), 12111221.Google Scholar
Fuster, J. M. (2005). Cortex and mind: Unifying cognition. Oxford University Press.Google Scholar
Gardner, H. (1984). Frames of mind: The theory of multiple intelligences. London: Heinemann.Google Scholar
Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009). The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology, 120(3), 453463.Google Scholar
Ghosh, A., Rho, Y., McIntosh, A. R., Kotter, R., & Jirsa, V. K. (2008). Noise during rest enables the exploration of the brain’s dynamic repertoire. PLoS Computational Biology, 4(10), e1000196.Google Scholar
Glascher, J. P., & O’Doherty, J. P. (2010). Model-based approaches to neuroimaging: Combining reinforcement learning theory with fMRI data. Wiley Interdisciplinary Reviews: Cognitive Science, 1(4), 501510.Google Scholar
Grigolini, P., Piccinini, N., Svenkeson, A., Pramukkul, P., Lambert, D., & West, B. J. (2015). From neural and social cooperation to the global emergence of cognition. Frontiers in Bioengineering and Biotechnology, 3, 78.Google Scholar
Hansenne, M., & Bianchi, J. (2009). Emotional intelligence and personality in major depression: Trait versus state effects. Psychiatry Research, 166(1), 6368.Google Scholar
Heggli, O. A., Cabral, J., Konvalinka, I., Vuust, P., & Kringelbach, M. L. (2019). A Kuramoto model of self-other integration across interpersonal synchronization strategies. PLoS Computational Biology, 15(10), e1007422. doi: 10.1371/journal.pcbi.1007422.Google Scholar
Honey, C. J., Kotter, R., Breakspear, M., & Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences USA, 104(24), 1024010245.Google Scholar
Houde, O. (2010). Beyond IQ comparisons: Intra-individual training differences. Nature Reviews Neuroscience, 11(5), 370.Google Scholar
Hu, G., Huang, X., Jiang, T., & Yu, S. (2019). Multi-scale expressions of one optimal state regulated by dopamine in the prefrontal cortex. Frontiers in Physiology, 10, 113.Google Scholar
Huron, D. (2006). Sweet anticipation: Music and the psychology of expectation. Cambridge, MA: MIT Press.Google Scholar
Huron, D. (2016). Voice leading: The science behind a musical art. Cambridge, MA: MIT Press.Google Scholar
Johnson-Laird, P. N. (2001). Mental models and deduction. Trends in Cognitive Science, 5(10), 434442.Google Scholar
Jung, R. E. & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135154; discussion 154–187.Google Scholar
Kanai, R., Komura, Y., Shipp, S., & Friston, K. (2015). Cerebral hierarchies: Predictive processing, precision and the pulvinar. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 370(1668), 20140169.Google Scholar
Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9(4), 637671.Google Scholar
Kitzbichler, M. G., Henson, R. N. A., Smith, M. L., Nathan, P. J., & Bullmore, E. T. (2011). Cognitive effort drives workspace configuration of human brain functional networks. The Journal of Neuroscience, 31(22), 8259.Google Scholar
Koelsch, S., Vuust, P., & Friston, K. (2019). Predictive processes and the peculiar case of music. Trends in Cognitive Sciences, 23(1), 6377.Google Scholar
Koenen, K. C., Moffitt, T. E., Roberts, A. L., Martin, L. T., Kubzansky, L., Harrington, H., … Caspi, A. (2009). Childhood IQ and adult mental disorders: A test of the cognitive reserve hypothesis. American Journal of Psychiatry, 166(1), 5057.Google Scholar
Konvalinka, I., Vuust, P., Roepstorff, A., & Frith, C. (2009). A coupled oscillator model of interactive tapping. Proceedings of the 7th Triennial Conference of European Society for the Cognitive Sciences of Music (ESCOM 2009), University of Jyväskylä, Jyväskylä, Finland, pp. 242–245.Google Scholar
Kringelbach, M. L., & Berridge, K. C. (2017). The affective core of emotion: Linking pleasure, subjective well-being, and optimal metastability in the brain. Emotion Review, 9(3), 191199.Google Scholar
Kringelbach, M. L., McIntosh, A. R., Ritter, P., Jirsa, V. K., & Deco, G. (2015). The rediscovery of slowness: Exploring the timing of cognition. Trends in Cognitive Science, 19(10), 616628.Google Scholar
Kringelbach, M. L., & Rapuano, K. M. (2016). Time in the orbitofrontal cortex. Brain, 139(4), 10101013.Google Scholar
Kringelbach, M. L., & Rolls, E. T. (2004). The functional neuroanatomy of the human orbitofrontal cortex: Evidence from neuroimaging and neuropsychology. Progress in Neurobiology, 72(5), 341372.Google Scholar
Kuramoto, Y. (1975) Self-entrainment of a population of coupled non-linear oscillators. In Araki, H. (ed.), International symposium on mathematical problems in theoretical physics. Lecture Notes in Physics, vol 39. (pp. 420422). Berlin, Heidelberg: Springer. doi: 10.1007/BFb0013365.Google Scholar
Landro, N. I., Stiles, T. C., & Sletvold, H. (2001). Neuropsychological function in nonpsychotic unipolar major depression. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 14(4), 233240.Google Scholar
Lee, W. H., Doucet, G. E., Leibu, E., & Frangou, S. (2018). Resting-state network connectivity and metastability predict clinical symptoms in schizophrenia. Schizophrenia Research, 201, 208216.Google Scholar
Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(Pt. 1), 1232.Google Scholar
Li, Y., Vanni-Mercier, G., Isnard, J., Mauguière, F., & Dreher, J.-C. (2016). The neural dynamics of reward value and risk coding in the human orbitofrontal cortex. Brain, 139(4), 12951309. doi: 10.1093/brain/awv409.Google Scholar
Liang, S., Brown, M. R. G., Deng, W., Wang, Q., Ma, X., Li, M., … Li, T. (2018). Convergence and divergence of neurocognitive patterns in schizophrenia and depression. Schizophrenia Research, 192, 327334.Google Scholar
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., … Smallwood, J. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences USA, 113(44), 1257412579.Google Scholar
Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15(10), 483506.Google Scholar
Mesulam, M. M. (1998). From sensation to cognition. Brain: A Journal of Neurology, 121(6), 10131052.Google Scholar
Näätänen, R., Gaillard, A. W. K., & Mäntysalo, S. (1978). Early selective-attention effect on evoked potential reinterpreted. Acta Psychologica, 42(4), 313329.Google Scholar
Näätänen, R., Paavilainen, P., Rinne, T., & Alho, K. (2007). The mismatch negativity (MMN) in basic research of central auditory processing: a review. Clinical Neurophysiology, 118(12), 25442590.CrossRefGoogle ScholarPubMed
Niv, Y., & Schoenbaum, G. (2008). Dialogues on prediction errors. Trends in Cognitive Science, 12(7), 265272.Google Scholar
O’Doherty, J. P. (2004). Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14(6), 769776.Google Scholar
Pamplona, G. S., Santos Neto, G. S., Rosset, S. R., Rogers, B. P., & Salmon, C. E. (2015). Analyzing the association between functional connectivity of the brain and intellectual performance. Frontiers in Human Neuroscience, 9, 61.Google Scholar
Parkinson, C., & Wheatley, T. (2015). The repurposed social brain. Trends in Cognitive Science, 19(3), 133141.Google Scholar
Pearce, M. T., & Wiggins, G. A. (2012). Auditory expectation: The information dynamics of music perception and cognition. Topics in Cognitive Science, 4(4), 625652.Google Scholar
Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 7987.Google Scholar
Ravnkilde, B., Videbech, P., Clemmensen, K., Egander, A., Rasmussen, N. A., & Rosenberg, R. (2002). Cognitive deficits in major depression. Scandinavian Journal of Psychology, 43(3), 239251.Google Scholar
Reinhart, R. M. G., & Nguyen, J. A. (2019). Working memory revived in older adults by synchronizing rhythmic brain circuits. Nature Neuroscience, 22(5), 820827.Google Scholar
Rohrmeier, M. A., & Koelsch, S. (2012). Predictive information processing in music cognition. A critical review. International Journal of Psychophysiology: Official Journal of the International Organization of Psychophysiology, 83(2), 164175.Google Scholar
Rolls, E. T. (2010). Attractor networks. Wiley Interdisciplinary Reviews: Cognitive Science, 1(1), 119134.Google Scholar
Rolls, E. T., Loh, M., Deco, G., & Winterer, G. (2008). Computational models of schizophrenia and dopamine modulation in the prefrontal cortex. Nature Reviews Neuroscience, 9(9), 696.Google Scholar
Roth, G., Dicke, U. (2005). Evolution of the brain and intelligence. Trends in Cognitive Science, 9(5), 250257.Google Scholar
Sackeim, H. A., Freeman, J., McElhiney, M., Coleman, E., Prudic, J., & Devanand, D. P. (1992). Effects of major depression on estimates of intelligence. Journal of Clinical and Experimental Neuropsychology, 14(2), 268288.Google Scholar
Sanger, J., Muller, V., & Lindenberger, U. (2012). Intra- and interbrain synchronization and network properties when playing guitar in duets. Frontiers in Human Neuroscience, 6, 312.Google Scholar
Santarnecchi, E., Galli, G., Polizzotto, N. R., Rossi, A., & Rossi, S. (2014). Efficiency of weak brain connections support general cognitive functioning. Human Brain Mapping, 35(9), 45664582.Google Scholar
Schacter, D. L., Addis, D. R., & Buckner, R. L. (2007). Remembering the past to imagine the future: The prospective brain. Nature Reviews Neuroscience, 8(9), 657661.Google Scholar
Schultz, W. (2015). Neuronal reward and decision signals: From theories to data. Physiological Review, 95(3), 853951.CrossRefGoogle ScholarPubMed
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 15931599.Google Scholar
Schultz, W., & Dickinson, A. (2000). Neuronal coding of prediction errors. Annual Review of Neuroscience, 23(1), 473500.Google Scholar
Shen, X., Cox, S. R., Adams, M. J., Howard, D. M., Lawrie, S. M., Ritchie, S. J., … Whalley, H. C. (2018). Resting-state connectivity and its association with cognitive performance, educational attainment, and household income in the UK biobank. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(10), 878886.Google Scholar
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., … Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484.Google Scholar
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., … Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354359.Google Scholar
Singer, W. (2001). Consciousness and the binding problem. Annals of the New York Academy of Sciences, 929, 123146.Google Scholar
Song, M., Zhou, Y., Li, J., Liu, Y., Tian, L., Yu, C., & Jiang, T. (2008). Brain spontaneous functional connectivity and intelligence. Neuroimage, 41(3), 11681176.Google Scholar
Stam, C. J., & van Straaten, E. C. (2012). The organization of physiological brain networks. Clinical Neurophysiology, 123(6), 10671087.Google Scholar
Steinbeck, J., & Ricketts, E. F. (1941). The log from the sea of Cortez. London: Penguin.Google Scholar
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 20152028.Google Scholar
Stone, A. A., Schwartz, J. E., Broderick, J. E., & Deaton, A. (2010). A snapshot of the age distribution of psychological well-being in the United States. Proceedings of the National Academy of Sciences of the United States of America, 107(22), 99859990.Google Scholar
Thorndike, E. L. (1920). Intelligence and its uses. Harper’s Magazine, 140, 227235.Google Scholar
Tognoli, E., & Kelso, J. A. (2014). The metastable brain. Neuron, 81(1), 3548.Google Scholar
Turalska, M., Geneston, E., West, B. J., Allegrini, P., & Grigolini, P. (2012). Cooperation-induced topological complexity: A promising road to fault tolerance and Hebbian learning. Frontiers in Physiology, 3, 52.Google Scholar
Turalska, M., West, B. J., & Grigolini, P. (2013). Role of committed minorities in times of crisis. Science Reports, 3, 1371.Google Scholar
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519534.Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 76197624.Google Scholar
Veiel, H. O. (1997). A preliminary profile of neuropsychological deficits associated with major depression. Journal of Clinical and Experimental Neuropsychology, 19(4), 587603.Google Scholar
Voytek, B., & Knight, R. T. (2015). Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biological Psychiatry, 77(12), 10891097.Google Scholar
Vuust, P., & Frith, C. D. (2008). Anticipation is the key to understanding music and the effects of music on emotion. Behavioral and Brain Sciences, 31(5), 599600.Google Scholar
Vuust, P., & Kringelbach, M. L. (2010). The pleasure of making sense of music. Interdisciplinary Science Reviews, 35(2), 166182.Google Scholar
Werner, G. (2007). Metastability, criticality and phase transitions in brain and its models. Biosystems, 90(2), 496508.Google Scholar
Zakzanis, K. K., Leach, L., & Kaplan, E. (1998). On the nature and pattern of neurocognitive function in major depressive disorder. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 11(3), 111119.Google Scholar
Zylberberg, A., Dehaene, S., Roelfsema, P. R., & Sigman, M. (2011). The human Turing machine: A neural framework for mental programs. Trends in Cognitive Sciences, 15(7), 293300.Google Scholar

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