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Reviews in the Neurosciences

Editor-in-Chief: Huston, Joseph P.

Editorial Board: Topic, Bianca / Adeli, Hojjat / Buzsaki, Gyorgy / Crawley, Jacqueline / Crow, Tim / Gold, Paul / Holsboer, Florian / Korth, Carsten / Li, Jay-Shake / Lubec, Gert / McEwen, Bruce / Pan, Weihong / Pletnikov, Mikhail / Robbins, Trevor / Schnitzler, Alfons / Stevens, Charles / Steward, Oswald / Trojanowski, John

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Volume 28, Issue 4


How neuroscience can inform the study of individual differences in cognitive abilities

Dennis J. McFarland
  • Corresponding author
  • National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, P.O. Box 509, Albany, NY 12201-0509, USA
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-02-14 | DOI: https://doi.org/10.1515/revneuro-2016-0073


Theories of human mental abilities should be consistent with what is known in neuroscience. Currently, tests of human mental abilities are modeled by cognitive constructs such as attention, working memory, and speed of information processing. These constructs are in turn related to a single general ability. However, brains are very complex systems and whether most of the variability between the operations of different brains can be ascribed to a single factor is questionable. Research in neuroscience suggests that psychological processes such as perception, attention, decision, and executive control are emergent properties of interacting distributed networks. The modules that make up these networks use similar computational processes that involve multiple forms of neural plasticity, each having different time constants. Accordingly, these networks might best be characterized in terms of the information they process rather than in terms of abstract psychological processes such as working memory and executive control.

Keywords: attention; intelligence; mental abilities; networks; speed of information processing; working memory


  • Abrahamse, E., Braem, S., Notebaert, W., and Verguts, T. (2016). Grounding cognitive control in associative learning. Psychol. Bull. 142, 693–728.CrossrefGoogle Scholar

  • Ackerman, P.L., Beier, M.E., and Boyle, M.O. (2005). Working memory and intelligence: The same or different constructs? Psychol. Bull. 131, 30–60.CrossrefGoogle Scholar

  • Alavash, M., Thiel, C.M., and Giessing, C. (2016). Dynamic coupling of complex brain networks and dual-task behavior. Neuroimage 129, 233–246.CrossrefGoogle Scholar

  • Alexander, G.E., DeLong, M.R., and Strick, P.L. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357–381.CrossrefGoogle Scholar

  • Alexander-Bloch, A., Giedd, J.N., and Bullmore, E. (2013). Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322–336.CrossrefGoogle Scholar

  • Althen, H., Grimm, S., and Escera, C. (2013). Simple and complex acoustic regularities are encoded at different levels of the auditory hierarchy. Eur. J. Neurosci. 38, 3448–3455.CrossrefGoogle Scholar

  • Andersen, R.A., Essick, G.K., and Siegel, R.M. (1985). Encoding of spatial location by posterior parietal neurons. Science 230, 456–458.CrossrefGoogle Scholar

  • Andrews, T.J., Halpern, S.D., and Purves, D. (1997). Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. J. Neurosci. 17, 2859–2868.Google Scholar

  • Arvanitakis, Z., Fleischman, D.A., Afranakis, K., Leurgans, S.E., Barnes, L.L., and Bennett, D.A. (2016). Association of white matter hyperintensities and gray matter volume with cognition in older individuals without cognitive impairment. Brain Struct. Funct. 221, 2135–2146.CrossrefGoogle Scholar

  • Atiani, S., David, S.V., Eigueda, D., Locaqstro, M., Radtke-Schuller, S., Shamma, S.A., and Fritz, J.B. (2014). Emergent selectivity for task-relevant stimuli in higher-order auditory cortex. Neuron 82, 486–499.CrossrefGoogle Scholar

  • Awh, E., Vogel, E.K., and Oh, S.H. (2006). Interactions between attention and working memory. Neuroscience 139, 201–208.CrossrefGoogle Scholar

  • Bacci, A., Huguenard, J.R., and Prince, D.A. (2005). Modulation of neocortical interneurons: extrinsic influences and exercises in self-control. Trends Neurosci. 28, 602–610.CrossrefGoogle Scholar

  • Baddeley, A. (2012). Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29.CrossrefGoogle Scholar

  • Bar, K-J., de la Cruz, F., Schumann, A., Koehler, S., Sauer, H., Critchley, H., and Wagner, G. (2016). Functional connectivity and network analysis of midbrain and brainstem nuclei. Neuroimage 134, 53–63.CrossrefGoogle Scholar

  • Barbas, H. (2015). General cortical and special prefrontal connections: Principals from structure to function. Annu. Rev. Neurosci. 18, 269–289.CrossrefGoogle Scholar

  • Bartholomew, D.J., Deary, I.J., and Lawn, M. (2009). A new lease of life for Thompson’s bonds model of intelligence. Psychol. Rev. 116, 567–579.CrossrefGoogle Scholar

  • Basten, U., Hilger, K., and Fiebach, C.J. (2015). Where smart brains are different: A quantative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence 51, 10–27.CrossrefGoogle Scholar

  • Bastos, A.M., Usrey, W.M., Adams, R.A., Mangum, G.R., Fries, P., and Friston, K.J. (2012). Canonical microcircuits for predictive coding. Neuron 76, 695–711.CrossrefGoogle Scholar

  • Bays, P.M. (2015). Spikes not slots: noise in neural populations limits working memory. Trends Cogn. Sci. 19, 431–438.CrossrefGoogle Scholar

  • Beaty, R.E., Benedek, M., Silvia, P.J., and Schacter, D.L. (2016). Creative cognition and brain network dynamics. Trends Cogn. Sci. 20, 87–95.CrossrefGoogle Scholar

  • Beier, M.E. and Ackerman, P.L. (2005). Working memory and intelligence: Different constructs. Reply to Oberauer et al. (2005) and Kane et al. (2005). Psychol. Bull. 131, 72–75.CrossrefGoogle Scholar

  • Benson, N., Hulac, D.M., and Kranzler, J.H. (2010). Independent examination of the Wechsler adult scale – fourth edition (WAIS-IV): What does the WAIS-IV measure? Psychol. Assess. 24, 328–340.Google Scholar

  • Bergmann, J., Genc, E., Kohler, A., Singer, W., and Pearson, J. (2016). Neural anatomy of primary visual cortex limits visual working memory. Cereb. Cortex 26, 43–50.CrossrefGoogle Scholar

  • Berry, A.S., Demeter, E., Sabhapathy, S., English, B.A., Blakely, R.D., Sarter, M., and Lustig, C. (2014). Disposed to distraction: Genetic variation in the cholinergic system influences distractibility but not time-on-task effects. J. Cogn. Neurosci. 26, 1981–1991.Google Scholar

  • Berry, A.S., Blakely, R.D., Sarter, M., and Lustig, C. (2015). Cholinergic capacity mediates prefrontal engagement during challenges to attention: Evidence from imaging genetics. Neuroimage 108, 386–395.CrossrefGoogle Scholar

  • Bisley, J.W. (2011). The neural basis of visual attention. J. Physiol. 589, 49–57.Google Scholar

  • Bourke, P., Brown, S., Ngan, E., and Liotti, A. (2013). Functional brain organization of preparatory attentional control in visual search. Brain Res. 1530, 32–43.CrossrefGoogle Scholar

  • Braun, U., Schafer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J.I., Grimm, O., Heinz, A., Tost, H., et al. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Natl. Acad. Sci. USA 112, 11678–11683.CrossrefGoogle Scholar

  • Braver, T.S., Cohen, J.D., Nystrom, L.E., Jonides, J., Smith, E.E., and Noll, D.C. (1997). A parametric study of prefrontal cortex involvement in human working memory. Neuroimage 5, 49–62.CrossrefGoogle Scholar

  • Brayanov, J.B., Press, D.Z., and Smith, M.A. (2012). Motor memory is encoded as a gain-field combination of intrinsic and extrinsic action representations. J. Neurosci. 32, 14951–14965.CrossrefGoogle Scholar

  • Buetti, S., Cronin, D.A., Madison, A.M., Wang, Z., and Lleras, A. (2016). Towards a better understanding of parallel visual processing in human vision: Evidence from exhaustive analysis of visual information. J. Exp. Psychol. Gen. 145, 672–707.CrossrefGoogle Scholar

  • Bullmore, E. and Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198.CrossrefGoogle Scholar

  • Burns, N.R., Nettelbeck, T., and McPherson, J. (2009). Attention and intelligence: A factor analytic study. J. Ind. Diff. 30, 44–57.CrossrefGoogle Scholar

  • Bussey, T.J. and Saksida, L.M. (2007). Memory, perception, and the ventral visual-perirhinal-hippocampal stream: thinking outside the boxes. Hippocampus 17, 898–908.CrossrefGoogle Scholar

  • Butz, M., Worgotter, F., and van Ooyen, A. (2009). Activity-dependent structural plasticity. Brain Res. Rev. 60, 287–305.CrossrefGoogle Scholar

  • Cacace, A.T. and McFarland, D.J. (2013). Factors influencing tests of auditory processing: A perspective on current issue and relevant concerns. J. Am. Acad. Audiol. 24, 1–18.Google Scholar

  • Cacace, A.T., McFarland, D.J., Emrich, J.F., and Haller, J.S. (1992). Assessing short-term recognition memory with forced choice psychophysical methods. J. Neurosci. Methods 44, 145–155.CrossrefGoogle Scholar

  • Caligiore, D., Peaaulo, G., Baldassarre, G., Bostan, A.C., Strick, P.L., Doya, K., Helmich, R.C., Dirkx, M., Houk, H.J., Jorntell, H., et al. (2017). Consensus paper: towards a systems-level view of cerebellar function: The interplay between cerebellum, basal ganglia, and cortex. Cerebellum 16, 203–229.CrossrefGoogle Scholar

  • Canivez, G.L. (2013). Psychometric versus Actuarial Interpretation of Intelligence and Related Aptitude Batteries. Oxford Handbook of Child Psychological Assessment. D.H. Saklofske, C.R. Reynolds, and V.L. Schwean, eds. (Oxford, UK: Oxford University Press), pp. 84–112.Google Scholar

  • Canivez, G.L. and Watkins, M.W. (2010). Investigation of the factor structure of the Wechsler adult intelligence scale – fourth edition (WAS-IV): Exploratory and higher order factor analysis. Psychol. Assess. 22, 827–836.CrossrefGoogle Scholar

  • Carroll, J.B. (1991). No demonstration that g is not unitary, but there’s more to the story: comment on Kranzler and Jensen. Intelligence 15, 423–436.CrossrefGoogle Scholar

  • Castro-Alamancos, M.A. and Gulati, T. (2014). Neuromodulators produce distinct activated states in neocortex. J. Neurosci. 34, 12533–12367.Google Scholar

  • Ceci, S.J. and Williams, W.M. (1997). Schooling, intelligence, and income. Am. Psychol. 52, 1051–1058.CrossrefGoogle Scholar

  • Chabris, C.F., Lee, J.J., Benjamin, D.J., Beauchamp, J.P., Glasser, E.L., Borst, G., Pinker, S., and Lainson, D.I. (2013). Why is it hard to find genes associated with social science traits: theoretical and empirical considerations. Am. J. Public Health 103, S152–S166.Google Scholar

  • Chaudhuri, R. and Fiete, I. (2016). Computational principals of memory. Nat. Neurosci. 19, 394–403.CrossrefGoogle Scholar

  • Chen, N., Sugihara, H., and Sur, M. (2015). An acetylcholine-activated microcircuit drives temporal dynamics of cortical activity. Nat. Neurosci. 18, 892–902.CrossrefGoogle Scholar

  • Chiappe, D. and MacDonald, K. (2005). The evolution of domain-general mechanisms in intelligence and learning. J. Gen. Psychol. 132, 5–40.CrossrefGoogle Scholar

  • Churchland, A.K. and Abbot, L.F. (2016). Conceptual and technical advances define a key moment for theoretical neuroscience. Nat. Neurosci. 19, 348–349.CrossrefGoogle Scholar

  • Cieslik, E.C., Zilles, K., Caspers, S., Roski, C., Kellermann, T.S., Jakobs, O., Langner, R., Laird, A.R., Fox, P.T., and Eickhoff, S.B. (2013). Is there ‘one’ DLPFC in cognitive action control? Evidence for heterogeneity from co-activation-based parcellation. Cereb. Cortex 23, 2677–2689.CrossrefGoogle Scholar

  • Citri, A. and Malenka, R.C. (2008). Synaptic plasticity: Multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18–41.CrossrefGoogle Scholar

  • Clemenson, G.D. and Stark, C.E.I. (2015). Virtual environmental enrichment through video games improves hippocampal-associated memory. J. Neurosci. 35, 16116–16125.CrossrefGoogle Scholar

  • Cocchi, L., Zalesky, A., Fornito, A., and Mattingley, J.B. (2013). Dynamic cooperation and competition between brain systems during cognitive control. Trends Cogn. Sci. 17, 493–501.CrossrefGoogle Scholar

  • Cohen, J. (1952). A factor-analytically based rationale for the Wechsler-Bellevue. J. Consult. Clin. Psychol. 16, 272–277.CrossrefGoogle Scholar

  • Colibazzi, T., Zhu, H., Bansal, R., Schultz, R.T., Wang, Z., and Peterson, S. (2008). Latent volumetric structure of the human brain: exploratory factor analysis and structural equation modeling of gray matter volumes in healthy children and adults. Hum. Brain Mapp. 29, 1302–1312.CrossrefGoogle Scholar

  • Colom, R. (2014a). All we need is brain (and technology). J. Intell. 2, 26–28.CrossrefGoogle Scholar

  • Colom, R. (2014b). From the earth to the brain. Pers. Individ. Dif. 6162, 3–6.Google Scholar

  • Comings, D.E., Wu, S., Rostamkhani, M., McGue, M., Lacono, W.G., Cheng, L.S-C., and MacMurray, J.P. (2003). Role of the cholinergic muscarinic 2 receptor (CHRM2) gene in cognition. Mol. Psychiatry 8, 10–13.CrossrefGoogle Scholar

  • Danthiir, V., Wilhelm, O., Schilze, R., and Robsert, R.D. (2005). Factor structure and validity of paper-and-pencil measures of mental speed: evidence for a higher-order model? Intelligence 33, 491–514.CrossrefGoogle Scholar

  • Danthiir, V., Wilhelm, O., and Roberts, R.D. (2012). Further evidence for a multifaceted model of mental speed: factor structure and validity of computerized measures. Learn. Individ. Differ. 22, 324–335.CrossrefGoogle Scholar

  • David, S.V., Hayden, B.Y., Mazer, J.A., and Gallant, J.L. (2008). Attention to stimulus features shifts spectral tuning of V4 neurons during natural vision. Neuron 59, 509–521.CrossrefGoogle Scholar

  • Deary, I.J., Johnson, W., and Starr, J.M. (2010). Are processing speed tasks biomarkers of cognitive aging? Psychol. Aging 25, 219–228.CrossrefGoogle Scholar

  • De Fockert, J.W., Rees, G., Frith, C.D., and Lavie, N. (2001). The role of working memory in visual selective attention. Science 291, 1803–1806.CrossrefGoogle Scholar

  • Destexhe, A. and Marder, E. (2004). Plasticity in single neuron and circuit computations. Nature 431, 789–795.CrossrefGoogle Scholar

  • Detterman, D.K., Petersen, E., and Frey, M.C. (2016). Process overlap and system theory: a simulation of, comment on, and integration of Kovacs and Conway. Psychol. Inq. 27, 200–204.CrossrefGoogle Scholar

  • Dick, D.M., Aliev, F., Kramer, J., Wang, J.C., Hinrichs, A., Bertelsen, S., Kuperman, S., Schuckit, M., Nurnberger, J., Edenberg, H.J., et al. (2007). Associations of CHRM2 with IQ: converging evidence for a gene influencing intelligence. Behav. Genet. 37, 265–272.CrossrefGoogle Scholar

  • Dougherty, R.F., Koch, V.M., Brewer, A.A., Fisher, B., Modersitzki, J., and Wandell, B.A. (2003). Visual field representations and locations of visual areas V1/2/3 in human visual cortex. J. Vis. 3, 586–598.CrossrefGoogle Scholar

  • Douglas, R.J. and Martin, K.A.C. (2004). Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27, 419–451.CrossrefGoogle Scholar

  • Downing, P.E. (2000). Interactions between visual working memory and selective attention. Psychol. Sci. 11, 467–473.CrossrefGoogle Scholar

  • D’Souza, D.V., Auer, T., Strasburger, H., Frahm, J., and Lee, B.B. (2011). Temporal frequency and chromatic processing in humans: an fMRI study of the cortical visual areas. J. Vis. 11, 1–17.Google Scholar

  • Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for intelligent behavior. Trends Cogn. Sci. 14, 172–179.CrossrefGoogle Scholar

  • Duncan, J., Schramm, M., Thompson, R., and Dumontheil, I. (2012). Task rules, working memory, and fluid intelligence. Psychon. Bull. Rev. 19, 864–870.CrossrefGoogle Scholar

  • Fecteau, J.H. and Munoz, D.P. (2006). Salience, relevance, and firing: a priority map for target selection. Trends Cogn. Sci. 10, 382–390.CrossrefGoogle Scholar

  • Fedorenko, E., Duncan, J., and Kanwisher, N. (2013). Broad domain generality in focal regions of frontal and parietal cortex. Proc. Natl. Acad. Sci. USA 110, 16616–16621.CrossrefGoogle Scholar

  • Field, G.D. and Chichilnisky, E.J. (2007). Information processing in the primate retina: circuitry and coding. Annu. Rev. Neurosci. 30, 1–30.CrossrefGoogle Scholar

  • Fink, M., Ulbrich, P., Churan, J., and Wittmann, M. (2006). Stimulus-dependent processing of temporal order. Behav. Processes 71, 344–352.CrossrefGoogle Scholar

  • Forwood, S.E., Cowell, R.A., Bussey, T.J., and Saksida, L.M. (2012). Multiple cognitive abilities from a single cognitive algorithm. J. Cogn. Neurosci. 24, 1807–1825.Google Scholar

  • Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., and Raichle, M.E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. USA 102, 9673–9678.CrossrefGoogle Scholar

  • Francken, J.C. and Slors, M. (2014). From commonsense to science, and back: the use of cognitive concepts in neuroscience. Conscious. Cogn. 29, 248–258.CrossrefGoogle Scholar

  • Fregnac, Y. and Bathellier, B. (2005). Cortical correlates of low-level perception: from neural circuits to percepts. Neuron 88, 110–126.CrossrefGoogle Scholar

  • Froemke, R.C. and Schreiner, C.E. (2015). Synaptic plasticity as a cortical coding scheme. Curr. Opin. Neurobiol. 35, 185–199.CrossrefGoogle Scholar

  • Fukuda, K. and Vogel, E.K. (2009). Human variation in overriding attentional capture. J. Neurosci. 29, 8726–8733.CrossrefGoogle Scholar

  • Fuster, J.M. and Alexander, G.E. (1971). Neuron activity related to short-term memory. Science 173, 652–654.CrossrefGoogle Scholar

  • Garlick, D. (2002). Understanding the nature of the general factor of intelligence: the role of individual differences in neural plasticity as an explanatory mechanism. Psychol. Rev. 109, 116–136.CrossrefGoogle Scholar

  • Gauthier, B., Eger, E., Hesselmann, G., Giraud, A-L., and Kleinschmidt, A. (2012). Temporal tuning properties along the human ventral visual stream. J. Neurosci. 32, 14433–14441.CrossrefGoogle Scholar

  • Gerton, B.K., Brown, T.T., Meyer-Lindenberg, A., Kohn, P., Holt, J.L., Olsen, R.K., and Berman, K.F. (2004). Shared and distinct neurophysiological components of the digits forward and backward tasks as revealed by functional neuroimaging. Neuropsychologia 42, 1781–1787.CrossrefGoogle Scholar

  • Glascher, J., Tranel, D., Paul, L.K., Rudrauf, D., Rorden, C., Homday, A., Grabowski, T., Damasio, H. and Adolphs, R. (2009). Lesion mapping of cognitive abilities linked to intelligence. Neuron 61, 681–691.CrossrefGoogle Scholar

  • Glascher, J., Rudrauf, D., Colom, R., Paul, L.K., Tranel, D., Damasio, H., and Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proc. Natl. Acad. Sci. USA 107, 4705–4709.CrossrefGoogle Scholar

  • Goldman-Rakic, P. (2000). Localization of function all over again. Neuroimage 11, 451–457.CrossrefGoogle Scholar

  • Gonzalez-Tapia, D., Martinez-Torres, N.I., Hernandez-Gonzalez, M., Guevara, M.A., and Gonzalez-Burgos, I.G. (2016). Plastic changes to dentritic spines on layer V pyramidal neurons are involved in the rectifying role of the prefrontal cortex during the fast period of motor learning. Behav. Brain Res. 298, 261–267.CrossrefGoogle Scholar

  • Gosso, M.F., van Belzen, M., de Geus, E.J.C., Polderman, J.C., Heutink, P., Boomsma, D.I., and Posthuma, D. (2006). Association between the CHRM2 gene and intelligence in a sample of 304 Dutch families. Genes Brain Behav. 5, 577–584.CrossrefGoogle Scholar

  • Gosso, F.M., de Geus, J.C., Polderman, J.C., Boomsma, D.I., Posthuma, D., and Heutink, P. (2007). Exploring the functional role of the CHRM2 gene in human cognition: results from a dense genotyping and brain expression study. BMC Med. Genet. 8, 66.CrossrefGoogle Scholar

  • Green, A.E., Munato, M.R., DeYoung, C.G., Fossella, J.A., Fan, J., and Gray, J.R. (2008). Using genetic data in cognitive neuroscience: from growing pains to genuine insight. Nat. Rev. Neurosci. 9, 710–720.CrossrefGoogle Scholar

  • Greenwood, P.M., Lin, M.K., Sundararajan, R., Fryxell, K.J., and Parasuraman, R. (2009) Synergistic effects of genetic variation in nicotinic and muscarinic receptors on visual attention but not working memory. Proc. Natl. Acad. Sci. USA 106, 3633–3638.CrossrefGoogle Scholar

  • Greenwood, P.M., Parasuraman, R., and Espeseth, T. (2012). A cognitive phenotype for a polymorphism in the nicotinic receptor gene CHRNA4. Neurosci. Biobehav. Rev. 36, 1331–1341.Google Scholar

  • Grossberg, S. (2013). Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Netw. 37, 1–47.Google Scholar

  • Grossman, S.P. (1967). A Textbook of Physiological Psychology (New York: John Wiley & Sons).Google Scholar

  • Haas, R.H. (1988). Thiamin and the brain. Annu. Rev. Nutr. 8, 483–515.CrossrefGoogle Scholar

  • Habeck, C., Steffener, J., Barulli, D., Gazes, Y., Shaked, D., Salthouse, T., and Stern, Y. (2015). Making cognitive latent varuiables manifest: distinct neural networks for fluid reasoning and processing speed. J. Cogn. Neurosci. 27, 1249–1258.Google Scholar

  • Haier, R.J., Colom, R., Schroeder, D.H., Condon, C.A., Tang, C., Eaves, E., and Head, K. (2009). Gray matter and intelligence: is there a neuro-g? Intelligence 37, 136–144.CrossrefGoogle Scholar

  • Halpern, S.D., Andrews, T.J., and Purves, D. (1999). Interindividual variation in human visual performance. J. Cogn. Neurosci. 11, 521–534.Google Scholar

  • Hampshire, A., Hellyer, P.J., Parkin, B., Hiebert, N., MacDonald, P., Owen, A.M., Leech, R., and Rowe, J. (2016). Network mechanisms of intentional learning. Neuroimage 127, 123–134.CrossrefGoogle Scholar

  • Hangya, B., Pi, H-J., Kvitsiani, D., Ranade, S.P., and Kepecs, A. (2014). From circuit motifs to computations: mapping the behavioral repertoire of cortical interneurons. Curr. Opin. Neurobiol. 26, 117–124.CrossrefGoogle Scholar

  • Harris, K.D. and Thiele, A. (2011). Cortical state and attention. Nat. Rev. Neurosci. 12, 509–523.CrossrefGoogle Scholar

  • Hayakawa, T., Fujimaki, N., and Imaruoka, T. (2006). Temporal characteristics of neural activity related to target detection during visual search. Neuroimage 33, 296–306.CrossrefGoogle Scholar

  • Hazy, T.E., Frank, M.J., and O’Reilly, R.C. (2007). Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 362, 1601–1613.CrossrefGoogle Scholar

  • Heck, A., Fastenrath, M., Ackermann, S., Auschra, B., Bickel, H., Coynel, D., Gschwind, L., Jessen, F., Kaduszkiewicz, H., Maier, W., et al. (2014). Converging genetic and functional brain imaging evidence links neuronal excitability to working memory, psychiatric disease, and brain activity. Neuron 81, 1203–1213.CrossrefGoogle Scholar

  • Helie, S., Ell, S.W., and Ashby, F.G. (2015). Learning robust cortico-cortical associations with the basal ganglia: An integrative review. Cortex 64, 123–135.CrossrefGoogle Scholar

  • Hernandez-Peon, R., Scherrer, H., and Jouvet, M. (1956). Modification of electrical activity in cochlear nucleus during ‘attention’ in unanesthetized cats. Science 123, 331–332.CrossrefGoogle Scholar

  • Hirsch, J.A. (2003). Synaptic physiology and receptive field structure in the early visual pathway of the cat. Cereb. Cortex 13, 63–69.CrossrefGoogle Scholar

  • Ho, Y.C., Cheng, J.K., and Chiou, L.C. (2015). Impairment of sdenylyl cyclase-mediated glutamatergic synaptic plasticity in the periaqueductal grey in a rat model of neuropathic pain. J. Physiol. 593, 2955–2973.CrossrefGoogle Scholar

  • Hong, Z., Ng, K.K., Sim, S.K.Y., Ngeow, M.Y., Zheng, H., Lo, J.C., Chee, M.W.L., and Zhou, J. (2015) Diffenential age-dependent associations of gray matter volume and white matter integrity with processing speed in healthy older adults. Neuroimage 123, 42–50.CrossrefGoogle Scholar

  • Hopf, J-M., Luck, S.J., Boelmans, K., Schoenfeld, M.A., Boehler, C.N., Rieger, J., and Heinze, H-J. (2006). The neural site of attention matches the spatial scale of perception. J. Neurosci. 26, 3532–3540.CrossrefGoogle Scholar

  • Irlbacher, K., Kraft, A., Kehrer, S., and Brandt, S.A. (2014). Mechanisms and neuronal networks involved in reactive and proactive control of interference in working memory. Neurosci. Biobehav. Rev. 46, 58–70.CrossrefGoogle Scholar

  • Isaacson, J.S. and Scanziani, M. (2011). How inhibition shapes cortical activity. Neuron 72, 231–243.CrossrefGoogle Scholar

  • Jensen, A.R. (2000). The g factor: Psychometrics and biology. Novartis Found. Symp. 233, 37–57.Google Scholar

  • Johnson, W. and Deary, I.J. (2011). Placing inspection time, reaction time, and perceptual speed in the broader context of cognitive ability: the VPR model in the Lothian Birth Cohort 1936. Intelligence 39, 405–417.CrossrefGoogle Scholar

  • Ju, H., Dranias, M.R., Banumurthy, G., and VanDongen, A.M.J. (2015). Spatiotemporal memory is an intrinsic property of networks of dissociated cortical neurons. J. Neurosci. 35, 4040–4051.CrossrefGoogle Scholar

  • Jung, R.E. and Haier, R.J. (2007). The parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav. Brain Sci. 30, 135–187.CrossrefGoogle Scholar

  • Kane, M.J. and Engle, R.W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychon. Bull. Rev. 9, 637–671.CrossrefGoogle Scholar

  • Kane, M.J., Hambrick, D.Z., and Conway, A.R.A. (2005). Working memory capacity and fluid intelligence are strongly related constructs: comment on Ackerman, Beir, and Boyle (2005). Psychol. Bull. 131, 66–71.CrossrefGoogle Scholar

  • Karama, S., Colom, R., Johnson, W., Deary, I.J., Haier, R., Waber, D.P., Lepage, C., Ganjavi, H., Jung, R., and Evans, A.C. The Brain Development Group (2011). Cortical thickness correlates of specific cognitive performance accounted for by the general factor of intelligence in healthy children aged 6 to 18. Neuroimage 55, 1443–1453.CrossrefGoogle Scholar

  • Kaufman, S.B., DeYoung, C.G., Gray, J.R., Brown, J., and Mackintosh, N. (2009). Associative learning predicts intelligence above and beyond working memory and processing speed. Intelligence 37, 374–382.CrossrefGoogle Scholar

  • Konishi, M., McLaren, D.G., Engen, H., and Smallwood, J. (2015). Shaped by the past: the default mode network supports cognition that is independent of immediate perceptual input. PLoS One 10, e0132209.Google Scholar

  • Koshino, H., Minamato, T., Yaoi, K., Osaka, M., and Osaka, N. (2014). Coactivation of the default network regions and working memory network regions during task preparation. Sci. Rep. 4, 5954.CrossrefGoogle Scholar

  • Kovacs, K. and Conway, A.R.A. (2016). Process overlap theory: a unified account of the general factor of intelligence. Psychol. Inq. 27, 151–177.CrossrefGoogle Scholar

  • Kranzer, J.H. and Jensen, A.R. (1991). Unitary g: Unquestioned postulate or empirical fact? Intelligence 15, 437–448.CrossrefGoogle Scholar

  • Krauzlis, R.J., Lovejoy, L.P., and Zenon, A. (2013). Superior colliculus and visual spatial attention. Annu. Rev. Neurosci. 36, 165–182.CrossrefGoogle Scholar

  • Kuznetsova, K.A., Maniega, S.M., Ritchie, S.J., Cox, S.R., Storkey, A.J., Starr, J.M., Wardlaw, J.M., Deary, I.J., and Bastin, M.E. (2016). Brain white matter structure and information processing speed in healthy older age. Brain Struct. Funct. 221, 3223–3235.CrossrefGoogle Scholar

  • Lamb, Y.N., Thompson, C.S., McKay, N.S., Waldie, K.E., and Kirk, I.J. (2015). The brain-derived neurotropic factor (BDNF) val66met polymorphism differentially affects performance on subscales of the Wechsler memory scale – third edition (WMS-III). Front. Psychol. 6, 1212.CrossrefGoogle Scholar

  • Larsen, S. and Sjostrom, P.J. (2015). Synapse-type-specific plasticity in local circuits. Curr. Opin. Neurobiol. 35, 127–135.CrossrefGoogle Scholar

  • Levy, R. and Goldman-Rakic, P.S. (2000). Segregation of working memory functions within the dorsallateral prefrontal cortex. Exp. Brain Res. 133, 23–32.CrossrefGoogle Scholar

  • Levy, P., Meister, E., and Schlachter, F. (2014). Reconfigurable swarm robots produce self-assembling and self-repairing organisms. Rob. Auton. Syst. 62, 1371–1376.CrossrefGoogle Scholar

  • Li, K., Guo, L., Nie, J., Li, G., and Liu, T. (2009). Review of methods for functional brain connectivity detection using fMRI. Comput. Med. Imaging Graph. 33, 131–139.Google Scholar

  • Li, P., Legault, J., and Litcofsky, K.A. (2014). Neuroplasticity as a function of second language learning: anatomical changes in the human brain. Cortex 58, 301–324.CrossrefGoogle Scholar

  • Li, H., Wei, H., Xiao, J., and Wang, T. (2015). Co-evolution framework of swarm self-assembly robots. Neurocomputing 148, 112–121.CrossrefGoogle Scholar

  • Lind, P.A., Luciano, M., Horan, M.A., Marioni, R.E., Wright, M.J., Bates, T.C., Rabbitt, P., Harris, S.E., Davidson, Y., Deary, I.J., et al. (2009). No association between cholinergic muscarinic receptor 2 (CHRM2) and genetic variation and cognitive abilities in three independent samples. Behav. Genet. 39, 513–523.CrossrefGoogle Scholar

  • Lipinski, J., Schneegans, S., Sandamirskaya, Y., Spencer, J.P., and Schoner, G. (2012). A neurobehavioral model of flexible spatial language behavior. J. Exp. Psychol. Learn. Mem. Cogn. 38, 1490–1511.CrossrefGoogle Scholar

  • Livingstone, M. and Hubel, D. (1988). Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science 240, 740–749.CrossrefGoogle Scholar

  • Luciano, M., Hansell, N.K., Lahti, J., Davies, G., Medland, S.E., Raikkonen, K., Tenesa, A., Widen, E., McGhee, K.A., Palotie, A., et al. (2011). Whole genome association scan for genetic polymorphisms influencing information processing speed. Biol. Psychol. 86, 193–202.CrossrefGoogle Scholar

  • MacCallum, R.C. and Browne, M.W. (1993). The use of causal indicators in covariance structure models: some practical issue. Psychol. Bull. 114, 533–541.CrossrefGoogle Scholar

  • MacCallum, R.C., Wegener, D.T., Uchino, B.N., and Fabrigar, L.R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychol. Bull. 114, 185–199.CrossrefGoogle Scholar

  • Mackintosh, N.J. and Bennett, E.S. (2003). The fractionation of working memory maps onto different components of intelligence. Intelligence 31, 519–531.CrossrefGoogle Scholar

  • Maguire, E.A., Intraub, H., and Mullally, S.L. (2016). Scenes, spaces, and memory traces: what does the hippocampus do? Neuroscientist 22, 432–439.CrossrefGoogle Scholar

  • Makino, Y., Yokosawa, K., Takeda, Y., and Kumada, T. (2004). Visual search and memory search engage extensive overlapping cerebral corticies: An fMRI study. Neuroimage 23, 525–533.CrossrefGoogle Scholar

  • Markus, K.A. and Borsboom, D. (2013). Reflective measurement models, behavior domains, and common causes. New Ideas Psychol. 31, 54–64.CrossrefGoogle Scholar

  • McFarland, D.J. (1985). Mouse phenotype modulates the behavioral effects of acute thiamine deficiency. Physiol. Behav. 35, 597–601.CrossrefGoogle Scholar

  • McFarland, D.J. (2012). A single g factor is not necessary to simulate positive correlations between cognitive tests. J. Clin. Exp. Neuropsychol. 34, 378–384.CrossrefGoogle Scholar

  • McFarland, D.J. (2014). Simulating the effects of common and specific abilities on test performance: an evaluation of factor analysis. J. Speech Lang. Hear. Res. 57, 1919–1928.Google Scholar

  • McFarland, D.J. (2017). Modeling general and specific abilities: evaluation of bifactor models for the WJ-III. Assessment 23, 698–706.CrossrefGoogle Scholar

  • McFarland, D.J., Sikora, E., and Hotchin, J. (1986). The production of focal herpes encephalitis in mice by stereotaxic inoculation of virus: anatomical and behavioral effects. J. Neurol. Sci. 72, 307–318.CrossrefGoogle Scholar

  • McFarland, D.J., Cacace, A.T., and Setzen, G. (1998). Temporal-order discrimination for selected auditory and visual stimulus dimensions. J. Speech Lang. Hear. Res, 41, 300–314.Google Scholar

  • McGrew, K.S. (2009). CHC theory and the human cognitive abilities project: standing on the shoulders of the giants of psychometric intelligence research. Intelligence 37, 1–10.CrossrefGoogle Scholar

  • McGrew, K. S., and Woodcock, R. W. (2001). Woodcock-Johnson III Technical Manual (Riverside).Google Scholar

  • Middleton, F.A. and Strick, P.L. (2000). Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Res. Rev. 31, 236–250.CrossrefGoogle Scholar

  • Miller, E.M. (1994). Intelligence and brain myelination: a hypothesis. Pers. Individ. Dif. 17, 803–832.CrossrefGoogle Scholar

  • Miller, E.K. and Cohen, J.D. (2001). An integrated theory of prefrontal cortex activity. Annu. Rev. Neurosci. 24, 167–202.CrossrefGoogle Scholar

  • Milner, A.D. and Goodale, M.A. (2008). Two visual systems reviewed. Neuropsychologia 46, 774–785.CrossrefGoogle Scholar

  • Mooney, D.M., Zhang, L., Basile, C., Senatorov, V.V., Ngsee, J., Omar, A., and Hu, B. (2004). Distinct forms of cholinergic modulation in parallel thalamic sensory pathways. Proc. Natl. Acad. Sci. USA 101, 320–324.CrossrefGoogle Scholar

  • Moosbrugger, H., Goldhammer, F., and Schweizer, K. (2006). Latent factors underlying individual differences in attention measures: perceptual and executive attention. Eur. J. Psychol. Assess. 22, 177–188.CrossrefGoogle Scholar

  • Morris, L.S., Kundu, P., Dowell, N., Mechelmans, D.J., Favre, P., Irvine, M.A., Robbins, T.W., Daw, N., Bullmore, E.T., Harrison, N.A., et al. (2016). Fronto-striatal organization: defining functional and microstructural substrates of behavioral flexibility. Cortex 74, 118–133.CrossrefGoogle Scholar

  • Murray, E.A. and Richmond, B.J. (2001). Role of perirhinal cortex in object perception, memory, and associations. Curr. Opin. Neurobiol. 11, 188–193.CrossrefGoogle Scholar

  • Naatanen, R. (1992). Attention and Brain Function (Hillsdale, New Jersey: Lawrence Erlbaum Associates), p. 3.Google Scholar

  • Naatanen, R., Tervaniemi, M., Sussman, E., Paavilainen, P., and Winkler, I. (2001). Primitive intelligence in the auditory cortex. Trends Neurosci. 24, 283–288.CrossrefGoogle Scholar

  • Nam, D. and Kim, S-Y. (2008). Gene-set approach for expression pattern analysis. Brief Bioinform. 9, 189–197.CrossrefGoogle Scholar

  • Needleman, H. (2004). Lead poisoning. Annu. Rev. Med. 55, 209–222.CrossrefGoogle Scholar

  • Nelson, C.L., Sarter, M., and Bruno, J.P. (2005). Prefrontal cortical modulation of acetylcholine release in posterior parietal cortex. Neuroscience 132, 347–359.CrossrefGoogle Scholar

  • Nicolaides, C., Juanes, R., and Cueto-Felgueroso, L. (2016). Self-organization of network dynamics into local quantized states. Sci. Rep. 6, 21360.CrossrefGoogle Scholar

  • Nobre, A.C., Sebestyen, G.N., Gitelman, D.R., Frith, C.D., and Mesulam, M.M. (2002). Filtering of distractors during visual search studied by positron emission tomography. Neuroimage 16, 968–976.CrossrefGoogle Scholar

  • Oberauer, K., Suss, H-M., Wilhelm, O., and Wittmann, W.W. (2008). Which working memory functions predict intelligence? Intelligence 36, 641–652.CrossrefGoogle Scholar

  • O’Connell, M.N., Barczak, A., Schroeder, C.E., and Lakatos, P. (2014). Layer specific sharpening of frequency tuning by selective attention in primary auditory cortex. J. Neurosci. 34, 16496–16496.CrossrefGoogle Scholar

  • Olsson, H., Bjorkman, C., Haag, K., and Juslin, P. (1998). Auditory inspection time: on the importance of selecting the appropriate sensory continuum. Pers. Individ. Dif. 25, 627–634.CrossrefGoogle Scholar

  • Pasternak, T. and Greenlee, M.W. (2005). Working memory in primate sensory systems. Nat. Rev. Neurosci. 6, 97–107.CrossrefGoogle Scholar

  • Paivio, A. (2014). Intelligence, dual coding theory, and the brain. Intelligence 47, 141–158.CrossrefGoogle Scholar

  • Pearce, E. and Bridge, H. (2013). Is orbital volume associated with eyeball and visual cortex volume in humans? Ann. Hum. Biol. 40, 531–540.CrossrefGoogle Scholar

  • Pessoa, L. (2014). Understanding brain networks and brain organization. Phys. Life Rev. 11, 400–435.CrossrefGoogle Scholar

  • Pezzulo, G. and Cisek, P. (2016). Navigating the affordance landscape: feedback control as a process model of behavior and cognition. Trends Cogn. Sci. 20, 414–424.CrossrefGoogle Scholar

  • Poghosyan, V. and Ioannides, A.A. (2007). Precise mapping of early visual responses in space and time. Neuroimage 35, 759–770.CrossrefGoogle Scholar

  • Posner, M.I. (1975). Psychobiology of Attention. Handbook of Psychobiology. M.S. Gazzaniga and C. Blakemore, eds. (Cambridge, MA: Academic Press), pp. 441–480.Google Scholar

  • Posner, M.L. and DiGirolamo, G.J. (2000). Cognitive neuroscience: origins and promise. Psychol. Bull. 126, 873–889.CrossrefGoogle Scholar

  • Primi, R. (2014). Developing a fluid intelligence scale through a combination of Rasch modeling and cognitive psychology. Psychol. Assess. 26, 774–788.CrossrefGoogle Scholar

  • Rasmusson, D.D., Smith, S.A., and Semba, K. (2007). Inactivation of prefrontal cortex abolishes cortical acetylcholine release evoked by sensory or sensory pathway stimulation in the rat. Neuroscience 149, 232–241.CrossrefGoogle Scholar

  • Rauschecker, J.P. (2009). Cortical processing streams and central auditory plasticity. Controversies in Central Auditory Processing Disorder. A.T. Cacace and D.J. McFarland, eds. (San Diego, CA: Plural Publishing), pp. 61–82.Google Scholar

  • Reed, T.E. and Jensen, A.R. (1992). Conduction velocity in a brain nerve pathway of normal adults correlates with intelligence level. Intelligence 16, 259–272.CrossrefGoogle Scholar

  • Reeve, C.L. and Charles, J.E. (2008). Survey of opinions on the primacy of g and social consequences of ability testing: a comparison of expert and non-expert views. Intelligence 36, 681–688.CrossrefGoogle Scholar

  • Reingold, E.M. and Glaholt, M.G. (2014). Cognitive control of fixation duration in visual search: the role of extrafovial processing. Vis. Cogn. 22, 610–634.CrossrefGoogle Scholar

  • Richiardi, J., Altmann, A., Milazzo, A-C., Chang, C., Chakravarty, M.M., Banaschewski, T., Barker, G.J., Bokde, A.L.W., Bromberg, U., Büchel, C., et al. (2015). Correlated gene expression supports synchronous activity in networks. Science 348, 1241–1244.CrossrefGoogle Scholar

  • Roman, F.J.R., Abad, F.J., Escorial, S., Burgaleta, M., Martinez, K., Alvarez-Linera, J., Quiroga, M.A., Karama, S., Haier, R.J., and Colom, R. (2014). Reversed hierarchy in the brain for general and specific cognitive abilities: a morphometric analysis. Hum. Brain Mapp. 35, 3805–3818.CrossrefGoogle Scholar

  • Rosvold, H.E. (1972). The frontal lobe system: cortical-subcortical interrelationships. Acta Neurobiol. Exp. (Warsaw) 32, 439–460.Google Scholar

  • Rottschy, C., Caspers, S., Roski, C., Reetz, K., Dogan, I., Schilz, J.B., Zilles, K., Laird, A.R., Fox, P.T., and Eickhoff, S.B. (2013). Differentiated parietal connectivity of frontal regions for ‘what’ and ‘where’ memory. Brain Struct. Funct. 218, 1551–1567.CrossrefGoogle Scholar

  • Rougier, N.P., Noelle, D.C., Braver, T.S., Cohen, J.D., and O’Reilly, R.C. (2005). Prefrontal cortex and flexible cognitive control: rules without symbols. Proc. Natl. Acad. Sci. USA 102, 7338–7343.CrossrefGoogle Scholar

  • Ruz, M. (2006). Let the brain explain the mind: the case of attention. Philos. Psychol. 19, 495–505.CrossrefGoogle Scholar

  • Salinas, E. and Their, P. (2000). Gain modulation: a major computational principal of the central nervous system. Neuron 27, 15–21.CrossrefGoogle Scholar

  • Saper, C.B. (1984). Organization of cerebral cortical afferent systems in the rat. II. Magnocellular basal nucleus. J. Comp. Neurol. 222, 313–342.CrossrefGoogle Scholar

  • Scantlebury, N., Bouffet, E., Laughlin, S., Strother, D., McConnel, D., Hukin, J., Fryer, C., Laperrierre, N., Montour-Proulx, I., Keene, D., et al. (2016). White matter and information processing speed following treatment with cranial-spinal radiation for pediatric brain tumor. Neuropsychology 30, 425–438.CrossrefGoogle Scholar

  • Schenkluhun, B., Ruff, C.C., Heinen, K., and Chambers, C.D. (2008). Parietal stimulation decouples spatial and feature-based attention. J. Neurosci. 28, 11106–11110.CrossrefGoogle Scholar

  • Schettino, A., Rossi, V., Pourtois, G., and Muller, M.M. (2016). Involuntary attentional orienting in the absence of awareness speeds up early sensory processing. Cortex 74, 107–117.CrossrefGoogle Scholar

  • Schmiedek, F., Hildebrandt, A., Lovden, M., Wilhelm, O., and Lindenberger, U. (2009). Complex span versus updating tasks of working memory: the gap is not that deep. J. Exp. Psychol. Learn. Mem. Cogn. 35, 1089–1096.CrossrefGoogle Scholar

  • Schneider, K.K., Schote, A.B., Meyer, J., Markett, S., Reuter, M., and Frings, C. (2015). Individual response speed is modulated by variants of the gene encoding the alpha 4 sub-unit of the nicotinic acetylcholine receptor (CHRNA4). Behav. Brain Res. 284, 11–18.Google Scholar

  • Schweizer, K. (2005). An overview of research into the cognitive basis of intelligence. J. Individ. Differ. 26, 43–51.Google Scholar

  • Schweizer, K., Moosbrugger, H., and Goldhammer, F. (2005). The structure of the relationship between attention and intelligence. Intelligence 33, 589–611.CrossrefGoogle Scholar

  • Scolari, M., Seidl-Rathkopf, K.N., and Kastner, S. (2015). Functions of the human frontoparietal attention network: evidence from neuroimaging. Curr. Opin. Behav. Sci. 1, 32–39.CrossrefGoogle Scholar

  • Sheppard, L.D. and Vernon, P.A. (2008). Intelligence and speed of information processing: a review of 50 years of research. Pers. Individ. Dif. 44, 535–551.CrossrefGoogle Scholar

  • Sherman, M.S. (2007). The thalamus is more than just a relay. Curr. Opin. Neurobiol. 17, 417–422.CrossrefGoogle Scholar

  • Silverstein, A.B. (1982). Factor structure of the Wechsler adult intelligence scale-revised. J. Consult. Clin. Psychol. 50, 661–664.CrossrefGoogle Scholar

  • Slee, S.J. and David, S.V. (2015). Rapid task-related plasticity of spectrotemporal receptive fields in the auditory midbrain. J. Neurosci. 35, 13090–13102.CrossrefGoogle Scholar

  • Soreq, H. (2015). Checks and balances on cholinergic signaling in brain and body function. Trends Neurosci. 38, 448–458.CrossrefGoogle Scholar

  • Soriano-Mas, C., Harrison, B.J., Pujol, J., Lopez-Sola, M., Hernandez-Ribas, R., Alonso, P., Contreras-Rodriguez, O., Gimenez, M., Blanco-Hinojo, L., Ortiz, H., et al. (2013). Structural covariance of the neostriatum with regional gray matter volumes. Brain Struct. Funct. 218, 697–709.CrossrefGoogle Scholar

  • Sreenivasan, K.K., Curtis, C.E., and D’Esposito, M. (2014). Revisiting the role of persistent neural activity during working memory. Trends Cogn. Sci. 18, 82–89.CrossrefGoogle Scholar

  • Stam, C.J. and van Straaten, E.C.W. (2012). The organization of physiological brain networks. Clin. Neurophysiol. 123, 1067–1087.CrossrefGoogle Scholar

  • Stankov, L. (1983). Attention and intelligence. J. Educ. Psychol. 75, 471–490.CrossrefGoogle Scholar

  • Staufer, C.C., Haldemann, J., Troche, S.J., and Rammsayer, T.H. (2012). Auditory and visual temporal sensitivity: evidence for a hierarchical structure of modality-specific and modality-independent levels of temporal information processing. Psychol. Res. 76, 20–31.CrossrefGoogle Scholar

  • Stemmler, M., Mathis, A., and Herz, A.V.M. (2015). Connecting multiple spatial scales to decode the population activity of grid cells. Sci. Adv. 1, e15008.CrossrefGoogle Scholar

  • Stigliani, A., Weiner, K.S., and Grill-Spector, K. (2015). Temporal processing capacity in high-level visual cortex is domain specific. J. Neurosci. 35, 12412–12424.CrossrefGoogle Scholar

  • Stromer, V.S., Passow, S., Biesenack, J., and Li, S-C. (2011). Dopaminergic and cholinergic modulations of visual-spatial attention and working memory: insights from molecular genetic research and implications for adult cognitive development. Dev. Psychol. 48, 875–889.CrossrefGoogle Scholar

  • Stuss, D.T. (2011). Functions of the frontal lobes: Relation to executive functions. J. Int. Neuropsychol. Soc. 17, 759–765.CrossrefGoogle Scholar

  • Suga, N., Gao, E., Zhang, Y., Ma, X., and Olsen, J.F. (2000). The corticofugal system for hearing: recent progress. Proc. Natl. Acad. Sci. USA 97, 11807–11814.CrossrefGoogle Scholar

  • Sutherland, M.T., Ray, K.L., Riedel, M.C., Yanes, J.A., Stein, E.A., and Laird, A.R. (2015). Neurobiological impact of nicotinic acetylcholine receptor agonists: an activation likehood estimation meta-analysis of pharmacologic neuroimaging studies. Biol. Psychiatry 78, 711–720.CrossrefGoogle Scholar

  • Tachibana, R., Namba, Y., and Noguchi, Y. (2014). Two factors of visual recognition independently correlate with fluid intelligence. PLoS One 9, e97429.Google Scholar

  • Takeuchi, H., Taki, Y., Hashizume, H., Asano, K., Sassa, Y., Yokota, S., Kotozaki, Y., Nouchi, R., and Kawashima, R. (2015). The impact of television viewing on brain structures: cross-sectional and longitudinal analysis. Cereb. Cortex 25, 1188–1197.CrossrefGoogle Scholar

  • Thompson, G.H. (1920). General versus group factors in mental activities. Psychol. Rev. 27, 173–190.CrossrefGoogle Scholar

  • Thompson, R.F. (2005). In search of memory traces. Annu. Rev. Psychol. 56, 1–23.CrossrefGoogle Scholar

  • Thompson-Schill, S.L., Bedny, M., and Goldberg, R.F. (2005). The frontal lobes and the regulation of mental activity. Curr. Opin. Neurobiol. 15, 219–224.CrossrefGoogle Scholar

  • Thorson, I.L., Lienard, J., and David, S.V. (2015). The essential complexity of auditory receptive fields. PLoS Comput. Biol. 11, e1004628.CrossrefGoogle Scholar

  • Tulsky, D.S. and Price, L.R. (2003). The joint WAIS-III and WMS-III factor structure: development and cross-validation of a six-factor model of cognitive functioning. Psychol. Assess. 15, 149–162.CrossrefGoogle Scholar

  • Turken, A.U., Whitfield-Gabrieli, S., Bammer, R., Baldo, J.V., Dronkers, N.F., and Gabrieli, J.D.E. (2008). Cognitive processing speed and the structure of white matter pathways: convergent evidence from normal variation and lesion studies. Neuroimage 42, 1032–1044.CrossrefGoogle Scholar

  • Turkheimer, F.E., Leech, R., Expert, P., Lord, L-D., and Vernon, A.C. (2015). The brain’s code and its canonical computational motifs. From sensory cortex to the default mode network: A multiscale model of brain function in health and disease. Neurosci. Biobehav. Rev. 55, 211–222.CrossrefGoogle Scholar

  • Ungerleider, L.G. and Haxby, J.V. (1994). ‘What’ and ‘where’ in the human brain. Curr. Opin. Neurobiol. 4, 157–165.CrossrefGoogle Scholar

  • Unsworth, N. (2010). On the division of working memory and long-term memory and their relation to intelligence: a latent variable approach. Acta Psychol. (Amst) 134, 16–28.CrossrefGoogle Scholar

  • Van Essen, D.C. (2005). Corticothalamic and thalmocortical information flow in the primate visual system. Prog. Brain Res. 149, 173–185.CrossrefGoogle Scholar

  • Vandenberg, S.G. (1966). Contributions of twin research to psychology. Psychol. Bull. 66, 327–352.CrossrefGoogle Scholar

  • Verghese, A., Kolbe, S.C., Anderson, A.J., Egan, G.F., and Vidyasagar, T.R. (2014). Functional size of human visual area V1: a neural correlate of top-down attention. Neuroimage 93, 47–52.CrossrefGoogle Scholar

  • Ward, L.C., Bergman, M.A., and Hebert, K.R. (2012). WAIS-IV subtest covariance structure: conceptual and statistical considerations. Psychol. Assess. 24, 328–340.CrossrefGoogle Scholar

  • Wechsler, D. (2008). Wechsler Adult Intelligence Scale – Fourth Edition: Technical and Interpretive Manual (Pearson).Google Scholar

  • Wei, P., Muller, H.J., Pollmann, S., and Zhou, X. (2011). Neural correlates of binding features within- or cross-dimensions in visual conjunction search: An fMRI study. Neuroimage 57, 235–241.CrossrefGoogle Scholar

  • Wilson, M., Wilson, W.A., and Sunenshine, H.S. (1968). Perception, learning, and retention of visual stimuli by monkeys with inferotemporal lesions. J. Comp. Physiol. Psychol. 65, 406–412.CrossrefGoogle Scholar

  • Wolpaw, J.R. (1997). The complex structure of a simple memory. Trends Neurosci. 20, 588–594.CrossrefGoogle Scholar

  • Wongupparaj, P., Kumari, V., and Morris, R.C. (2015). The relation between a multicomponent working memory and intelligence: the roles of central executive and short-term storage functions. Intelligence 53, 166–180.CrossrefGoogle Scholar

  • Wurtz, R.H. (2008). Neuronal mechanisms of visual stability. Vision Res. 48, 2070–2089.CrossrefGoogle Scholar

  • Yeo, B.T.T., Krienen, F.M., Eickhoff, S.B., Yaakub, S.N., Fox, P.T., Buckner, R.L., Asplund, C.L., and Chee, M.W.I. (2015). Functional specialization and flexibility in human association cortex. Cereb. Cortex 25, 3654–3672.CrossrefGoogle Scholar

  • Zaborszky, L., Csordas, A., Mosca, K., Kim, J., Gielow, M.R., Vadasz, C., and Nadasdy, Z. (2015). Neurons in the basal forebrain project to the cortex in a complex topographic organization that reflects corticocortical connectivity patterns: an experimental study based on retrograde tracing and 3D reconstruction. Cereb. Cortex 25, 118–137.CrossrefGoogle Scholar

  • Zatorre, R.J., Fields, R.D., and Johansen-Berg, H. (2012). Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15, 528–536.CrossrefGoogle Scholar

About the article

Received: 2016-11-01

Accepted: 2016-12-17

Published Online: 2017-02-14

Published in Print: 2017-05-24

Citation Information: Reviews in the Neurosciences, Volume 28, Issue 4, Pages 343–362, ISSN (Online) 2191-0200, ISSN (Print) 0334-1763, DOI: https://doi.org/10.1515/revneuro-2016-0073.

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