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Published online by Cambridge University Press:  13 July 2023

Richard J. Haier
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University of California, Irvine
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References

Abdellaoui, A., Dolan, C. V., Verweij, K. J. H., & Nivard, M. G. (2022). Gene–environment correlations across geographic regions affect genome-wide association studies. Nature Genetics. doi:10.1038/s41588-022-01158-0Google Scholar
Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131(1), 3060. doi:10.1037/0033-2909.131.1.30Google Scholar
Adamczyk, A. K., & Zawadzki, P. (2020). The memory-modifying potential of optogenetics and the need for neuroethics. NanoEthics, 14(3), 207225. doi:10.1007/s11569-020-00377-1Google Scholar
Alavash, M., Lim, S. J., Thiel, C., Sehm, B., Deserno, L., & Obleser, J. (2018). Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance. Neuroimage, 172, 341356. doi:10.1016/j.neuroimage.2018.01.048Google Scholar
Alkire, M. T., & Haier, R. J. (2001). Correlating in vivo anaesthetic effects with ex vivo receptor density data supports a GABAergic mechanism of action for propofol, but not for isoflurane. British Journal of Anaesthesia, 86(5), 618626. Retrieved from www.ncbi.nlm.nih.gov/pubmed/11575335CrossRefGoogle Scholar
Alkire, M. T., Haier, R. J., & Fallon, J. H. (2000). Toward a unified theory of narcosis: Brain imaging evidence for a thalamocortical switch as the neurophysiologic basis of anesthetic-induced unconsciousness. Consciousness and Cognition, 9(3), 370386.Google Scholar
Alkire, M. T., Haier, R. J., Barker, S. J., Shah, N. K., Wu, J. C., & Kao, Y. J. (1995). Cerebral metabolism during propofol anesthesia in humans studied with positron emission tomography. Anesthesiology, 82(2), 393403; discussion 327A. Retrieved from www.ncbi.nlm.nih.gov/pubmed/7856898CrossRefGoogle ScholarPubMed
Alkire, M. T., Pomfrett, C. J. D., Haier, R. J., Gianzero, M. V., Chan, C. M., Jacobsen, B. P., & Fallon, J. H. (1999). Functional brain imaging during anesthesia in humans – Effects of halothane on global and regional cerebral glucose metabolism. Anesthesiology, 90(3), 701709.CrossRefGoogle ScholarPubMed
Allegrini, A. G., Selzam, S., Rimfeld, K., von Stumm, S., Pingault, J. B., & Plomin, R. (2019). Genomic prediction of cognitive traits in childhood and adolescence. Molecular Psychiatry, 24(6), 819827. doi:10.1038/s41380-019-0394-4Google Scholar
Anderson, D. J. (2012). Optogenetics, sex, and violence in the brain: Implications for psychiatry. Biological Psychiatry, 71(12), 10811089. doi:10.1016/j.biopsych.2011.11.012Google Scholar
Anderson, J. W., Johnstone, B. M., & Remley, D. T. (1999). Breast-feeding and cognitive development: A meta-analysis. The American Journal of Clinical Nutrition, 70(4), 525535. Retrieved from www.ncbi.nlm.nih.gov/pubmed/10500022Google Scholar
Anderson, K. M., & Holmes, A. J. (2021). Predicting individual differences in cognitive ability from brain imaging and genetics. In Barbey, A. K., Karama, S., & Haier, R. J. (Eds.), Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 327348). New York: Cambridge University Press.Google Scholar
Andreasen, N. C., Flaum, M., Swayze, V., Oleary, D. S., Alliger, R., Cohen, G., … Yuh, W. T. C. (1993). Intelligence and brain structure in normal individuals. American Journal of Psychiatry, 150(1), 130134.Google Scholar
Antal, A., Luber, B., Brem, A. K., Bikson, M., Brunoni, A. R., Cohen Kadosh, R., … Paulus, W. (2022). Non-invasive brain stimulation and neuroenhancement. Clinical Neurophysiological Practise, 7, 146165. doi:10.1016/j.cnp.2022.05.002Google Scholar
Arden, R. (2003). An arthurian romance. In Nyborg, H. (Ed.), The Scientific Study of General Intelligence (pp. 533553). Amsterdam: Pergamon.Google Scholar
Arden, R., Chavez, R. S., Grazioplene, R., & Jung, R. E. (2010). Neuroimaging creativity: A psychometric view. Behavioral Brain Research, 214(2), 143156. doi:10.1016/j.bbr.2010.05.015Google Scholar
Arden, R., Luciano, M., Deary, I. J., Reynolds, C. A., Pedersen, N. L., Plassman, B. L., … Visscher, P. M. (2015). The association between intelligence and lifespan is mostly genetic. International Journal of Epidemiology. doi:10.1093/ije/dyv112Google Scholar
Ardlie, K. G., DeLuca, D. S., Segre, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., … Consortium, G. (2015). The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science, 348(6235), 648660. doi:10.1126/science.1262110Google Scholar
Arevalo, A., Abusamra, V., & Lepski, G. (2022). Editorial: How to improve neuroscience education for the public and for a multi-professional audience in different parts of the globe. Frontiers in Human Neuroscience, 16, 973893.Google Scholar
Aristizabal, M. J., Anreiter, I., Halldorsdottir, T., Odgers, C. L., McDade, T. W., Goldenberg, A., … O’Donnell, K. J. (2020). Biological embedding of experience: A primer on epigenetics. Proceedings of the National Academy of Sciences, 117(38), 23261232620. doi:10.1073/pnas.1820838116Google Scholar
Asbury, K., & Fields, D. (2021). Implications of biological research on intelligence for education and public policy. In Barbey, A., Karama, S., & Haier, R. J. (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 399–415). New York: Cambridge University Press.Google Scholar
Asbury, K., & Plomin, R. (2014). G Is for Genes : The Impact of Genetics on Education and Achievement. Chichester, West Sussex: Wiley-Blackwell.Google Scholar
Asbury, K., & Wai, J. (2019). Viewing education policy through a genetic lens. Journal of School Choice, 115. doi:10.1080/15582159.2019.1705008Google Scholar
Asbury, K., Wachs, T. D., & Plomin, R. (2005). Environmental moderators of genetic influence on verbal and nonverbal abilities in early childhood. Intelligence, 33(6), 643661.Google Scholar
Ashburner, J., & Friston, K. (1997). Multimodal image coregistration and partitioning – a unified framework. Neuroimage, 6(3), 209217.Google Scholar
Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry – the methods. Neuroimage, 11(6 Pt 1), 805821.Google Scholar
Aston-Jones, G., & Deisseroth, K. (2013). Recent advances in optogenetics and pharmacogenetics. Brain Research, 1511, 15. doi:10.1016/j.brainres.2013.01.026Google Scholar
Atherton, M., Zhuang, J. C., Bart, W. M., Hu, X. P., & He, S. (2000). A functional magnetic resonance imaging study of chess expertice. Journal of Cognitive Neuroscience, 105105.Google Scholar
Au, J., Buschkuehl, M., Duncan, G. J., & Jaeggi, S. M. (2016). There is no convincing evidence that working memory training is NOT effective: A reply to Melby-Lervag and Hulme (2015). Psychonomic Bulletin and Review, 23(1), 331337. doi:10.3758/s13423-015-0967-4Google Scholar
Au, J., Gibson, B. C., Bunarjo, K., Buschkuehl, M., & Jaeggi, S. M. (2020). Quantifying the difference between active and passive control groups in cognitive interventions using two meta-analytical approaches. Journal of Cognitive Enhancement, 4(2), 192210. doi:10.1007/s41465-020-00164-6Google Scholar
Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: A meta-analysis. Psychonomic Bulletin and Review, 22(2), 366377. doi:10.3758/s13423-014-0699-xGoogle Scholar
Bagot, K. S., & Kaminer, Y. (2014). Efficacy of stimulants for cognitive enhancement in non-attention deficit hyperactivity disorder youth: A systematic review. Addiction, 109(4), 547557. Retrieved from www.ncbi.nlm.nih.gov/pubmed/24749160Google Scholar
Bailey, M. J., Sun, S., & Timpe, B. (2021). Prep School for Poor Kids: The long-run impacts of head start on human capital and economic self-sufficiency. American Economic Review, 111(12), 39634001. doi:10.1257/aer.20181801Google Scholar
Barbey, A. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 820.Google Scholar
Barbey, A. (2021). Human intelligence and network neuroscience. In Barbey, A., Karama, S., & Haier, R. J. (Eds.), Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 102122). New York: Cambridge University Press.Google Scholar
Barbey, A. K., Colom, R., Paul, E. J., & Grafman, J. (2014). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Structure and Function, 219(2), 485494.Google Scholar
Barbey, A. K., Colom, R., Paul, E., Forbes, C., Krueger, F., Goldman, D., & Grafman, J. (2014). Preservation of general intelligence following traumatic brain injury: Contributions of the Met66 brain-derived neurotrophic factor. Plos One, 9(2).Google Scholar
Barbey, A. K., Colom, R., Solomon, J., Krueger, F., Forbes, C., & Grafman, J. (2012). An integrative architecture for general intelligence and executive function revealed by lesion mapping. Brain, 135(Pt 4), 11541164. doi:10.1093/brain/aws021Google Scholar
Barbey, A., Karama, S., & Haier, R. J. (2021). Cambridge Handbook of Intelligence and Cognitive Neuroscience. New York: Cambridge University Press.CrossRefGoogle Scholar
Barnett, W. S., & Hustedt, J. T. (2005). Head start’s lasting benefits. Infants and Young Children, 18(1), 1624.Google Scholar
Bashwiner, D. M. (2018). The neuroscience of musical creativity. In J. R. E. & V. O. (Eds.), The Cambridge Handbook of the Neuroscience of Creativity (pp. 495516). Cambridge: Cambridge University Press.Google Scholar
Bashwiner, D. M., Wertz, C. J., Flores, R. A., & Jung, R. E. (2016). Musical creativity “Revealed” in brain structure: Interplay between motor, default mode, and limbic networks. Scientific Reports, 6, 20482. doi:10.1038/srep20482Google Scholar
Bassett, D. S., Yang, M., Wymbs, N. F., & Grafton, S. T. (2015). Learning-induced autonomy of sensorimotor systems. Natural Neuroscience, 18(5), 744751. doi:10.1038/nn.3993Google Scholar
Basso, A., De Renzi, E., Faglioni, P., Scotti, G., & Spinnler, H. (1973). Neuropsychological evidence for the existence of cerebral areas critical to the performance of intelligence tasks. Brain, 96(4), 715728.Google Scholar
Basten, U., & Fiebach, C. J. (2021). Functional brain imaging of intelligence. In Barbey, A., Karama, S., & Haier, R. J. (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 235260). New York: Cambridge University Press.Google Scholar
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51(0), 1027. doi:10.1016/j.intell.2015.04.009Google Scholar
Basten, U., Stelzel, C., & Fiebach, C. J. (2013). Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network. Intelligence, 41(5), 517528.Google Scholar
Bates, T. C., & Gignac, G. E. (2022). Effort impacts IQ test scores in a minor way: A multi-study investigation with healthy adult volunteers. Intelligence, 92, 101652. doi:10.1016/j.intell.2022.101652Google Scholar
Bates, T. C., Lewis, G. J., & Weiss, A. (2013). Childhood socioeconomic status amplifies genetic effects on adult intelligence. Psychological Science, 24(10), 21112116.Google Scholar
Batty, G. D., Deary, I. J., & Gottfredson, L. S. (2007). Premorbid (early life) IQ and later mortality risk: Systematic review. Annals of Epidemiology, 17(4), 278288.Google Scholar
Beaty, R. E. (2015). The neuroscience of musical improvisation. Neuroscience and Biobehavioral Epigraph, 51, 108117. doi:10.1016/j.neubiorev.2015.01.004Google Scholar
Beaujean, A. A., Firmin, M. W., Knoop, A. J., Michonski, J. D., Berry, T. P., & Lowrie, R. E. (2006). Validation of the Frey and Detterman (2004) IQ prediction equations using the Reynolds Intellectual Assessment Scales. Personality and Individual Differences, 41(2), 353357. doi:10.1016/j.paid.2006.01.014Google Scholar
Bejjanki, V. R., Zhang, R., Li, R., Pouget, A., Green, C. S., Lu, Z. L., & Bavelier, D. (2014). Action video game play facilitates the development of better perceptual templates. Proceedings of the National Academy Science USA, 111(47), 16961169610. doi:10.1073/pnas.1417056111Google Scholar
Bengtsson, S. L., Csikszentmihalyi, M., & Ullen, F. (2007). Cortical regions involved in the generation of musical structures during improvisation in pianists. Journal of Cognition Neuroscience, 19(5), 830842. doi:10.1162/jocn.2007.19.5.830Google Scholar
Benyamin, B., Pourcain, B., Davis, O. S., Davies, G., Hansell, N. K., Brion, M. J., … Visscher, P. M. (2014). Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Molecular Psychiatry, 19(2), 253258. doi:10.1038/mp.2012.184Google Scholar
Berkowitz, A. L., & Ansari, D. (2010). Expertise-related deactivation of the right temporoparietal junction during musical improvisation. Neuroimage, 49(1), 712719. doi:10.1016/j.neuroimage.2009.08.042Google Scholar
Bernstein, B. O., Lubinski, D., & Benbow, C. P. (2019). Psychological constellations assessed at age 13 predict distinct forms of eminence 35 years later. Psychological Science, 30(3), 444454. doi:10.1177/0956797618822524Google Scholar
Bhaduri, A., Sandoval-Espinosa, C., Otero-Garcia, M., Oh, I., Yin, R., Eze, U. C., … Kriegstein, A. R. (2021). An atlas of cortical arealization identifies dynamic molecular signatures. Nature, 598(7879), 200204. doi:10.1038/s41586-021-03910-8Google Scholar
Biazoli, C. E., Jr., Salum, G. A., Pan, P. M., Zugman, A., Amaro, E., Jr., Rohde, L. A., … Sato, J. R. (2017). Commentary: Functional connectome fingerprint: Identifying individuals using patterns of brain connectivity. Frontiers in Human Neuroscience, 11, 47.Google Scholar
Bishop, S. J., Fossella, J., Croucher, C. J., & Duncan, J. (2008). COMT val158met genotype affects recruitment of neural mechanisms supporting fluid intelligence. Cerebral Cortex, 18(9), 21322140. doi:10.1093/cercor/bhm240Google Scholar
Bogg, T., & Lasecki, L. (2015). Reliable gains? Evidence for substantially underpowered designs in studies of working memory training transfer to fluid intelligence. Frontiers in Psychology, 5.Google Scholar
Bohlken, M. M., Brouwer, R. M., Mandl, R. C., van Haren, N. E., Brans, R. G., van Baal, G. C., … Hulshoff Pol, H. E. (2014). Genes contributing to subcortical volumes and intellectual ability implicate the thalamus. Human Brain Mapping, 35(6), 26322642. doi:10.1002/hbm.22356Google Scholar
Boivin, M. J., Giordani, B., Berent, S., Amato, D. A., Lehtinen, S., Koeppe, R. A., … Kuhl, D. E. (1992). Verbal fluency and positron emission tomographic mapping of regional cerebral glucose-metabolism. Cortex, 28(2), 231239.Google Scholar
Bouchard, T. J., Jr. (1998). Genetic and environmental influences on adult intelligence and special mental abilities. Human Biology, 70(2), 257279.Google Scholar
Bouchard, T. J. (2009). Genetic influence on human intelligence (Spearman’s g): How much? Annals of Human Biology, 36(5), 527544.Google Scholar
Bouchard, T. J., Jr., & McGue, M. (1981). Familial studies of intelligence: A review. Science, 212(4498), 10551059. Retrieved from www.ncbi.nlm.nih.gov/pubmed/7195071Google Scholar
Bowren, M., Adolphs, R., Bruss, J., Manzel, K., Corbetta, M., Tranel, D., & Boes, A. D. (2020). Multivariate lesion-behavior mapping of general cognitive ability and its psychometric constituents. The Journal of Neuroscience, 40(46), 8924. doi:10.1523/JNEUROSCI.1415-20.2020CrossRefGoogle ScholarPubMed
Brans, R. G. H., Kahn, R. S., Schnack, H. G., van Baal, G. C. M., Posthuma, D., van Haren, N. E. M., … Pol, H. E. H. (2010). Brain plasticity and intellectual ability are influenced by shared genes. Journal of Neuroscience, 30(16), 55195524.Google Scholar
Brem, A. K., Almquist, J. N., Mansfield, K., Plessow, F., Sella, F., Santarnecchi, E., … Honeywell SHARP Team authors. (2018). Modulating fluid intelligence performance through combined cognitive training and brain stimulation. Neuropsychologia, 118(Pt A), 107114. doi:10.1016/j.neuropsychologia.2018.04.008Google Scholar
Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Leipzig: Barth.Google Scholar
Brody, N. (2003). Construct validation of the Sternberg Triarchic Abilities Test Comment and reanalysis. Intelligence, 31(4), 319329. doi:10.1016/S0160-2896(01)00087-3CrossRefGoogle Scholar
Bruzzone, S. E. P., Lumaca, M., Brattico, E., Vuust, P., Kringelbach, M. L., & Bonetti, L. (2022). Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: A DTI and MEG study. Scientific Reports, 12(1), 4746. doi:10.1038/s41598-022-08521-5Google Scholar
Burgaleta, M., & Colom, R. (2008). Short-term storage and mental speed account for the relationship between working memory and fluid intelligence. Psicothema, 20(4), 780785.Google Scholar
Burgaleta, M., Johnson, W., Waber, D. P., Colom, R., & Karama, S. (2014). Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents. Neuroimage, 84, 810819. doi:10.1016/j.neuroimage.2013.09.038Google Scholar
Burgaleta, M., Macdonald, P. A., Martinez, K., Roman, F. J., Alvarez-Linera, J., Gonzalez, A. R., … Colom, R. (2013). Subcortical regional morphology correlates with fluid and spatial intelligence. Human Brain Mapping. doi:10.1002/hbm.22305Google Scholar
Burgoyne, A. P., & Engle, R. W. (2020). Mitochondrial functioning and its relation to higher-order cognitive processes. Journal of Intelligence, 8(2).CrossRefGoogle ScholarPubMed
Burt, C. (1943). Ability and income. British Journal of Educational Psychology, 13, 8398.Google Scholar
Burt, C. (1955). The evidence for the concept of intelligence. British Journal of Educational Psychology, 25, 158177.Google Scholar
Burt, C. (1966). Genetic determination of differences in intelligence – A study of monozygotic twins reared together and apart. British Journal of Psychology, 57, 137-&.CrossRefGoogle ScholarPubMed
Cabeza, R., & Nyberg, L. (2000). Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12(1), 147.Google Scholar
Cajal, R. S. (1924). Pensamientos Escogidos (Chosen Thoughts). Madrid: Cuadernos Literarios.Google Scholar
Campbell, F. A., Pungello, E., Miller-Johnson, S., Burchinal, M., & Ramey, C. T. (2001). The development of cognitive and academic abilities: Growth curves from an early childhood educational experiment. Developmental Psychology, 37(2), 231242.Google Scholar
Cardoso-Leite, P., & Bavelier, D. (2014). Video game play, attention, and learning: How to shape the development of attention and influence learning? Current Opinion in Neurology, 27(2), 185191. doi:10.1097/WCO.0000000000000077CrossRefGoogle ScholarPubMed
Carl, N. (2018). How stifling debate around race, genes and IQ can do harm. Evolutionary Psychological Science, 4(4), 399407. doi:10.1007/s40806-018-0152-xGoogle Scholar
Cattell, R. B. (1971). Abilities : Their structure, growth, and action. Boston: Houghton Mifflin.Google Scholar
Cattell, R. B. (1987). Intelligence: Its structure, growth, and action. North-Holland: Sole distributors for the U.S.A. and Canada, Elsevier Science Pub. Co.Google Scholar
Ceci, S. J. (1991). How much does schooling influence general intelligence and its cognitive components – A reassessment of the evidence. Developmental Psychology, 27(5), 703722.Google Scholar
Ceci, S. J., & Williams, W. M. (1997). Schooling, intelligence, and income. American Psychologist, 52(10), 10511058.Google Scholar
Chabris, C. F. (1999). Prelude or requiem for the /`Mozart effect/’? Nature, 400(6747), 826827.Google Scholar
Chabris, C. F., Hebert, B. M., Benjamin, D. J., Beauchamp, J., Cesarini, D., van der Loos, M., … Laibson, D. (2012). Most reported genetic associations with general intelligence are probably false positives. Psychological Science, 23(11), 13141323.Google Scholar
Champagne, F. A., & Curley, J. P. (2009). Epigenetic mechanisms mediating the long-term effects of maternal care on development. Neuroscience Biobehavioral Review, 33(4), 593600. doi:10.1016/j.neubiorev.2007.10.009Google Scholar
Cheesman, R., Hunjan, A., Coleman, J. R. I., Ahmadzadeh, Y., Plomin, R., McAdams, T. A., … Breen, G. (2020). Comparison of adopted and nonadopted individuals reveals gene–environment interplay for education in the UK Biobank. Psychological Science, 31(5), 582591. doi:10.1177/0956797620904450Google Scholar
Chen, C.-Y., Tian, R., Ge, T., Lam, M., Sanchez-Andrade, G., Singh, T., … Runz, H. (2022). The impact of rare protein coding genetic variation on adult cognitive function. medRxiv, 2022.2006.2024.22276728. doi:10.1101/2022.06.24.22276728Google Scholar
Chiang, M. C., Barysheva, M., McMahon, K. L., de Zubicaray, G. I., Johnson, K., Montgomery, G. W., … Thompson, P. M. (2012). Gene network effects on brain microstructure and intellectual performance identified in 472 twins. Journal of Neuroscience, 32(25), 87328745. doi:10.1523/Jneurosci.5993-11.2012Google Scholar
Chiang, M. C., Barysheva, M., Shattuck, D. W., Lee, A. D., Madsen, S. K., Avedissian, C., … Thompson, P. M. (2009). Genetics of brain fiber architecture and intellectual performance. Journal of Neuroscience, 29(7), 22122224. doi:10.1523/Jneurosci.4184-08.2009Google Scholar
Chiang, M. C., Barysheva, M., Toga, A. W., Medland, S. E., Hansell, N. K., James, M. R., … Thompson, P. M. (2011a). BDNF gene effects on brain circuitry replicated in 455 twins. Neuroimage, 55(2), 448454. doi:10.1016/j.neuroimage.2010.12.053Google Scholar
Chiang, M. C., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Hickie, I., Toga, A. W., … Thompson, P. M. (2011b). Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29. Neuroimage, 54(3), 23082317. doi:10.1016/j.neuroimage.2010.10.015Google Scholar
Choi, Y. Y., Shamosh, N. A., Cho, S. H., DeYoung, C. G., Lee, M. J., Lee, J. M., … Lee, K. H. (2008). Multiple bases of human intelligence revealed by cortical thickness and neural activation. Journal of Neuroscience, 28(41), 10323103210. doi:10.1523/JNEUROSCI.3259-08.2008Google Scholar
Chollet, F. (2019). On the measure of intelligence. arXiv preprint arXiv: 1911.01547.Google Scholar
Chooi, W. T., & Thompson, L. A. (2012). Working memory training does not improve intelligence in healthy young adults. Intelligence, 40(6), 531542.Google Scholar
Chugani, H. T., Phelps, M. E., & Mazziotta, J. C. (1987). Positron emission tomography study of human brain functional development. Annals of Neurology, 22(4), 487497.Google Scholar
Clark, V. P., Coffman, B. A., Mayer, A. R., Weisend, M. P., Lane, T. D., Calhoun, V. D., … Wassermann, E. M. (2012). TDCS guided using fMRI significantly accelerates learning to identify concealed objects. Neuroimage, 59(1), 117128. doi:10.1016/j.neuroimage.2010.11.036Google Scholar
Coffman, B. A., Clark, V. P., & Parasuraman, R. (2014). Battery powered thought: Enhancement of attention, learning, and memory in healthy adults using transcranial direct current stimulation. Neuroimage, 85(Pt 3), 895908. doi:10.1016/j.neuroimage.2013.07.083Google Scholar
Cofnas, N. (2020). Research on group differences in intelligence: A defense of free inquiry. Philosophical Psychology, 33(1), 125147. doi:10.1080/09515089.2019.1697803Google Scholar
Cohen, J. R., & D’Esposito, M. (2021). An integrated, dynamic functional connectome undies intelligence. In Barbey, A., Karama, S., & Haier, R. (Eds.), Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 261281). New York: Cambridge University Press.Google Scholar
Cole, M. W., Yarkoni, T., Repovs, G., Anticevic, A., & Braver, T. S. (2012). Global connectivity of prefrontal cortex predicts cognitive control and intelligence. Journal of Neuroscience, 32(26), 89888999. doi:10.1523/JNEUROSCI.0536-12.2012Google Scholar
Colom, R., Abad, F. J., Quiroga, M. A., Shih, P. C., & Flores-Mendoza, C. (2008). Working memory and intelligence are highly related constructs, but why? Intelligence, 36(6), 584606.Google Scholar
Colom, R., & Flores-Mendoza, C. E. (2007). Intelligence predicts scholastic achievement irrespective of SES factors: Evidence from Brazil. Intelligence, 35(3), 243251.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006a). Distributed brain sites for the g-factor of intelligence. Neuroimage, 31(3), 13591365.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2006b). Finding the g-factor in brain structure using the method of correlated vectors. Intelligence, 34(6), 561.Google Scholar
Colom, R., Jung, R. E., & Haier, R. J. (2007). General intelligence and memory span: Evidence for a common neuroanatomic framework. Cognitive Neuropsychology, 24(8), 867878.Google Scholar
Colom, R., Karama, S., Jung, R. E., & Haier, R. J. (2010). Human intelligence and brain networks. Dialogues in Clinical Neuroscience, 12(4), 489501. Retrieved from www.ncbi.nlm.nih.gov/pubmed/21319494Google 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
Colom, R., Roman, F. J., Abad, F. J., Shih, P. C., Privado, J., Froufe, M., … Jaeggi, S. M. (2013). Adaptive n-back training does not improve fluid intelligence at the construct level: Gains on individual tests suggest that training may enhance visuospatial processing. Intelligence, 41(5), 712727.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. doi:10.1016/J.Tics.2003.10.005Google Scholar
Covington, H. E., 3rd, Lobo, M. K., Maze, I., Vialou, V., Hyman, J. M., Zaman, S., … Nestler, E. J. (2010). Antidepressant effect of optogenetic stimulation of the medial prefrontal cortex. Journal of Neuroscience, 30(48), 1608216090. doi:10.1523/JNEUROSCI.1731-10.2010Google Scholar
Cowan, N. (2014). Working memory underpins cognitive development, learning, and education. Educational Psychology Review, 26(2), 197223. doi:10.1007/s10648-013-9246-yGoogle Scholar
Coyle, T. R. (2015). Relations among general intelligence (g), aptitude tests, and GPA: Linear effects dominate. Intelligence, 53, 1622. doi:10.1016/j.intell.2015.08.005Google Scholar
Coyle, T. R. (2021). Defining and measuring intelligence: The psychometrics and neuroscience of g. In Barbey, A., Karama, S., & Haier, R. (Eds.), Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 325). New York: Cambridge University Press.Google Scholar
Crick, F. (1994). The Astonishing Hypothesis : The Scientific Search for the Soul. New York, Scribner : Maxwell Macmillan International.Google Scholar
Curlik, D. M., 2nd, & Shors, T. J. (2013). Training your brain: Do mental and physical (MAP) training enhance cognition through the process of neurogenesis in the hippocampus? Neuropharmacology, 64, 506514. doi:10.1016/j.neuropharm.2012.07.027Google Scholar
Curlik, D. M., 2nd, Maeng, L. Y., Agarwal, P. R., & Shors, T. J. (2013). Physical skill training increases the number of surviving new cells in the adult hippocampus. Plos One, 8(2), e55850. doi:10.1371/journal.pone.0055850Google Scholar
Davies, G., Armstrong, N., Bis, J. C., Bressler, J., Chouraki, V., Giddaluru, S., … Deary, I. J. (2015). Genetic contributions to variation in general cognitive function: A meta-analysis of genome-wide association studies in the CHARGE consortium (N=53949). Molecular Psychiatry, 20(2), 183192. doi:10.1038/mp.2014.188Google Scholar
Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., … Deary, I. J. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry, 16(10), 9961005.Google Scholar
Davis, J. M., Searles, V. B., Anderson, N., Keeney, J., Raznahan, A., Horwood, L. J., … Sikela, J. M. (2015). DUF1220 copy number is linearly associated with increased cognitive function as measured by total IQ and mathematical aptitude scores. Human Genetics, 134(1), 6775. doi:10.1007/s00439-014-1489-2Google Scholar
Dawkins, R. (2016). The Selfish Gene : 40th Anniversary Edition. New York: Oxford University Press.Google Scholar
Deary, I. J. (2000). Looking Down on Human Intelligence: From Psychometrics to the Brain. Oxford; New York: Oxford University Press.Google Scholar
Deary, I. J., Cox, S. R., & Hill, W. D. (2022). Genetic variation, brain, and intelligence differences. Molecular Psychiatry, 27(1), 335353. doi:10.1038/s41380-021-01027-yGoogle Scholar
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201211. doi:10.1038/Nrn2793Google Scholar
Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J., & Fox, H. C. (2004). The impact of childhood intelligence on later life: Following up the Scottish Mental Surveys of 1932 and 1947. Journal of Personality and Social Psychology, 86(1), 130147.Google Scholar
Del Río, D., Cuesta, P., Bajo, R., García-Pacios, J., López-Higes, R., del-Pozo, F., & Maestú, F. (2012). Efficiency at rest: Magnetoencephalographic resting-state connectivity and individual differences in verbal working memory. International Journal of Psychophysiology, 86(2), 160167.Google Scholar
Demetriou, A., Golino, H., Spanoudis, G., Makris, N., & Greiff, S. (2021). The future of intelligence: The central meaning-making unit of intelligence in the mind, the brain, and artificial intelligence. Intelligence, 87, 101562. doi:10.1016/j.intell.2021.101562Google Scholar
Der, G., Batty, G. D., & Deary, I. J. (2006). Effect of breast feeding on intelligence in children: Prospective study, sibling pairs analysis, and meta-analysis. BMJ, 333(7575), 945. doi:10.1136/bmj.38978.699583.55Google Scholar
Desrivieres, S., Lourdusamy, A., Tao, C., Toro, R., Jia, T., Loth, E., … Consortium, I. (2015). Single nucleotide polymorphism in the neuroplastin locus associates with cortical thickness and intellectual ability in adolescents. Molecular Psychiatry, 20(2), 263274. doi:10.1038/mp.2013.197Google Scholar
Detterman, D. K. (1998). Kings of men: Introduction to a special issue. Intelligence, 26(3), 175180. doi:10.1016/S0160-2896(99)80001-4Google Scholar
Detterman, D. K. (2014). Introduction to the intelligence special issue on the development of expertise: Is ability necessary? Intelligence, 45, 15.Google Scholar
Detterman, D. K. (2016). Education and intelligence: Pity the poor teacher because student characteristics are more significant than teachers or schools. Spanish Journal of Psychology, 19. doi:10.1017/sjp.2016.88Google Scholar
Dietrich, A., & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging studies of creativity and insight. Psychological Bulletin, 136(5), 822848. doi:10.1037/a0019749Google Scholar
Dietz, P., Soyka, M., & Franke, A. G. (2016). Pharmacological neuroenhancement in the field of economics-poll results from an online survey. Frontiers in Psychology, 7.Google Scholar
Dietz, P., Striegel, H., Franke, A. G., Lieb, K., Simon, P., & Ulrich, R. (2013). Randomized response estimates for the 12-month prevalence of cognitive-enhancing drug use in University Students. Pharmacotherapy, 33(1), 4450.Google Scholar
Donnay, G. F., Rankin, S. K., Lopez-Gonzalez, M., Jiradejvong, P., & Limb, C. J. (2014). Neural substrates of interactive musical improvisation: An FMRI study of ‘trading fours’ in jazz. Plos One, 9(2), e88665. doi:10.1371/journal.pone.0088665Google Scholar
Douw, L., Nissen, I. A., Fitzsimmons, S. M. D. D., Santos, F. A. N., Hillebrand, A., van Straaten, E. C. W., … Goriounova, N. A. (2021). Cellular substrates of functional network integration and memory in temporal lobe epilepsy. Cerebral Cortex, 32(11), 24242436. doi:10.1093/cercor/bhab349Google Scholar
Drakulich, S., & Karama, S. (2021). Structural brain imaging of intelligence. In Barbey, A., Karama, S., & Haier, R. (Eds.), Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 210234). New York: Cambridge University Press.Google Scholar
Drakulich, S., Sitartchouk, A., Olafson, E., Sarhani, R., Thiffault, A.-C., Chakravarty, M., … Karama, S. (2022). General cognitive ability and pericortical contrast. Intelligence, 91, 101633. doi:10.1016/j.intell.2022.101633Google Scholar
Dresler, M., Sandberg, A., Bublitz, C., Ohla, K., Trenado, C., Mroczko-Wąsowicz, A., … Repantis, D. (2019). Hacking the brain: Dimensions of cognitive enhancement. ACS Chemical Neuroscience, 10(3), 11371148. doi:10.1021/acschemneuro.8b00571Google Scholar
Dreszer, J., Grochowski, M., Lewandowska, M., Nikadon, J., Gorgol, J., Balaj, B., … Piotrowski, T. (2020). Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters. Human Brain Mapping, 41(17), 48464865. doi:10.1002/hbm.25162Google Scholar
Drury, S. S., Theall, K., Gleason, M. M., Smyke, A. T., De Vivo, I., Wong, J. Y., … Nelson, C. A. (2012). Telomere length and early severe social deprivation: linking early adversity and cellular aging. Molecular Psychiatry, 17(7), 719727. doi:10.1038/mp.2011.53Google Scholar
Dubois, J., Galdi, P., Paul, L. K., & Adolphs, R. (2018). A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philosophical Transactions on Royal Society London B Biological Sciences, 373(1756). doi:10.1098/rstb.2017.0284Google Scholar
Duncan, G. J., & Sojourner, A. J. (2013). Can intensive early childhood intervention programs eliminate income-based cognitive and achievement gaps? Journal of Human Resources, 48(4), 945968.Google Scholar
Duncan, J. (2010). The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognition Science, 14(4), 172179. doi:10.1016/j.tics.2010.01.004Google Scholar
Duncan, J., Burgess, P., & Emslie, H. (1995). Fluid intelligence after frontal lobe lesions. Neuropsychologia, 33(3), 261268.Google Scholar
Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A., … Emslie, H. (2000). A neural basis for general intelligence. Science, 289(5478), 457460.Google Scholar
Durkin, K., Lipsey, M. W., Farran, D. C., & Wiesen, S. E. (2022). Effects of a statewide pre-kindergarten program on children’s achievement and behavior through sixth grade. Developmental Psychology. doi:10.1037/dev0001301Google Scholar
Editorial. (2017). Intelligence research should not be held back by its past. Nature, 545, 385386.Google Scholar
Ericsson, K. A. (2014). Why expert performance is special and cannot be extrapolated from studies of performance in the general population: A response to criticisms. Intelligence, 45, 81103.Google Scholar
Ericsson, K. A., & Towne, T. J. (2010). Expertise. Wiley Interdisciplinary Reviews-Cognitive Science, 1(3), 404416Google Scholar
Esposito, G., Kirkby, B. S., Van Horn, J. D., Ellmore, T. M., & Berman, K. F. (1999). Context-dependent, neural system-specific neurophysiological concomitants of ageing: Mapping PET correlates during cognitive activation. Brain, 122, 963979.Google Scholar
Estrada, E., Ferrer, E., Román, F. J., Karama, S., & Colom, R. (2019). Time-lagged associations between cognitive and cortical development from childhood to early adulthood. Developmental Psychology, 55(6), 13381352.Google Scholar
Euler, M. J., & McKinney, T. L. (2021). Evaluating the weight of the evidence: Cognitive neuroscience theories of intelligence. In Barbey, A., Karama, S., & Haier, R. J. (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 85–101). New York: Cambridge University Press.Google Scholar
Euler, M. J., McKinney, T. L., Schryver, H. M., & Okabe, H. (2017). ERP correlates of the decision time-IQ relationship: The role of complexity in task- and brain-IQ effects. Intelligence, 65, 110. doi:10.1016/j.intell.2017.08.003Google Scholar
Euler, M. J., Weisend, M. P., Jung, R. E., Thoma, R. J., & Yeo, R. A. (2015). Reliable activation to novel stimuli predicts higher fluid intelligence. Neuroimage, 114, 311319. doi:10.1016/j.neuroimage.2015.03.078Google Scholar
Ezkurdia, I., Juan, D., Rodriguez, J. M., Frankish, A., Diekhans, M., Harrow, J., … Tress, M. L. (2014). Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Human Molecular Genetics, 23(22), 58665878.Google Scholar
Falk, D., Lepore, F. E., & Noe, A. (2013). The cerebral cortex of Albert Einstein: A description and preliminary analysis of unpublished photographs. Brain, 136(Pt 4), 13041327. doi:10.1093/brain/aws295Google Scholar
Fangmeier, T., Knauff, M., Ruff, C. C., & Sloutsky, V. M. (2006). fMRI evidence for a three-stage model of deductive reasoning. Journal of Cognitive Neuroscience, 18(3), 320334.Google Scholar
Farah, M. J., Betancourt, L., Shera, D. M., Savage, J. H., Giannetta, J. M., Brodsky, N. L., … Hurt, H. (2008). Environmental stimulation, parental nurturance and cognitive development in humans. Developmental Science, 11(5), 793801. Retrieved from www.ncbi.nlm.nih.gov/pubmed/18810850Google Scholar
Farah, M. J., Shera, D. M., Savage, J. H., Betancourt, L., Giannetta, J. M., Brodsky, N. L., … Hurt, H. (2006). Childhood poverty: Specific associations with neurocognitive development. Brain Research, 1110(1), 166174. doi:10.1016/j.brainres.2006.06.072Google Scholar
Farah, M. J., Smith, M. E., Ilieva, I., & Hamilton, R. H. (2014). Cognitive enhancement. Wiley Interdisciplinary Reviews-Cognitive Science, 5(1), 95103.Google Scholar
Feilong, M., Guntupalli, J. S., & Haxby, J. V. (2021). The neural basis of intelligence in fine-grained cortical topographies. Elife, 10, e64058. doi:10.7554/eLife.64058Google Scholar
Ferrer, E., O’Hare, E. D., & Bunge, S. A. (2009). Fluid reasoning and the developing brain. Frontiers in Neuroscience, 3(1), 4651. doi:10.3389/neuro.01.003.2009Google Scholar
Ferrero, M., Vadillo, M. A., & León, S. P. (2021). A valid evaluation of the theory of multiple intelligences is not yet possible: Problems of methodological quality for intervention studies. Intelligence, 88, 101566. doi:10.1016/j.intell.2021.101566Google Scholar
Fink, A., Grabner, R. H., Gebauer, D., Reishofer, G., Koschutnig, K., & Ebner, F. (2010). Enhancing creativity by means of cognitive stimulation: Evidence from an fMRI study. Neuroimage, 52(4), 16871695. doi:10.1016/j.neuroimage.2010.05.072Google Scholar
Finn, E. S., & Rosenberg, M. D. (2021). Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes. Neuroimage, 239, 118254. doi:10.1016/j.neuroimage.2021.118254Google Scholar
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., … Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Natural Neuroscience, 18(11), 16641671. doi:10.1038/nn.4135Google Scholar
Firkowska, A., Ostrowska, A., Sokolowska, M., Stein, Z., Susser, M., & Wald, I. (1978). Cognitive-development and social-policy. Science, 200(4348), 13571362.Google Scholar
Flashman, L. A., Andreasen, N. C., Flaum, M., & Swayze, V. W. (1997). Intelligence and regional brain volumes in normal controls. Intelligence, 25(3), 149160.Google Scholar
Flynn, J. R. (2013). The “Flynn Effect” and Flynn’s paradox. Intelligence, 41(6), 851857. doi:10.1016/J.Intell.2013.06.014Google Scholar
Fox, K. C. R., Girn, M., Parro, C. C., & Christoff, K. (2018). Functional neuroimaging of psychedelic experience: An overview of psychological and neural effects and their relevance to research on creativity, daydreaming, and dreaming. In Jung, R.E. & Vartanian, O. (Eds.), The Cambridge Handbook of the Neuroscience of Creativity (pp. 92113): Cambridge University Press.Google Scholar
Fraenz, C., Schlüter, C., Friedrich, P., Jung, R. E., Güntürkün, O., & Genç, E. (2021). Interindividual differences in matrix reasoning are linked to functional connectivity between brain regions nominated by Parieto-Frontal Integration Theory. Intelligence, 87, 101545. doi:10.1016/j.intell.2021.101545Google Scholar
Frangou, S., Chitins, X., & Williams, S. C. R. (2004). Mapping IQ and gray matter density in healthy young people. Neuroimage, 23(3), 800805.Google Scholar
Franke, A. G., Bagusat, C., Rust, S., Engel, A., & Lieb, K. (2014). Substances used and prevalence rates of pharmacological cognitive enhancement among healthy subjects. European Archives of Psychiatry and Clinical Neuroscience, 264, S83-S90. doi:10.1007/s00406-014-0537-1Google Scholar
Fregnac, Y., & Laurent, G. (2014). Neuroscience: Where is the brain in the human brain project? Nature, 513(7516), 2729. doi:10.1038/513027aGoogle Scholar
Frey, M. C., & Detterman, D. K. (2004). Scholastic assessment or g? The relationship between the scholastic assessment test and general cognitive ability (vol 15, p. 373, 2004). Psychological Science, 15(9), 641641.Google Scholar
Frischkorn, G. T., Schubert, A. L., & Hagemann, D. (2019). Processing speed, working memory, and executive functions: Independent or inter-related predictors of general intelligence. Intelligence, 75, 95110. doi:10.1016/j.intell.2019.05.003Google Scholar
Galaburda, A. M. (1999). Albert Einstein’s brain. Lancet, 354(9192), 1821; author reply 1822. Retrieved from www.ncbi.nlm.nih.gov/pubmed/10577668Google Scholar
Galton, F. (1869). Hereditary Genius: An Inquiry into Its Laws and Consequences. London: Macmillan.Google Scholar
Galton, F. (2006). Hereditary Genius: An Inquiry into Its Laws and Consequences. Amherst, NY: Prometheus Books.Google Scholar
Galton, F., & Prinzmetal, M. (1884). Hereditary Genius : An Inquiry into its Laws and Consequences (New and revised edition, with an American preface. ed.). New York: D. Appleton and Company …Google Scholar
Gardner, H. (1987). The theory of multiple intelligences. Annals of Dyslexia, 37, 1935.Google Scholar
Gardner, H., & Moran, S. (2006). The science of multiple intelligences theory: A response to Lynn Waterhouse. Educational Psychologist, 41(4), 227232. doi:10.1207/s15326985ep4104_2Google Scholar
Geake, J. (2008). Neuromythologies in education. Educational Research, 50(2), 123133.Google Scholar
Geake, J. (2011). Position statement on motivations, methodologies, and practical implications of educational neuroscience research: fMRI studies of the neural correlates of creative intelligence. Educational Philosophy and Theory, 43(1), 4347.Google Scholar
Geake, J. G., & Hansen, P. C. (2005). Neural correlates of intelligence as revealed by fMRI of fluid analogies. Neuroimage, 26(2), 555564.Google Scholar
Geary, D. C. (2018). Efficiency of mitochondrial functioning as the fundamental biological mechanism of general intelligence (g). Psychological Review, 125(6), 10281050.Google Scholar
Geary, D. C. (2019). Mitochondria as the Linchpin of general intelligence and the link between g, health, and aging. Journal of Intelligence, 7(4).Google Scholar
Genc, E., & Fraenz, C. (2021). Diffusion-weighted imaging of intelligence. In Barbey, A., Karama, S., & Haier, R. (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 191209). New York: Cambridge University Press.Google Scholar
Genc, E., Fraenz, C., Schluter, C., Friedrich, P., Hossiep, R., Voelkle, M. C., … Jung, R. E. (2018). Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature Communication, 9(1), 1905. doi:10.1038/s41467-018-04268-8Google Scholar
Genc, E., Schluter, C., Fraenz, C., Arning, L., Metzen, D., Nguyen, H. P., … Ocklenburg, S. (2021). Polygenic scores for cognitive abilities and their association with different aspects of general intelligence: A deep phenotyping approach. Molecular Neurobiology, 58(8), 41454156.Google Scholar
George, M. S., Nahas, Z., Molloy, M., Speer, A. M., Oliver, N. C., Li, X. B., … Ballenger, J. C. (2000). A controlled trial of daily left prefrontal cortex TMS for treating depression. Biological Psychiatry, 48(10), 962970. doi:10.1016/s0006-3223(00)01048-9Google Scholar
Ghatan, P. H., Hsieh, J. C., Wirsenmeurling, A., Wredling, R., Eriksson, L., Stoneelander, S., … Ingvar, M. (1995). Brain activation-induced by the perceptual Maze Test – A Pet Study of Cognitive Performance. Neuroimage, 2(2), 112124.Google Scholar
Gignac, G. E. (2015). Raven’s is not a pure measure of general intelligence: Implications for g factor theory and the brief measurement of g. Intelligence, 52, 7179. doi:10.1016/j.intell.2015.07.006Google Scholar
Girn, M., Mills, C., & Christoff, K. (2019). Linking brain network reconfiguration and intelligence: Are we there yet? Trends in Neuroscience and Education, 15, 6270. doi:10.1016/j.tine.2019.04.001Google Scholar
Glascher, J., Rudrauf, D., Colom, R., Paul, L. K., Tranel, D., Damasio, H., & Adolphs, R. (2010). Distributed neural system for general intelligence revealed by lesion mapping. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 47054709. doi:10.1073/Pnas.0910397107Google Scholar
Glascher, J., Tranel, D., Paul, L. K., Rudrauf, D., Rorden, C., Hornaday, A., … Adolphs, R. (2009). Lesion mapping of cognitive abilities linked to intelligence. Neuron, 61(5), 681691. doi:10.1016/j.neuron.2009.01.026Google Scholar
Gobet, F., & Sala, G. (2022). Cognitive training: A field in search of a phenomenon. Perspectives on Psychological Science, 17456916221091830. doi:10.1177/17456916221091830Google Scholar
Goel, V., & Dolan, R. J. (2004). Differential involvement of left prefrontal cortex in inductive and deductive reasoning. Cognition, 93(3), B109B121.Google Scholar
Goel, V., Gold, B., Kapur, S., & Houle, S. (1997). The seats of reason? An imaging study of deductive and inductive reasoning. Neuroreport, 8(5), 13051310.Google Scholar
Goel, V., Gold, B., Kapur, S., & Houle, S. (1998). Neuroanatomical correlates of human reasoning. Journal of Cognitive Neuroscience, 10(3), 293302.Google Scholar
Gonen-Yaacovi, G., de Souza, L. C., Levy, R., Urbanski, M., Josse, G., & Volle, E. (2013). Rostral and caudal prefrontal contribution to creativity: A meta-analysis of functional imaging data. Frontiers in Human Neuroscience, 7, 465. doi:10.3389/fnhum.2013.00465Google Scholar
Gong, Q.-Y., Sluming, V., Mayes, A., Keller, S., Barrick, T., Cezayirli, E., & Roberts, N. (2005). Voxel-based morphometry and stereology provide convergent evidence of the importance of medial prefrontal cortex for fluid intelligence in healthy adults. Neuroimage, 25(4), 1175.Google Scholar
Gonzalez-Lima, F., & Barrett, D. W. (2014). Augmentation of cognitive brain functions with transcranial lasers. Frontiers in Systematic Neuroscience, 8, 36. doi:10.3389/fnsys.2014.00036Google Scholar
Gordon, R. A. (1997). Everyday life as an intelligence test: Effects of intelligence and intelligence context. Intelligence, 24(1), 203320. doi:10.1016/S0160-2896(97)90017-9Google Scholar
Goriounova, N. A., & Mansvelder, H. D. (2019). Genes, Cells and Brain Areas of Intelligence. Frontiers in Human Neuroscience, 13, 44. doi:10.3389/fnhum.2019.00044Google Scholar
Goriounova, N. A., Heyer, D. B., Wilbers, R., Verhoog, M. B., Giugliano, M., Verbist, C., … Mansvelder, H. D. (2018). Large and fast human pyramidal neurons associate with intelligence. Elife, 7.Google Scholar
Gottfredson, L. S. (1997a). Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography (Reprinted from The Wall Street Journal, 1994). Intelligence, 24(1), 1323.Google Scholar
Gottfredson, L. S. (1997b). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79132.Google Scholar
Gottfredson, L. S. (2002). Where and why g matters: Not a mystery. Human Performance, 15(1/2).Google Scholar
Gottfredson, L. S. (2003a). Dissecting practical intelligence theory: Its claims and evidence. Intelligence, 31(4), 343397.Google Scholar
Gottfredson, L. S. (2003b). g, jobs and life. In Nyborg, H. (Ed.), The Scientific Study of General Intelligence (pp. 293342). New York: Elsevier Science.Google Scholar
Gottfredson, L. S. (2005). Suppressing intelligence research: Hurting those we intend to help. In Wright, R.H. and Cummings, N.A. (Ed.), Destructive Trends in Mental Health: The Well-intentioned Path to Harm (pp. 155186). New York: Routledge.Google Scholar
Gozli, D. G., Bavelier, D., & Pratt, J. (2014). The effect of action video game playing on sensorimotor learning: Evidence from a movement tracking task. Human Movement Science, 38C, 152162. doi:10.1016/j.humov.2014.09.004Google Scholar
Grabner, R. H. (2014). The role of intelligence for performance in the prototypical expertise domain of chess. Intelligence, 45, 2633.Google Scholar
Grabner, R. H., Stern, E., & Neubauer, A. C. (2007). Individual differences in chess expertise: A psychometric investigation. Acta Psychologica, 124(3), 398420.Google Scholar
Graham, S., Jiang, J., Manning, V., Nejad, A. B., Zhisheng, K., Salleh, S. R., … McKenna, P. J. (2010). IQ-related fMRI differences during cognitive set shifting. Cerebral Cortex, 20(3), 641649. doi:10.1093/cercor/bhp130Google Scholar
Gray, J. R., Chabris, C. F., & Braver, T. S. (2003). Neural mechanisms of general fluid intelligence. Nature Neuroscience, 6(3), 316322.Google Scholar
Greely, H., Sahakian, B., Harris, J., Kessler, R. C., Gazzaniga, M., Campbell, P., & Farah, M. J. (2008). Towards responsible use of cognitive-enhancing drugs by the healthy. Nature, 456(7223), 702705. doi:10.1038/456702aGoogle Scholar
Green, A. E., Kraemer, D. J., Fugelsang, J. A., Gray, J. R., & Dunbar, K. N. (2012). Neural correlates of creativity in analogical reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(2), 264272. doi:10.1037/a0025764Google Scholar
Gregory, H. (2015). McNamara’s Folly: The Use of Low IQ Troops in the Vietnam War: West Conshohocken: Infinity.Google Scholar
Gullich, A., Macnamara, B. N., & Hambrick, D. Z. (2022). What makes a champion? Early multidisciplinary practice, not early specialization, predicts world-class performance. Perspectives on Psychological Science, 17(1), 629. doi:10.1177/1745691620974772Google Scholar
Gur, R. C., Butler, E. R., Moore, T. M., Rosen, A. F. G., Ruparel, K., Satterthwaite, T. D., … Gur, R. E. (2021). Structural and functional brain parameters related to cognitive performance across development: Replication and extension of the parieto-frontal integration theory in a single sample. Cerebral Cortex, 31(3), 14441463. doi:10.1093/cercor/bhaa282Google Scholar
Gur, R. C., Ragland, J. D., Resnick, S. M., Skolnick, B. E., Jaggi, J., Muenz, L., & Gur, R. E. (1994). Lateralized increases in cerebral blood-flow during performance of verbal and spatial tasks – Relationship with performance-level. Brain and Cognition, 24(2), 244258.Google Scholar
Haasz, J., Westlye, E. T., Fjaer, S., Espeseth, T., Lundervold, A., & Lundervold, A. J. (2013). General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults. Neuroimage, 83, 372383. doi:10.1016/j.neuroimage.2013.06.040Google Scholar
Hackman, D. A., Farah, M. J., & Meaney, M. J. (2010). Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Review Neuroscience, 11(9), 651659. doi:10.1038/nrn2897Google Scholar
Haggarty, P., Hoad, G., Harris, S. E., Starr, J. M., Fox, H. C., Deary, I. J., & Whalley, L. J. (2010). Human intelligence and polymorphisms in the DNA methyltransferase genes involved in epigenetic marking. Plos One, 5(6), e11329. doi:10.1371/journal.pone.0011329Google Scholar
Haier, R. J. (1990). The end of intelligence research. Intelligence, 14(4), 371374.Google Scholar
Haier, R. J. (2009a). Neuro-intelligence, neuro-metrics and the next phase of brain imaging studies. Intelligence, 37(2), 121123.Google Scholar
Haier, R. J. (2009b). What does a smart brain look like? Scientific American Mind, November/December, 26–33.Google Scholar
Haier, R. J. (Producer). (2013). The intelligent brain. [lecture course] Retrieved from www.thegreatcourses.com/courses/the-intelligent-brainGoogle Scholar
Haier, R. J. (2014). Increased intelligence is a myth (so far). Frontiers in Systematic Neuroscience, 8, 34. doi:10.3389/fnsys.2014.00034Google Scholar
Haier, R. J. (2021). Are we thinking big enough about the road ahead? Overview of the special issue on the future of intelligence research. Intelligence, 89, 101603. doi:10.1016/j.intell.2021.101603Google Scholar
Haier, R. J., & Benbow, C. P. (1995). Sex differences and lateralization in temporal lobe glucose metabolism during mathematical reasoning. Developmental Neuropsychology, 11(4), 405414.Google Scholar
Haier, R. J., & Colom, R. (2023). The Science of Human Intelligence. Oxford: Cambridge University Press.Google Scholar
Haier, R. J., & Jung, R. E. (2007). Beautiful minds (i.e., brains) and the neural basis of intelligence. Behavioral and Brain Sciences, 30(02), 174178.Google Scholar
Haier, R. J., & Jung, R. E. (2008). Brain imaging studies of intelligence and creativity – What is the picture for education?. Roeper Review, 30(3), 171180.Google Scholar
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2004). Structural brain variation and general intelligence. Neuroimage, 23(1), 425433.Google Scholar
Haier, R. J., Jung, R. E., Yeo, R. A., Head, K., & Alkire, M. T. (2005). The neuroanatomy of general intelligence: Sex matters. Neuroimage, 25(1), 320327.Google Scholar
Haier, R. J., Robinson, D. L., Braden, W., & Williams, D. (1983). Electrical potentials of the cerebral cortex and psychometric intelligence. Personality & Individual Differences, 4(6), 591599.Google Scholar
Haier, R. J., Siegel, B. V.Crinella, , F. M., & Buchsbaum, M. S. (1993). Biological and psychometric intelligence: Testing an animal model in humans with positron emission tomography. In Detterman, Douglas K. (Ed.), Individual Differences and Cognition (pp. 317331). Norwood: Ablex Publishing Corp.Google Scholar
Haier, R. J., Siegel, B. V.MacLachlan, , A., Soderling, E., Lottenberg, S., & Buchsbaum, M. S. (1992a). Regional glucose metabolic changes after learning a complex visuospatial/motor task: A positron emission tomographic study. Brain Research, 570(1–2), 134143.Google Scholar
Haier, R. J., Siegel, B. V., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., … Buchsbaum, M. S. (1988). Cortical glucose metabolic-rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199217.Google Scholar
Haier, R. J., Siegel, B., Tang, C., Abel, L., & Buchsbaum, M. S. (1992b). Intelligence and changes in regional cerebral glucose metabolic-rate following learning. Intelligence, 16(3–4), 415426.Google Scholar
Haier, R. J., White, N. S., & Alkire, M. T. (2003). Individual differences in general intelligence correlate with brain function during nonreasoning tasks. Intelligence, 31(5), 429441. (2007).Google Scholar
Halpern, Diane F., Camilla, P. Benbow, David, C. Geary, Ruben, C. Gur, Janet, Shibley Hyde, and Morton, Ann Gernsbacher. The science of sex differences in science and mathematics. Psychological Science in the Public Interest, 8(1), 151. doi:10.1111/j.1529-1006.2007.00032.xGoogle Scholar
Halstead, W. C. (1947). Brain and Intelligence; A Quantitative Study of the Frontal Lobes. Chicago: University of Chicago Press.Google Scholar
Hambrick, D. Z., Macnamara, B. N., & Oswald, F. L. (2020). Is the deliberate practice view defensible? A review of evidence and discussion of issues. Frontiers in Psychology, 11, 1134. doi:10.3389/fpsyg.2020.01134Google Scholar
Hampshire, A., Thompson, R., Duncan, J., & Owen, A. M. (2011). Lateral prefrontal cortex subregions make dissociable contributions during fluid reasoning. Cerebral Cortex, 21(1), 110. doi:10.1093/cercor/bhq085Google Scholar
Hanscombe, K. B., Trzaskowski, M., Haworth, C. M. A., Davis, O. S. P., Dale, P. S., & Plomin, R. (2012). Socioeconomic Status (SES) and Children’s Intelligence (IQ): In a UK-Representative sample SES moderates the environmental, not genetic, effect on IQ. Plos One, 7(2).Google Scholar
Hansen, J. Y., Shafiei, G., Markello, R. D., Smart, K., Cox, S. M. L., Wu, Y., … Misic, B. (2021). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. bioRxiv, 2021.2010.2028.466336. doi:10.1101/2021.10.28.466336Google Scholar
Harrison, T. L., Shipstead, Z., Hicks, K. L., Hambrick, D. Z., Redick, T. S., & Engle, R. W. (2013). Working memory training may increase working memory capacity but not fluid intelligence. Psychological Science, 24(12), 24092419. doi:10.1177/0956797613492984Google Scholar
Hartshorne, J. K. & Germine, L. T. (2015). When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychological Science, 26(4), 433443.Google Scholar
Hawkins, J. (2021). A Thousand Brains : A New Theory of Intelligence (First edition. ed.). New York: Basic Books.Google Scholar
Hawkins, J., & Blakeslee, S. (2004). On intelligence (1st ed.). New York: Times Books.Google Scholar
Haworth, C. M. A., Wright, M. J., Luciano, M., Martin, N. G., de Geus, E. J. C., van Beijsterveldt, C. E. M., … Plomin, R. (2010). The heritability of general cognitive ability increases linearly from childhood to young adulthood. Molecular Psychiatry, 15(11), 11121120.Google Scholar
Hayes, T. R., Petrov, A. A., & Sederberg, P. B. (2015). Do we really become smarter when our fluid-intelligence test scores improve? Intelligence, 48, 114.Google Scholar
Hebling Vieira, B., Dubois, J., Calhoun, V. D., & Garrido Salmon, C. E. (2021). A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI. Human Brain Mapping, 42(18), 58735887. doi:10.1002/hbm.25656Google Scholar
Heishman, S. J., Kleykamp, B. A., & Singleton, E. G. (2010). Meta-analysis of the acute effects of nicotine and smoking on human performance. Psychopharmacology (Berl), 210(4), 453469. doi:10.1007/s00213-010-1848-1Google Scholar
Herrnstein, R. J. (1973). I.Q. in the Meritocracy (1st ed.). Boston: Little.Google Scholar
Herrnstein, R. J., & Murray, C. A. (1994). The Bell Curve : Intelligence and Class Structure in American life. New York: Free Press.Google Scholar
Hescham, S., Liu, H., Jahanshahi, A., & Temel, Y. (2020). Deep brain stimulation and cognition: Translational aspects. Neurobiology of Learning and Memory, 174, 107283. doi:10.1016/j.nlm.2020.107283Google Scholar
Heyer, D. B., Wilbers, R., Galakhova, A. A., Hartsema, E., Braak, S., Hunt, S., … Goriounova, N. A. (2021). Verbal and general IQ associate with supragranular layer Thickness and cell properties of the left temporal cortex. Cerebral Cortex, 32(11), 23432357. doi:10.1093/cercor/bhab330Google Scholar
Heyward, F. D., & Sweatt, J. D. (2015). DNA methylation in memory formation: Emerging insights. Neuroscientist. doi:10.1177/1073858415579635Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017a). Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 1025. doi:10.1016/j.intell.2016.11.001Google Scholar
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2017b). Intelligence is associated with the modular structure of intrinsic brain networks. Scientific Reports, 7(1), 16088. doi:10.1038/s41598-017-15795-7Google Scholar
Hilger, K., Fukushima, M., Sporns, O., & Fiebach, C. J. (2020). Temporal stability of functional brain modules associated with human intelligence. Human Brain Mapping, 41(2), 362372. doi:10.1002/hbm.24807Google Scholar
Hilger, K., Spinath, F. M., Troche, S., & Schubert, A. L. (2022). The biological basis of intelligence: Benchmark findings. Intelligence, 93. doi:ARTN 101665Google Scholar
Hill, W. D., Davies, G., van de Lagemaat, L. N., Christoforou, A., Marioni, R. E., Fernandes, C. P., … Deary, I. J. (2014). Human cognitive ability is influenced by genetic variation in components of postsynaptic signalling complexes assembled by NMDA receptors and MAGUK proteins. Translational Psychiatry, 4, e341. doi:10.1038/tp.2013.114Google Scholar
Hines, T. (1998). Further on Einstein’s brain. Experiment in Neurology, 150(2), 343344. doi:10.1006/exnr.1997.6759Google Scholar
Hopkins, W. D., Russe, J. L., & Schaeffer, J. (2014). Chimpanzee Intelligence Is Heritable. Current Biology, 24(14), 16491652.Google Scholar
Horvath, J. C., Forte, J. D., & Carter, O. (2015a). Evidence that transcranial direct current stimulation (tDCS) generates little-to-no reliable neurophysiologic effect beyond MEP amplitude modulation in healthy human subjects: A systematic review. Neuropsychologia, 66, 213236. doi:10.1016/j.neuropsychologia.2014.11.021Google Scholar
Horvath, J. C., Forte, J. D., & Carter, O. (2015b). Quantitative review finds no evidence of cognitive effects in healthy populations from single-session transcranial direct current stimulation (tDCS). Brain Stimulation. doi:10.1016/j.brs.2015.01.400Google Scholar
Howard-Jones, P. A. (2014). Neuroscience and education: Myths and messages. Natral Review in Neuroscience, 15(12), 817824. doi:10.1038/nrn3817Google Scholar
Hulshoff-Pol, H. E., Schnack, H. G., Posthuma, D., Mandl, R. C. W., Baare, W. F., van Oel, C., … Kahn, R. S. (2006). Genetic contributions to human brain morphology and intelligence. Journal of Neuroscience, 26(40), 1023510242.Google Scholar
Hunt, E. (2011). Human Intelligence. Cambridge; New York: Cambridge University Press.Google Scholar
Husain, M., & Mehta, M. A. (2011). Cognitive enhancement by drugs in health and disease. Trends in Cognition Science, 15(1), 2836. doi:10.1016/j.tics.2010.11.002Google Scholar
Huttenlocher, P. R. (1975). Snyaptic and dendritic development and mental defect. UCLA Forum Medical Science, 18, 123140.Google Scholar
Ilieva, I. P., & Farah, M. J. (2013). Enhancement stimulants: Perceived motivational and cognitive advantages. Frontiers in Neuroscience, 7.Google Scholar
Jacobs, B., Schall, M., & Scheibel, A. B. (1993). A quantitative dendritic analysis of Wernicke’s area in humans. II. Gender, hemispheric, and environmental factors. Journal of Comparative Neurology, 327(1), 97111. doi:10.1002/cne.903270108Google Scholar
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy Science USA, 105(19), 68296833. doi:10.1073/pnas.0801268105Google Scholar
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short- and long-term benefits of cognitive training. Proceedings of the National Academy Science USA, 108(25), 1008110086. doi:10.1073/pnas.1103228108Google Scholar
Jaeggi, S. M., Buschkuehl, M., Shah, P., & Jonides, J. (2013). The role of individual differences in cognitive training and transfer. Memory and Cognition. doi:10.3758/s13421-013-0364-zGoogle Scholar
Jaeggi, S. M., Buschkuehl, M., Shah, P., & Jonides, J. (2014). The role of individual differences in cognitive training and transfer. Memory and Cognition , 42(3), 464480. doi:10.3758/s13421-013-0364-zGoogle Scholar
Jaeggi, S. M., Studer-Luethi, B., Buschkuehl, M., Su, Y. F., Jonides, J., & Perrig, W. J. (2010). The relationship between n-back performance and matrix reasoning – implications for training and transfer. Intelligence, 38(6), 625635.Google Scholar
Jaenisch, R., & Bird, A. (2003). Epigenetic regulation of gene expression: How the genome integrates intrinsic and environmental signals. Nature Genetics, 33(Suppl), 245254. doi:10.1038/ng1089Google Scholar
Jauk, E. (2018). Intelligence and creativity from the neuroscience perspective. In Jung, R. E. and Vartanian, O. (Eds.), The Cambridge Handbook of the Neuroscience of Creativity (pp. 421436). Cambridge: Cambridge University Press.Google Scholar
Jauk, E., Neubauer, A. C., Dunst, B., Fink, A., & Benedek, M. (2015). Gray matter correlates of creative potential: A latent variable voxel-based morphometry study. Neuroimage, 111, 312320. doi:10.1016/j.neuroimage.2015.02.002Google Scholar
Jensen, A. R. (1980). Bias in Mental Testing. New York: The Free Press.Google Scholar
Jensen, A. R. (1969). How much can we boost IQ and scholastic achievement. Harvard Educational Review, 39(1), 1123.Google Scholar
Jensen, A. R. (1974). Kinship correlations reported by Burt,C Sir. Behavior Genetics, 4(1), 128.Google Scholar
Jensen, A. R. (1981). Straight Talk about Mental Tests. New York: Free Press.Google Scholar
Jensen, A. R. (1998a). Jensen on “Jensenism.” Intelligence, 26(3), 181208. doi:10.1016/S0160-2896(99)80002-6Google Scholar
Jensen, A. R. (1998b). The g Factor: The Science of Mental Ability. Westport: Praeger.Google Scholar
Jensen, A. R. (2006). Clocking the Mind: Mental Chronometry and Individual Differences. New York: Elsevier.Google Scholar
Jensen, A. R., & Miele, F. (2002). Intelligence, Race, and Genetics : Conversations with Arthur R. Jensen. Boulder, CO: Westview.Google Scholar
Jiang, L., Cui, H., Zhang, C., Cao, X., Gu, N., Zhu, Y., … Li, C. (2020). Repetitive transcranial magnetic stimulation for improving cognitive function in patients with mild cognitive impairment: A systematic review. Frontiers in Aging Neuroscience, 12, 593000. doi:10.3389/fnagi.2020.593000Google Scholar
Jiang, R., Calhoun, V. D., Fan, L., Zuo, N., Jung, R., Qi, S., … Sui, J. (2020). Gender differences in connectome-based predictions of individualized intelligence quotient and sub-domain scores. Cerebral Cortex, 30(3), 888900. doi:10.1093/cercor/bhz134Google Scholar
Johnson, M. R., Shkura, K., Langley, S. R., Delahaye-Duriez, A., Srivastava, P., Hill, W. D., … Petretto, E. (2016). Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease. Nature Neuroscience, 19(2), 223232. doi:10.1038/nn.4205Google Scholar
Johnson, W. (2012). How much can we boost IQ? An updated look at Jensen’s (1969) question and answer.Google Scholar
Johnson, W., & Bouchard, T. J. (2005). The structure of human intelligence: It is verbal, perceptual, and image rotation (VPR), not fluid and crystallized. Intelligence, 33(4), 393416.Google Scholar
Johnson, W., Bouchard, T. J., Krueger, R. F., McGue, M., & Gottesman, I. I. (2004). Just one g: Consistent results from three test batteries. Intelligence, 32(1), 95107.Google Scholar
Johnson, W., Jung, R. E., Colom, R., & Haier, R. J. (2008). Cognitive abilities independent of IQ correlate with regional brain structure. Intelligence, 36(1), 1828.Google Scholar
Johnson, W., te Nijenhuis, J., & Bouchard, T. J. (2008). Still just 1 g: Consistent results from five test batteries. Intelligence, 36(1), 8195.Google Scholar
Jung, R. E. (2014). Evolution, creativity, intelligence, and madness: “Here Be Dragons.” Frontiers in Psychology, 5.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(02), 135154.Google Scholar
Jung, R. E., & Haier, R. J. (2013). Creativity and intelligence: Brain networks that link and differentiate the expression of genius. In Vartanian, O., Bristol, A. S., & Kaufman, J. C. (Eds.), Neuroscience of creativity (pp. 233254). Cambridge, MA: The MIT Press.Google Scholar
Jung, R. E., & Vartanian, O. (Eds.) (2018). The Cambridge Handbook of the Neuroscience of Creativity. Cambridge, New York: Cambridge University Press.Google Scholar
Jung, R. E., Brooks, W. M., Yeo, R. A., Chiulli, S. J., Weers, D. C., & Sibbitt, W. L. (1999a). Biochemical markers of intelligence: A proton MR spectroscopy study of normal human brain. Proceedings of the Royal Society of London Series B-Biological Sciences, 266(1426), 13751379.Google Scholar
Jung, R. E., Haier, R. J., Yeo, R. A., Rowland, L. M., Petropoulos, H., Levine, A. S., … Brooks, W. M. (2005). Sex differences in N-acetylaspartate correlates of general intelligence: An H-1-MRS study of normal human brain. Neuroimage, 26(3), 965972.Google Scholar
Jung, R. E., Yeo, R. A., Chiulli, S. J., Sibbitt, W. L., Weers, D. C., Hart, B. L., & Brooks, W. M. (1999b). Biochemical markers of cognition: A proton MR spectroscopy study of normal human brain. Neuroreport, 10(16), 33273331.Google Scholar
Kaminski, J. A., Schlagenhauf, F., Rapp, M., Awasthi, S., Ruggeri, B., Deserno, L., … the, IMAGEN consortium. (2018). Epigenetic variance in dopamine D2 receptor: A marker of IQ malleability? Translational Psychiatry, 8(1), 169. doi:10.1038/s41398-018-0222-7Google Scholar
Kanai, R., & Rees, G. (2011). The structural basis of inter-individual differences in human behaviour and cognition. Nature Review in Neuroscience, 12(4), 231242. doi:10.1038/nrn3000Google 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. doi:10.3758/Bf03196323Google Scholar
Kane, M. J., Hambrick, D. Z., & Conway, A. R. A. (2005). Working memory capacity and fluid intelligence are strongly related constructs: Comment on Ackerman, Beier, and Boyle (2005). Psychological Bulletin, 131(1), 6671. doi:10.1037/0033-2909.131.1.66Google Scholar
Karalija, N., Köhncke, Y., Düzel, S., Bertram, L., Papenberg, G., Demuth, I., … Brandmaier, A. M. (2021). A common polymorphism in the dopamine transporter gene predicts working memory performance and in vivo dopamine integrity in aging. Neuroimage, 245, 118707. DOI: 10.1016/j.neuroimage.2021.118707Google Scholar
Karama, S., Ad-Dab’bagh, Y., Haier, R. J., Deary, I. J., Lyttelton, O. C., Lepage, C., & Evans, A. C. (2009b). Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds (vol 37, p. 145, 2009). Intelligence, 37(4), 431442.Google Scholar
Karama, S., Ad-Dab’bagh, Y., Haier, R. J., Deary, I. J., Lyttelton, O. C., Lepage, C., … Grp, B. D. C. (2009a). Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence, 37(2), 145155.Google Scholar
Karama, S., Bastin, M. E., Murray, C., Royle, N. A., Penke, L., Muñoz Maniega, S., … Deary, I. J. (2014). Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age. Molecular Psychiatry, 19(5), 555559. doi:10.1038/mp.2013.64Google Scholar
Karama, S., Colom, R., Johnson, W., Deary, I. J., Haier, R., Waber, D. P., … Grp, B. D. C. (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(4), 14431453. doi:10.1016/J.Neuroimage.2011.01.016Google Scholar
Kendler, K. S., Turkheimer, E., Ohlsson, H., Sundquist, J., & Sundquist, K. (2015). Family environment and the malleability of cognitive ability: A Swedish national home-reared and adopted-away cosibling control study. Proceedings of the National Academy Science USA, 112(15), 46124617. doi:10.1073/pnas.1417106112Google Scholar
Keyes, D. (1966). Flowers for Algernon (1st ed.). New York: Harcourt.Google Scholar
Khundrakpam, B. S., Poline, J., & Evans, A. (2021). Research consortia and large-scale data repositories for studying intelligence. In Barbey, A., Karama, S., & Haier, R. J. (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 7082). New York: Cambridge University Press.Google Scholar
Kievit, R. A., & Simpson-Kent, I. L. (2021). It’s about time: Towards a longitudinal cognitive neuroscience of intelligence. In Barbey, A., Karama, S., & Haier, R. J. (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 123146). New York: Cambridge University Press.Google Scholar
Kievit, R. A., Romeijn, J. W., Waldorp, L. J., Wicherts, J. M., Scholte, H. S., & Borsboom, D. (2011). Mind the gap: A psychometric approach to the reduction problem. Psychological Inquiry, 22(2), 6787. doi:10.1080/1047840x.2011.550181Google Scholar
Kievit, R. A., van Rooijen, H., Wicherts, J. M., Waldorp, L. J., Kan, K. J., Scholte, H. S., & Borsboom, D. (2012). Intelligence and the brain: A model-based approach. Cognition Neuroscience, 3(2), 8997. doi:10.1080/17588928.2011.628383Google Scholar
Kim, D.-J., Davis, E. P., Sandman, C. A., Sporns, O., O’Donnell, B. F., Buss, C., & Hetrick, W. P. (2016). Children’s intellectual ability is associated with structural network integrity. Neuroimage, 124(Part A), 550556. doi:10.1016/j.neuroimage.2015.09.012Google Scholar
Kim, T. D., Hong, G., Kim, J., & Yoon, S. (2019). Cognitive enhancement in neurological and psychiatric disorders using transcranial magnetic stimulation (TMS): A review of modalities, potential mechanisms and future implications. Experimental Neurobiology, 28(1), 116. doi:10.5607/en.2019.28.1.1Google Scholar
Knafo, S., & Venero, C. (2015). Cognitive Enhancement : Pharmacologic, Environmental, and Genetic factors. Amsterdam; Boston: Elsevier/AP, Academic Press is an imprint of Elsevier.Google Scholar
Knowles, E. E. M., Mathias, S. R., McKay, D. R., Sprooten, E., Blangero, J., Almasy, L., & Glahn, D. C. (2014). Genome-wide analyses of working-memory ability: A review. Current Behavioral Neuroscience Reports, 1(4), 224233. doi:10.1007/s40473-014-0028-8Google Scholar
Koenig, K. A., Frey, M. C., & Detterman, D. K. (2008). ACT and general cognitive ability. Intelligence, 36(2), 153160.Google Scholar
Koenis, M. M., Brouwer, R. M., van den Heuvel, M. P., Mandl, R. C., van Soelen, I. L., Kahn, R. S., … Hulshoff Pol, H. E. (2015). Development of the brain’s structural network efficiency in early adolescence: A longitudinal DTI twin study. Human Brain Mapping. doi:10.1002/hbm.22988Google Scholar
Kohannim, O., Hibar, D. P., Stein, J. L., Jahanshad, N., Hua, X., Rajagopalan, P., … Alzheimers Disease Neuroimaging, I. (2012a). Discovery and replication of gene influences on brain structure using LASSO regression. Frontiers in Neuroscience, 6, 115.Google Scholar
Kohannim, O., Jahanshad, N., Braskie, M. N., Stein, J. L., Chiang, M.-C., Reese, A. H., … Thompson, P. M. (2012b). Predicting white matter integrity from multiple common genetic variants. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 37(9), 20122019.Google Scholar
Kokkinakis, A. V., Cowling, P. I., Drachen, A., & Wade, A. R. (2017). Exploring the relationship between video game expertise and fluid intelligence. Plos One, 12(11), e0186621. doi:10.1371/journal.pone.0186621Google Scholar
Kolata, S., Light, K., Wass, C. D., Colas-Zelin, D., Roy, D., & Matzel, L. D. (2010). A dopaminergic gene cluster in the prefrontal cortex predicts performance indicative of general intelligence in genetically heterogeneous mice. Plos One, 5(11), e14036. doi:10.1371/journal.pone.0014036Google Scholar
Kovas, Y., & Plomin, R. (2006). Generalist genes: Implications for the cognitive sciences. Trends in Cognitive Sciences, 10(5), 198203.Google Scholar
Krause, B., & Cohen Kadosh, R. (2014). Not all brains are created equal: The relevance of individual differences in responsiveness to transcranial electrical stimulation. Frontiers in Systematic Neuroscience, 8, 25. doi:10.3389/fnsys.2014.00025Google Scholar
Kuhl, P. K. (2000). A new view of language acquisition. Proceedings of the National Academy of Sciences of the United States of America, 97(22), 1185011857.Google Scholar
Kuhl, P. K. (2004). Early language acquisition: Cracking the speech code. Nature Reviews Neuroscience, 5(11), 831843.Google Scholar
Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability is (Little More Than) working-memory capacity. Intelligence, 14(4), 389433.Google Scholar
Langer, N., Pedroni, A., Gianotti, L. R., Hanggi, J., Knoch, D., & Jancke, L. (2012). Functional brain network efficiency predicts intelligence. Hum Brain Mappings, 33(6), 13931406. doi:10.1002/hbm.21297Google Scholar
Lashley, K. S. (1964). Brain Mechanisms and Intelligence. New York: Hafner.Google Scholar
Lee, J. J. (2010). Review of intelligence and how to get it: Why schools and cultures count. Personality and Individual Differences, 48, 247255.Google Scholar
Lee, J. J., & Willoughby, E. A. (2021). Predicting cognitive-ability differences from genetic and brain-imaging data. In Barbey, A. K., Karama, S., & Haier, R. J. (Eds.), Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 349363). New York: Cambridge University Press.Google Scholar
Lee, J. J., McGue, M., Iacono, W. G., Michael, A. M., & Chabris, C. F. (2019). The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling. Intelligence, 75, 4858. doi:10.1016/j.intell.2019.01.011Google Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … Consortiu, S. S. G. A. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112-+. doi:10.1038/s41588-018-0147-3Google Scholar
Lee, J. Y., Jun, H., Soma, S., Nakazono, T., Shiraiwa, K., Dasgupta, A., … Igarashi, K. M. (2021). Dopamine facilitates associative memory encoding in the entorhinal cortex. Nature, 598(7880), 321326. doi:10.1038/s41586-021-03948-8Google Scholar
Lee, K. H., Choi, Y. Y., Gray, J. R., Cho, S. H., Chae, J. H., Lee, S., & Kim, K. (2006). Neural correlates of superior intelligence: Stronger recruitment of posterior parietal cortex. Neuroimage, 29(2), 578586.Google Scholar
Lemos, G. C., Almeida, L. S., & Colom, R. (2011). Intelligence of adolescents is related to their parents’ educational level but not to family income. Personality and Individual Differences, 50(7), 10621067.Google Scholar
Lerner, B. (1980). The war on testing – Detroit Edison in perspective. Personnel Psychology, 33(1), 1116.Google Scholar
Lett, T. A., Vogel, B. O., Ripke, S., Wackerhagen, C., Erk, S., Awasthi, S., … consortium, I. (2019). Cortical surfaces mediate the relationship between polygenic scores for intelligence and general intelligence. Cerebral Cortex, 30(4), 27082719. doi:10.1093/cercor/bhz270Google Scholar
Li, H., Namburi, P., Olson, J. M., Borio, M., Lemieux, M. E., Beyeler, A., … Tye, K. M. (2022). Neurotensin orchestrates valence assignment in the amygdala. Nature. doi:10.1038/s41586-022-04964-yGoogle Scholar
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009). Brain anatomical network and intelligence. PLoS Computational Biology, 5(5), e1000395. doi:10.1371/journal.pcbi.1000395Google Scholar
Limb, C. J., & Braun, A. R. (2008). Neural substrates of spontaneous musical performance: An FMRI study of jazz improvisation. Plos One, 3(2), e1679. doi:10.1371/journal.pone.0001679Google Scholar
Lipp, I., Benedek, M., Fink, A., Koschutnig, K., Reishofer, G., Bergner, S., … Neubauer, A. (2012). Investigating neural efficiency in the visuo-spatial domain: An FMRI study. Plos One, 7(12), e51316. doi:10.1371/journal.pone.0051316Google Scholar
Liu, S., Chow, H. M., Xu, Y., Erkkinen, M. G., Swett, K. E., Eagle, M. W., … Braun, A. R. (2012). Neural correlates of lyrical improvisation: An FMRI study of freestyle rap. Scientific Reports, 2, 834. doi:10.1038/srep00834Google Scholar
Loehlin, J. C. (1989). Partitioning environmental and genetic contributions to behavioral development. American Psychology, 44(10), 12851292. Retrieved from www.ncbi.nlm.nih.gov/pubmed/2679255Google Scholar
Loehlin, J. C., & Nichols, R. C. (1976). Heredity, Environment, & Personality : A Study of 850 Sets of Twins. Austin: University of Texas Press.Google Scholar
Loo, C. K., & Mitchell, P. B. (2005). A review of the efficacy of transcranial magnetic stimulation (TMS) treatment for depression, and current and future strategies to optimize efficacy. Journal of Affected Disorders, 88(3), 255267. doi:10.1016/j.jad.2005.08.001Google Scholar
Luber, B., & Lisanby, S. H. (2014). Enhancement of human cognitive performance using transcranial magnetic stimulation (TMS). Neuroimage, 85(Pt 3), 961970. doi:10.1016/j.neuroimage.2013.06.007Google Scholar
Lubinski, D. (2009). Cognitive epidemiology: With emphasis on untangling cognitive ability and socioeconomic status. Intelligence, 37(6), 625633.Google Scholar
Lubinski, D., Benbow, C. P., & Kell, H. J. (2014). Life paths and accomplishments of mathematically precocious males and females four decades later. Psychological Science, 25(12), 22172232. doi:10.1177/0956797614551371Google Scholar
Lubinski, D., Benbow, C. P., Webb, R. M., & Bleske-Rechek, A. (2006). Tracking exceptional human capital over two decades. Psychological Science, 17(3), 194199.Google Scholar
Lubinski, D., Schmidt, D. B., & Benbow, C. P. (1996). A 20-year stability analysis of the study of values for intellectually gifted individuals from adolescence to adulthood. Journal of Applied Psychology, 81(4), 443451.Google Scholar
Luciano, M., Wright, M. J., Smith, G. A., Geffen, G. M., Geffen, L. B., & Martin, N. G. (2001). Genetic covariance among measures of information processing speed, working memory, and IQ. Behavior Genetics, 31(6), 581592.Google Scholar
Luders, E., Harr, K. L., Thompson, P. M., Rex, D. E., Woods, R. P., DeLuca, H., … Toga, A. W. (2006). Gender effects on cortical thickness and the influence of scaling. Human Brain Mapping, 27(4), 314324.Google Scholar
Luders, E., Narr, K. L., Bilder, R. M., Thompson, P. M., Szeszko, P. R., Hamilton, L., & Toga, A. W. (2007). Positive correlations between corpus callosum thickness and intelligence. Neuroimage, 37(4), 14571464. doi:10.1016/j.neuroimage.2007.06.028Google Scholar
Luders, E., Narr, K. L., Thompson, P. M., Rex, D. E., Jancke, L., Steinmetz, H., & Toga, A. W. (2004). Gender differences in cortical complexity. Natural Neuroscience, advanced online publication. Retrieved from http://dx.doi.org/10.1038/nn1277Google Scholar
Luo, Q., Perry, C., Peng, D. L., Jin, Z., Xu, D., Ding, G. S., & Xu, S. Y. (2003). The neural substrate of analogical reasoning: An fMRI study. Cognitive Brain Research, 17(3), 527534.Google Scholar
Lynn, R. (2009). What has caused the Flynn effect? Secular increases in the Development Quotients of infants. Intelligence, 37(1), 1624.Google Scholar
Mackey, A. P., Finn, A. S., Leonard, J. A., Jacoby-Senghor, D. S., West, M. R., Gabrieli, C. F., & Gabrieli, J. D. (2015). Neuroanatomical correlates of the income-achievement gap. Psychological Science, 26(6), 925933. doi:10.1177/0956797615572233Google Scholar
Mackey, A. P., Hill, S. S., Stone, S. I., & Bunge, S. A. (2011). Differential effects of reasoning and speed training in children. Developmental Science, 14(3), 582590.Google Scholar
Mackintosh, N. J. (1995). Cyril Burt : Fraud or Framed? Oxford; New York: Oxford University Press.Google Scholar
Mackintosh, N. J. (2011). IQ and Human Intelligence (2nd ed.). Oxford; New York: Oxford University Press.Google Scholar
Macnamara, B. N., & Maitra, M. (2019). The role of deliberate practice in expert performance: Revisiting Ericsson, Krampe & Tesch-Romer (1993). Royal Society on Open Science, 6(8), 190327. doi:10.1098/rsos.190327Google Scholar
Maguire, E. A., Valentine, E. R., Wilding, J. M., & Kapur, N. (2003). Routes to remembering: The brains behind superior memory. Nature Neuroscience, 6(1), 9095. doi:10.1038/nn988Google Scholar
Maher, B. (2008). Poll results: Look who’s doping. Nature, 452(7188), 674675. doi:10.1038/452674aGoogle Scholar
Makel, M. C., Kell, H. J., Lubinski, D., Putallaz, M., & Benbow, C. P. (2016). When lightning strikes twice: Profoundly gifted, profoundly accomplished. Psychological Science, 27(7), 10041018. doi:10.1177/0956797616644735Google Scholar
Makowski, C., Meer, D. v. d., Dong, W., Wang, H., Wu, Y., Zou, J., … Chen, C.-H. (2022). Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases. Science, 375(6580), 522528. doi:10.1126/science.abe8457Google Scholar
Malanchini, M., Rimfeld, K., Gidziela, A., Cheesman, R., Allegrini, A. G., Shakeshaft, N., … Plomin, R. (2021). Pathfinder: A gamified measure to integrate general cognitive ability into the biological, medical, and behavioural sciences. Molecular Psychiatry. doi:10.1038/s41380-021-01300-0Google Scholar
Maldjian, J. A., Davenport, E. M., & Whitlow, C. T. (2014). Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode. Neuroimage, 96, 8894. doi:10.1016/j.neuroimage.2014.03.065Google Scholar
Mardis, E. R. (2008). Next-generation DNA sequencing methods. Annual Review of Genomics and Human Genetics, 9, 387402. doi:10.1146/annurev.genom.9.081307.164359Google Scholar
Marioni, R. E., Davies, G., Hayward, C., Liewald, D., Kerr, S. M., Campbell, A., … Deary, I. J. (2014). Molecular genetic contributions to socioeconomic status and intelligence. Intelligence, 44(100), 2632. doi:10.1016/j.intell.2014.02.006Google Scholar
Marks, G. N. (2022). Cognitive ability has powerful, widespread and robust effects on social stratification: Evidence from the 1979 and 1997 US National Longitudinal Surveys of Youth. Intelligence, 94, 101686. doi:10.1016/j.intell.2022.101686Google Scholar
Martinez, K., & Colom, R. (2021). Imaging the intelligence of humans. In Barbey, A., Karama, S., & H. R. J. (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 4469). New York: Cambridge University Press.Google Scholar
Martinez, K., Janssen, J., Pineda-Pardo, J. A., Carmona, S., Roman, F. J., Aleman-Gomez, Y., … Colom, R. (2017). Individual differences in the dominance of interhemispheric connections predict cognitive ability beyond sex and brain size. Neuroimage, 155, 234244.Google Scholar
Maslen, H., Faulmuller, N., & Savulescu, J. (2014). Pharmacological cognitive enhancement-how neuroscientific research could advance ethical debate. Frontier in Systematic Neuroscience, 8, 107. doi:10.3389/fnsys.2014.00107Google Scholar
Matzel, L. D., & Kolata, S. (2010). Selective attention, working memory, and animal intelligence. Neuroscience Biobehavioral Review, 34(1), 2330. doi:10.1016/j.neubiorev.2009.07.002Google Scholar
Matzel, L. D., Crawford, D. W., & Sauce, B. (2020). Déjà vu All Over Again: A unitary biological mechanism for intelligence is (Probably) untenable. Journal of Intelligence, 8(24).Google Scholar
Matzel, L. D., Han, Y. R., Grossman, H., Karnik, M. S., Patel, D., Scott, N., … Gandhi, C. C. (2003). Individual differences in the expression of a “General” learning ability in mice. Journal of Neuroscience, 23(16), 64236433. Retrieved from www.jneurosci.org/cgi/content/abstract/23/16/6423Google Scholar
Matzel, L. D., Sauce, B., & Wass, C. (2013). The architecture of intelligence: Converging evidence from studies of humans and animals. Current Directions in Psychological Science, 22(5), 342348. doi:10.1177/0963721413491764Google Scholar
Mayseless, N., & Shamay-Tsoory, S. G. (2015). Enhancing verbal creativity: Modulating creativity by altering the balance between right and left inferior frontal gyrus with tDCS. Neuroscience, 291, 167176. doi:10.1016/j.neuroscience.2015.01.061Google Scholar
McCabe, K. O., Lubinski, D., & Benbow, C. P. (2020). Who shines most among the brightest?: A 25-year longitudinal study of elite STEM graduate students. Journal of Personality and Social Psychology, 119(2), 390416. doi:10.1037/pspp0000239Google Scholar
McDaniel, M. A. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33(4), 337346.Google Scholar
McGue, M., Anderson, E. L., Willoughby, E., Giannelis, A., Iacono, W. G., & Lee, J. J. (2022). Not by g alone: The benefits of a college education among individuals with low levels of general cognitive ability. Intelligence, 92, 101642. doi:10.1016/j.intell.2022.101642Google Scholar
McGue, M., Bouchard, T. J., Iacono, W. G., & Lykken, D. T. (1993). Age effects on heritability of intelligence. In Plomin, R. & McClearn, G. E. (Eds.), Nature, Nurture, and Psychology (pp. 5976). Washington, DC: American Psychological Association.Google Scholar
McKinley, R. A., Bridges, N., Walters, C. M., & Nelson, J. (2012). Modulating the brain at work using noninvasive transcranial stimulation. Neuroimage, 59(1), 129137. doi:10.1016/j.neuroimage.2011.07.075Google Scholar
Melby-Lervag, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49(2), 270291. doi:10.1037/a0028228Google Scholar
Melby-Lervåg, M., Redick, T. S., & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “Far Transfer”: Evidence from a meta-analytic review. Perspectives on Psychological Science, 11(4), 512534. doi:10.1177/1745691616635612Google Scholar
Miller, B. L., Boone, K., Cummings, J. L., Read, S. L., & Mishkin, F. (2000). Functional correlates of musical and visual ability in frontotemporal dementia. British Journal of Psychiatry, 176, 458463. Retrieved from www.ncbi.nlm.nih.gov/pubmed/10912222Google Scholar
Miller, B. L., Cummings, J., Mishkin, F., Boone, K., Prince, F., Ponton, M., & Cotman, C. (1998). Emergence of artistic talent in frontotemporal dementia. Neurology, 51(4), 978982. Retrieved from www.ncbi.nlm.nih.gov/pubmed/9781516Google Scholar
Miller, E. B., Farkas, G., & Duncan, G. J. (2016). Does Head Start differentially benefit children with risks targeted by the program’s service model? Early Childhood Research Quarterly, 34, 112. doi:10.1016/j.ecresq.2015.08.001Google Scholar
Mitchell, B. L., Hansell, N. K., McAloney, K., Martin, N. G., Wright, M. J., Renteria, M. E., & Grasby, K. L. (2022). Polygenic influences associated with adolescent cognitive skills. Intelligence, 94, 101680. doi:10.1016/j.intell.2022.101680Google Scholar
Mitchell, K. J. (2018). Innate : How the Wiring of Our Brains Shapes Who We Are. Princeton, NJ: Princeton University Press.Google Scholar
Moody, D. E. (2009). Can intelligence be increased by training on a task of working memory? Intelligence, 37(4), 327328.Google Scholar
Moreau, D. (2022). How malleable are cognitive abilities? A critical perspective on popular brief interventions. American Psychologist, 77(3), 409423. doi:10.1037/amp0000872Google Scholar
Moreau, D., Macnamara, B. N., & Hambrick, D. Z. (2019). Overstating the role of environmental factors in success: A cautionary note. Current Directions in Psychological Science, 28(1), 2833. doi:10.1177/0963721418797300Google Scholar
Mountjoy, E., Schmidt, E. M., Carmona, M., Schwartzentruber, J., Peat, G., Miranda, A., … Ghoussaini, M. (2021). An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nature Genetics. doi:10.1038/s41588-021-00945-5Google Scholar
Muetzel, R. L., Mous, S. E., van der Ende, J., Blanken, L. M., van der Lugt, A., Jaddoe, V. W., … White, T. (2015). White matter integrity and cognitive performance in school-age children: A population-based neuroimaging study. Neuroimage, 119, 119128. doi:10.1016/j.neuroimage.2015.06.014Google Scholar
Murray, C. (1995). The-bell-curve and its critics. Commentary, 99(5), 2330.Google Scholar
Murray, C. A. (2013). Coming Apart : The State of White America, 1960–2010 (First paperback edition. ed.). New York: Crown Forum.Google Scholar
Murray, C., Pattie, A., Starr, J. M., & Deary, I. J. (2012). Does cognitive ability predict mortality in the ninth decade? The Lothian Birth Cohort 1921. Intelligence, 40(5), 490498.Google Scholar
Muzur, A., Pace-Schott, E. F., & Hobson, J. A. (2002). The prefrontal cortex in sleep. Trends in Cognition Science, 6(11), 475481. Retrieved from www.ncbi.nlm.nih.gov/pubmed/12457899Google Scholar
Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci, S. J., … Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51(2), 77101.Google Scholar
Neubauer, A. C. (2021). The future of intelligence research in the coming age of artificial intelligence – With a special consideration of the philosophical movements of trans- and posthumanism. Intelligence, 87, 101563. doi:10.1016/j.intell.2021.101563Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience and Biobehavioral Reviews, 33(7), 10041023.Google Scholar
Neville, H., Stevens, C., Pakulak, E., & Bell, T. A. (2013). Commentary: Neurocognitive consequences of socioeconomic disparities. Developmental Science, 16(5), 708712. doi:10.1111/desc.12081Google Scholar
Newman, S. D., & Just, M. A. (2005). The neural bases of intelligence: A perspective based on functional neuroimaging. In Sternberg, Robert J. & Pretz, Jean E. (Eds.), Cognition and Intelligence: Identifying the Mechanisms of the Mind (pp. 88103). New York: Cambridge University Press.Google Scholar
Nihongaki, Y., Kawano, F., Nakajima, T., & Sato, M. (2015). Photoactivatable CRISPR-Cas9 for optogenetic genome editing. Nature Biotechnology. doi:10.1038/nbt.3245Google Scholar
Nisbett, R. E. (2009). Intelligence and How to Get It: Why Schools and Cultures Count (1st ed.). New York: W.W. Norton & Co.Google Scholar
Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Intelligence new findings and theoretical developments. American Psychologist, 67(2), 130159. doi:10.1037/a0026699Google Scholar
Noble, K. G., & Giebler, M. A. (2020). The neuroscience of socioeconomic inequality. Current Opinion in Behavioral Sciences, 36, 2328. doi:10.1016/j.cobeha.2020.05.007Google Scholar
Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., … Sowell, E. R. (2015). Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18(5), 773778. doi:10.1038/nn.3983Google Scholar
Noble, K. G., Wolmetz, M. E., Ochs, L. G., Farah, M. J., & McCandliss, B. D. (2006). Brain-behavior relationships in reading acquisition are modulated by socioeconomic factors. Developmental Science, 9(6), 642654. doi:10.1111/j.1467-7687.2006.00542.xGoogle Scholar
Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., … LifeLines Cohort, S. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437449. doi:10.1038/s41588-022-01016-zGoogle Scholar
Ozawa, A., & Arakawa, H. (2021). Chemogenetics drives paradigm change in the investigation of behavioral circuits and neural mechanisms underlying drug action. Behavioural Brain Research, 406, 113234. doi:10.1016/j.bbr.2021.113234Google Scholar
Pages, R., Lukes, D. J., Bailey, D. H., & Duncan, G. J. (2020). Elusive longer-run impacts of head start: Replications within and across cohorts. Educational Evaluation and Policy Analysis, 42(4), 471492. doi:10.3102/0162373720948884Google Scholar
Pages, R., Protzko, J., & Bailey, D. H. (2021). The breadth of impacts from the Abecedarian project early intervention on cognitive skills. Journal of Research on Educational Effectiveness, 120. doi:10.1080/19345747.2021.1969711Google Scholar
Pagnaer, T., Siermann, M., Borry, P., & Tsuiko, O. (2021). Polygenic risk scoring of human embryos: A qualitative study of media coverage. BMC Medical Ethics, 22(1), 125. doi:10.1186/s12910-021-00694-4Google Scholar
Pahor, A., & Jausovec, N. (2014). The effects of theta transcranial alternating current stimulation (tACS) on fluid intelligence. International Journal of Psychophysiology, 93(3), 322331. doi:10.1016/j.ijpsycho.2014.06.015Google Scholar
Pahor, A., Seitz, A. R., & Jaeggi, S. M. (2022). Near transfer to an unrelated N-back task mediates the effect of N-back working memory training on matrix reasoning. Nature Human Behaviour. doi:10.1038/s41562-022-01384-wGoogle Scholar
Panizzon, M. S., Vuoksimaa, E., Spoon, K. M., Jacobson, K. C., Lyons, M. J., Franz, C. E., … Kremen, W. S. (2014). Genetic and environmental influences of general cognitive ability: Is g a valid latent construct? Intelligence, 43, 6576. doi:10.1016/j.intell.2014.01.008Google Scholar
Parasuraman, R., & Jiang, Y. (2012). Individual differences in cognition, affect, and performance: Behavioral, neuroimaging, and molecular genetic approaches. Neuroimage, 59(1), 7082.Google Scholar
Parks, R. W., Loewenstein, D. A., Dodrill, K. L., Barker, W. W., Yoshii, F., Chang, J. Y., … Duara, R. (1988). Cerebral metabolic effects of a verbal fluency test – A Pet Scan Study. Journal of Clinical and Experimental Neuropsychology, 10(5), 565575.Google Scholar
Pascoli, V., Turiault, M., & Luscher, C. (2012). Reversal of cocaine-evoked synaptic potentiation resets drug-induced adaptive behaviour. Nature, 481(7379), 7175. doi:10.1038/nature10709Google Scholar
Pedersen, N. L., Plomin, R., Nesselroade, J. R., & Mcclearn, G. E. (1992). A quantitative genetic-analysis of cognitive-abilities during the 2nd-half of the life-span. Psychological Science, 3(6), 346353.Google Scholar
Penke, L., Maniega, S. M., Bastin, M. E., Hernandez, M. C. V., Murray, C., Royle, N. A., … Deary, I. J. (2012). Brain white matter tract integrity as a neural foundation for general intelligence. Molecular Psychiatry, 17(10), 10261030. doi:10.1038/Mp.2012.66Google Scholar
Perez, P., Chavret-Reculon, E., Ravassard, P., & Bouret, S. (2022). Using inhibitory DREADDs to silence LC neurons in Monkeys. Brain Sciences, 12(2), 206. Retrieved from www.mdpi.com/2076-3425/12/2/206Google Scholar
Perfetti, B., Saggino, A., Ferretti, A., Caulo, M., Romani, G. L., & Onofrj, M. (2009). Differential patterns of cortical activation as a function of fluid reasoning complexity. Hum Brain Mapping, 30(2), 497510. doi:10.1002/hbm.20519Google Scholar
Perobelli, S., Alessandrini, F., Zoccatelli, G., Nicolis, E., Beltramello, A., Assael, B. M., & Cipolli, M. (2015). Diffuse alterations in grey and white matter associated with cognitive impairment in Shwachman-Diamond syndrome: Evidence from a multimodal approach. Neuroimage Clinic, 7, 721731. doi:10.1016/j.nicl.2015.02.014Google Scholar
Pesenti, M., Zago, L., Crivello, F., Mellet, E., Samson, D., Duroux, B., … Tzourio-Mazoyer, N. (2001). Mental calculation in a prodigy is sustained by right prefrontal and medial temporal areas. Nature Neuroscience, 4(1), 103107.Google Scholar
Petrill, S. A., & Deater-Deckard, K. (2004). The heritability of general cognitive ability: A within-family adoption design. Intelligence, 32(4), 403409.Google Scholar
Pfleiderer, B., Ohrmann, P., Suslow, T., Wolgast, M., Gerlach, A. L., Heindel, W., & Michael, N. (2004). N-acetylaspartate levels of left frontal cortex are associated with verbal intelligence in women but not in men: A proton magnetic resonance spectroscopy study. Neuroscience, 123(4), 10531058.Google Scholar
Pietschnig, J., & Voracek, M. (2015). One century of global IQ gains: A formal meta-analysis of the Flynn Effect (1909–2013). Perspectives on Psychological Science, 10(3), 282306.Google Scholar
Pietschnig, J., Voracek, M., & Formann, A. K. (2010). Mozart effect-Shmozart effect: A meta-analysis. Intelligence, 38(3), 314–323.Google Scholar
Pineda-Pardo, J. A., Bruna, R., Woolrich, M., Marcos, A., Nobre, A. C., Maestu, F., & Vidaurre, D. (2014). Guiding functional connectivity estimation by structural connectivity in MEG: An application to discrimination of conditions of mild cognitive impairment. Neuroimage, 101, 765777. doi:10.1016/j.neuroimage.2014.08.002Google Scholar
Pinho, A. L., de Manzano, O., Fransson, P., Eriksson, H., & Ullen, F. (2014). Connecting to create: Expertise in musical improvisation is associated with increased functional connectivity between premotor and prefrontal areas. Journal of Neuroscience, 34(18), 61566163. doi:10.1523/JNEUROSCI.4769-13.2014Google Scholar
Pinker, S. (2002). The Blank Slate : The Modern Denial of Human Nature. New York: Viking.Google Scholar
Plis, S. M., Weisend, M. P., Damaraju, E., Eichele, T., Mayer, A., Clark, V. P., … Calhoun, V. D. (2011). Effective connectivity analysis of fMRI and MEG data collected under identical paradigms. Computational Biology Medicine, 41(12), 11561165. doi:10.1016/j.compbiomed.2011.04.011Google Scholar
Plomin, R. (1999). Genetics and general cognitive ability. Nature, 402(6761 Suppl), C25–29. doi:10.1038/35011520Google Scholar
Plomin, R. (2018). Blueprint : How DNA Makes us Who We Are. Cambridge, MA: The MIT Press.Google Scholar
Plomin, R., & Deary, I. J. (2015). Genetics and intelligence differences: Five special findings. Molecular Psychiatry, 20(1), 98108. doi:10.1038/mp.2014.105Google Scholar
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016). Top 10 replicated findings from behavioral genetics. Perspectives on Psychological Science : A Journal of the Association for Psychological Science, 11(1), 323. doi:10.1177/1745691615617439Google Scholar
Plomin, R., & Kosslyn, S. M. (2001). Genes, brain and cognition. Nature Neuroscience, 4(12), 11531154.Google Scholar
Plomin, R., & Petrill, S. A. (1997). Genetics and intelligence: What’s new? Intelligence, 24(1), 5377.Google ScholarGoogle Scholar
Plomin, R., Shakeshaft, N. G., McMillan, A., & Trzaskowski, M. (2014b). Nature, nurture, and expertise: Response to Ericsson. Intelligence, 45, 115117.Google Scholar
Plomin, R., & von Stumm, S. (2018). The new genetics of intelligence. Nature Review Genetics, 19(3), 148159. doi:10.1038/nrg.2017.104Google Scholar
Pol, H. E. H., Posthuma, D., Baare, W. F. C., De Geus, E. J. C., Schnack, H. G., van Haren, N. E. M., … Boomsma, D. I. (2002). Twin-singleton differences in brain structure using structural equation modelling. Brain, 125, 384390.Google Scholar
Polderman, T. J., Benyamin, B., de Leeuw, C. A., Sullivan, P. F., van Bochoven, A., Visscher, P. M., & Posthuma, D. (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics, 47(7), 702709. doi:10.1038/ng.3285Google Scholar
Poldrack, R. A. (2015). Is “efficiency” a useful concept in cognitive neuroscience? Developmental Cognition Neuroscience, 11, 1217. doi:10.1016/j.dcn.2014.06.001Google Scholar
Posthuma, D., & de Geus, E. J. C. (2006). Progress in the molecular-genetic study of intelligence. Current Directions in Psychological Science, 15(4), 151155.Google Scholar
Posthuma, D., Baare, W. F. C., Pol, H. E. H., Kahn, R. S., Boomsma, D. I., & De Geus, E. J. C. (2003). Genetic correlations between brain volumes and the WAIS-III dimensions of verbal comprehension, working memory, perceptual organization, and processing speed. Twin Research, 6(2), 131139.Google Scholar
Posthuma, D., De Geus, E. J., Baare, W. F., Hulshoff Pol, H. E., Kahn, R. S., & Boomsma, D. I. (2002). The association between brain volume and intelligence is of genetic origin. Natural Neuroscience, 5(2), 8384.Google Scholar
Posthuma, D., De Geus, E., & Boomsma, D. (2003). Genetic contributions to anatomical, behavioral, and neurophysiological indices of cognition. In Plomin, R., DeFries, J., Craig, I. W., & McGuffin, P. (Eds.), Behavioral Genetics in the Postgenomic Era (pp. 141161). Washington, DC: American psychological Association.Google Scholar
Prabhakaran, V., Smith, J. A., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. (1997). Neural substrates of fluid reasoning: An fMRI study of neocortical activation during performance of the Raven’s Progressive Matrices Test. Cognitive Psychology, 33(1), 4363.Google Scholar
Prat, C. S., Mason, R. A., & Just, M. A. (2012). An fMRI investigation of analogical mapping in metaphor comprehension: The influence of context and individual cognitive capacities on processing demands. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(2), 282294. doi:10.1037/a0026037Google Scholar
Preusse, F., Van Der Meer, E., Deshpande, G., Krueger, F., & Wartenburger, I. (2011). Frontiers: Fluid intelligence allows flexible recruitment of the parieto-frontal network in analogical reasoning. Frontier in Human Neuroscience, 5.Google Scholar
Protzko, J., Aronson, J., & Blair, C. (2013). How to make a young child smarter: Evidence from the database of raising intelligence. Perspectives on Psychological Science, 8(1), 2540.Google Scholar
Quiroga, M. A., Escorial, S., Román, F. J., Morillo, D., Jarabo, A., Privado, J., … Colom, R. (2015). Can we reliably measure the general factor of intelligence (g) through commercial video games? Yes, we can! Intelligence, 53, 17. doi:10.1016/j.intell.2015.08.004Google Scholar
Quiroga, M. Á., & Colom, R. (2020). Intelligence and video games. In Sternberg, R. J. (Ed.), The Cambridge Handbook of Intelligence (2 ed., pp. 626656). Cambridge: Cambridge University Press.Google Scholar
Quiroga, M. A., Diaz, A., Roman, F. J., Privado, J., & Colom, R. (2019). Intelligence and video games: Beyond “brain-games.” Intelligence, 75, 8594. doi:10.1016/j.intell.2019.05.001Google Scholar
Ramey, C. T., & Ramey, S. L. (2004). Early learning and school readiness: Can early intervention make a difference? Merrill-Palmer Quarterly-Journal of Developmental Psychology, 50(4), 471491.Google Scholar
Rankin, K. P., Liu, A. A., Howard, S., Slama, H., Hou, C. E., Shuster, K., & Miller, B. L. (2007). A case-controlled study of altered visual art production in Alzheimer’s and FTLD. Cognitive Behavioral Neurology, 20(1), 4861. doi:10.1097/WNN.0b013e31803141ddGoogle Scholar
Rauscher, F. H., Shaw, G. L., & Ky, K. N. (1993). Music and spatial task performance. Nature, 365(6447), 611. doi:10.1038/365611a0Google Scholar
Redick, T. S. (2015). Working memory training and interpreting interactions in intelligence interventions. Intelligence, 50(0), 1420. doi:10.1016/j.intell.2015.01.014Google Scholar
Redick, T. S. (2019). The Hype Cycle of working memory training. Current Directions in Psychological Sciences, 28(5), 423429. doi:10.1177/0963721419848668Google Scholar
Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z., … Engle, R. W. (2013). No evidence of intelligence improvement after working memory training: A Randomized, Placebo-Controlled Study. Journal of Experimental Psychology-General, 142(2), 359379. doi:10.1037/A0029082Google Scholar
Ree, M. J., & Carretta, T. R. (1996). Central role of g in military pilot selection. International Journal of Aviation Psychology, 6(2), 111123.Google Scholar
Ree, M. J., & Carretta, T. R. (2022). Thirty years of research on general and specific abilities: Still not much more than g. Intelligence, 91, 101617. doi:10.1016/j.intell.2021.101617Google Scholar
Ree, M. J., & Earles, J. A. (1991). Predicting training success – Not much more than G. Personnel Psychology, 44(2), 321332.Google Scholar
Reijneveld, J. C., Ponten, S. C., Berendse, H. W., & Stam, C. J. (2007). The application of graph theoretical analysis to complex networks in the brain. Clinical Neurophysiology, 118(11), 23172331. doi:10.1016/j.clinph.2007.08.010Google Scholar
Reverberi, C., Bonatti, L. L., Frackowiak, R. S., Paulesu, E., Cherubini, P., & Macaluso, E. (2012). Large scale brain activations predict reasoning profiles. Neuroimage, 59(2), 17521764. doi:10.1016/j.neuroimage.2011.08.027Google Scholar
Rhein, C., Muhle, C., Richter-Schmidinger, T., Alexopoulos, P., Doerfler, A., & Kornhuber, J. (2014). Neuroanatomical correlates of intelligence in healthy young adults: The role of basal ganglia volume. Plos One, 9(4), e93623. doi:10.1371/journal.pone.0093623Google Scholar
Rietveld, C. A., Esko, T., Davies, G., Pers, T. H., Turley, P., Benyamin, B., … Koellinger, P. D. (2014). Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proceedings of the National Academy of Sciences of the United States of America, 111(38), 1379013794.Google Scholar
Ritchie, S. (2022). Everything you need to know about breastfeeding and intelligence. Substack: https://stuartritchie.substack.com/p/breastfeeding-iq?utm_source=%2Fprofile%2F1881468-stuart-ritchie&utm_medium=reader2.Google Scholar
Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A Meta-Analysis. Psychological Science, 29(8), 13581369. doi:10.1177/0956797618774253Google Scholar
Ritchie, S. J., Booth, T., Valdés Hernández, M. d. C., Corley, J., Maniega, S. M., Gow, A. J., … Deary, I. J. (2015). Beyond a bigger brain: Multivariable structural brain imaging and intelligence. Intelligence, 51(0), 4756. doi:10.1016/j.intell.2015.05.001Google Scholar
Ritchie, S. J., Cox, S. R., Shen, X. Y., Lombardo, M. V., Reus, L. M., Alloza, C., … Deary, I. J. (2018). Sex differences in the adult human brain: Evidence from 5216 UK Biobank participants. Cerebral Cortex, 28(8), 29592975. doi:10.1093/cercor/bhy109Google Scholar
Robertson, K. F., Smeets, S., Lubinski, D., & Benbow, C. P. (2010). Beyond the threshold hypothesis: Even among the gifted and top math/science graduate students, cognitive abilities, vocational interests, and lifestyle preferences matter for career choice, performance, and persistence. Current Directions in Psychological Science, 19(6), 346351.Google Scholar
Román, F. J., Morillo, D., Estrada, E., Escorial, S., Karama, S., & Colom, R. (2018). Brain-intelligence relationships across childhood and adolescence: A latent-variable approach. Intelligence, 68, 2129. doi:10.1016/j.intell.2018.02.006Google Scholar
Romeo, R. R., Leonard, J. A., Scherer, E., Robinson, S., Takada, M., Mackey, A. P., … Gabrieli, J. D. E. (2021). Replication and extension of family-based training program to improve cognitive abilities in young children. Journal of Research on Educational Effectiveness, 14(4), 792811. doi:10.1080/19345747.2021.1931999Google Scholar
Ryman, S. G., Yeo, R. A., Witkiewitz, K., Vakhtin, A. A., van den Heuvel, M., de Reus, M., … Jung, R. E. (2016). Fronto-Parietal gray matter and white matter efficiency differentially predict intelligence in males and females. Human Brain Mapping, 37(11), 40064016. doi:10.1002/hbm.23291Google Scholar
Sackett, P. R., Kuncel, N. R., Arneson, J. J., Cooper, S. R., & Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance? Psychological Bulletin, 135(1), 122. doi:10.1037/a0013978Google Scholar
Sahakian, B. J., & Kramer, A. F. (2015). Editorial overview: Cognitive enhancement. Current Opinion in Behavioral Sciences, 4, V-vii. doi:10.1016/j.cobeha.2015.06.006Google Scholar
Sahakian, B., & Morein-Zamir, S. (2007). Professor’s little helper. Nature, 450(7173), 11571159. doi:10.1038/4501157aGoogle Scholar
Sander, J. D., & Joung, J. K. (2014). CRISPR-Cas systems for genome editing, regulation and targeting. Nature Biotechnology, 32(4), 347355. doi:10.1038/nbt.2842Google Scholar
Santarnecchi, E., Brem, A.-K., Levenbaum, E., Thompson, T., Kadosh, R. C., & Pascual-Leone, A. (2015). Enhancing cognition using transcranial electrical stimulation. Current Opinion in Behavioral Sciences, 4, 171178. doi:10.1016/j.cobeha.2015.06.003Google Scholar
Santarnecchi, E., Emmendorfer, A., & Pascual-Leone, A. (2017). Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study. Intelligence, 63, 928. doi:10.1016/j.intell.2017.04.008Google Scholar
Santarnecchi, E., Emmendorfer, A., Tadayon, S., Rossi, S., Rossi, A., Pascual-Leone, A., & Team, H. S. (2017). Network connectivity correlates of variability in fluid intelligence performance. Intelligence, 65, 3547.Google Scholar
Santarnecchi, E., Galli, G., Polizzotto, N. R., Rossi, A., & Rossi, S. (2014). Efficiency of weak brain connections support general cognitive functioning. Hum Brain Mapping, 35(9), 45664582. doi:10.1002/hbm.22495Google Scholar
Santarnecchi, E., Muller, T., Rossi, S., Sarkar, A., Polizzotto, N. R., Rossi, A., & Cohen Kadosh, R. (2016). Individual differences and specificity of prefrontal gamma frequency-tACS on fluid intelligence capabilities. Cortex, 75, 3343. doi:10.1016/j.cortex.2015.11.003Google Scholar
Santarnecchi, E., Polizzotto, N. R., Godone, M., Giovannelli, F., Feurra, M., Matzen, L., … Rossi, S. (2013). Frequency-dependent enhancement of fluid intelligence induced by transcranial oscillatory potentials. Current Biology, 23(15), 14491453. doi:10.1016/j.cub.2013.06.022Google Scholar
Santarnecchi, E., Rossi, S., & Rossi, A. (2015a). The smarter, the stronger: Intelligence level correlates with brain resilience to systematic insults. Cortex, 64, 293309. doi:10.1016/j.cortex.2014.11.005Google Scholar
Santarnecchi, E., Tatti, E., Rossi, S., Serino, V., & Rossi, A. (2015b). Intelligence-related differences in the asymmetry of spontaneous cerebral activity. Human Brain Mapping. doi:10.1002/hbm.22864Google Scholar
Sauce, B., & Matzel, L. D. (2013). The causes of variation in learning and behavior: Why individual differences matter. Frontiers in Psychology, 4.Google Scholar
Sauce, B., & Matzel, L. D. (2018). The paradox of intelligence: Heritability and malleability coexist in hidden gene-environment interplay. Psychology Bulletin, 144(1), 2647. doi:10.1037/bul0000131Google Scholar
Sauce, B., Bendrath, S., Herzfeld, M., Siegel, D., Style, C., Rab, S., … Matzel, L. D. (2018). The impact of environmental interventions among mouse siblings on the heritability and malleability of general cognitive ability. Philosophical Transactions of the Royal Society B-Biological Sciences, 373(1756). doi:ARTN 20170289Google Scholar
Sauce, B., Liebherr, M., Judd, N., & Klingberg, T. (2022). The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background. Scientific Reports, 12(1), 7720. doi:10.1038/s41598-022-11341-2Google Scholar
Sawyer, K. (2011). The cognitive neuroscience of creativity: A critical review. Creativity Research Journal, 23(2), 137–154.Google Scholar
Schaie, K. W. (1993). The Seattle Longitudinal Study: A thirty-five-year inquiry of adult intellectual development. Zeitschrift fur Gerontologie, 26(3), 129137. Retrieved from www.ncbi.nlm.nih.gov/pubmed/8337905Google Scholar
Schmidt, F. (2016). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 100 years of research findings.Google Scholar
Schmidt, F. L., & Hunter, J. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86(1), 162173. doi:10.1037/0022-3514.86.1.162Google Scholar
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262274.Google Scholar
Schmithorst, V. J., & Holland, S. K. (2006). Functional MRI evidence for disparate developmental processes underlying intelligence in boys and girls. Neuroimage, 31(3), 13661379.Google Scholar
Schmithorst, V. J., Wilke, M., Dardzinski, B. J., & Holland, S. K. (2005). Cognitive functions correlate with white matter architecture in a normal pediatric population: A diffusion tensor MRI study. Human Brain Mapping, 26(2), 139147.Google Scholar
Schubert, A. L., Hagemann, D., & Frischkorn, G. T. (2017). Is general intelligence little more than the speed of higher-order processing? Journal of Experimental Psychology-General, 146(10), 14981512.Google Scholar
Schubert, A. L., Hagemann, D., Frischkorn, G. T., & Herpertz, S. C. (2018). Faster, but not smarter: An experimental analysis of the relationship between mental speed and mental abilities. Intelligence, 71, 6675. doi:10.1016/j.intell.2018.10.005Google Scholar
Schubert, A.-L., & Frischkorn, G. T. (2020). Neurocognitive psychometrics of intelligence: How measurement advancements unveiled the role of mental speed in intelligence differences. Current Directions in Psychological Science, 29(2), 140146. doi:10.1177/0963721419896365Google Scholar
Schubert, A.-L., Hagemann, D., Löffler, C., & Frischkorn, G. T. (2020). Disentangling the effects of processing speed on the association between age differences and fluid intelligence. Journal of Intelligence, 8(1), 1. Retrieved from www.mdpi.com/2079-3200/8/1/1Google Scholar
Schubert, A.-L., Nunez, M. D., Hagemann, D., & Vandekerckhove, J. (2019). Individual differences in cortical processing speed predict cognitive abilities: A model-based cognitive neuroscience account. Computational Brain & Behavior, 2(2), 6484. doi:10.1007/s42113-018-0021-5Google Scholar
Schwaighofer, M., Fischer, F., & Buhner, M. (2015). Does working memory training transfer? A meta-analysis including training conditions as moderators. Educational Psychologist, 50(2), 138166.Google Scholar
Sellers, K. K., Mellin, J. M., Lustenberger, C. M., Boyle, M. R., Lee, W. H., Peterchev, A. V., & Frohlich, F. (2015). Transcranial direct current stimulation (tDCS) of frontal cortex decreases performance on the WAIS-IV intelligence test. Behavioral Brain Research, 290, 3244. doi:10.1016/j.bbr.2015.04.031Google Scholar
Shakeshaft, N. G., Trzaskowski, M., McMillan, A., Krapohl, E., Simpson, M. A., Reichenberg, A., … Plomin, R. (2015). Thinking positively: The genetics of high intelligence. Intelligence, 48, 123132. doi:10.1016/j.intell.2014.11.005Google Scholar
Shamay-Tsoory, S. G., Adler, N., Aharon-Peretz, J., Perry, D., & Mayseless, N. (2011). The origins of originality: The neural bases of creative thinking and originality. Neuropsychologia, 49(2), 178185. doi:10.1016/j.neuropsychologia.2010.11.020Google Scholar
Sharif, S., Guirguis, A., Fergus, S., & Schifano, F. (2021). The Use and Impact of Cognitive Enhancers among University Students: A Systematic Review. Brain Sciences, 11(3), 355. doi:10.3390/brainsci11030355Google Scholar
Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., … Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440(7084), 676679.Google Scholar
Shehzad, Z., Kelly, C., Reiss, P. T., Cameron Craddock, R., Emerson, J. W., McMahon, K., … Milham, M. P. (2014). A multivariate distance-based analytic framework for connectome-wide association studies. Neuroimage, 93 Pt 1, 7494. doi:10.1016/j.neuroimage.2014.02.024Google Scholar
Shi, M., Li, Y., Sun, J., Li, X., Han, Y., Liu, Z., & Qiu, J. (2022). Intelligence correlates with the temporal variability of brain networks. Neuroscience. doi:10.1016/j.neuroscience.2022.08.001Google Scholar
Shipstead, Z., Redick, T. S., & Engle, R. W. (2012). Is working memory training effective? Psychology Bulletin, 138(4), 628654. doi:10.1037/a0027473Google Scholar
Shonkoff, J. P., Phillips, D., & National Research Council (U.S.). Committee on Integrating the Science of Early Childhood Development. (2000). From Neurons to Neighborhoods : The Science of Early Childhood Development. Washington, DC: National Academy Press.Google Scholar
Siebner, H. R., Funke, K., Aberra, A. S., Antal, A., Bestmann, S., Chen, R., … Ugawa, Y. (2022). Transcranial magnetic stimulation of the brain: What is stimulated? – A consensus and critical position paper. Clinical Neurophysiology, 140, 5997. doi:10.1016/j.clinph.2022.04.022Google Scholar
Sigala, N. (2015). Effects of memory training or task design? A Commentary on “Neural evidence for the use of digit-image mnemonic in a superior memorist: An fMRI study.” Frontier in Human Neuroscience, 9, 183. doi:10.3389/fnhum.2015.00183Google Scholar
Sigman, M., Pena, M., Goldin, A. P., & Ribeiro, S. (2014). Neuroscience and education: Prime time to build the bridge. Nature Neuroscience, 17(4), 497502. doi:10.1038/nn.3672Google Scholar
Silverman, P. H. (2004). Rethinking genetic determinism. The Scientist, 18(10), 3233.Google Scholar
Simos, P. G., Rezaie, R., Papanicolaou, A. C., & Fletcher, J. M. (2014). Does IQ affect the functional brain network involved in pseudoword reading in students with reading disability? A magnetoencephalography study. Frontier in Human Neuroscience, 7, 932. doi:10.3389/fnhum.2013.00932Google Scholar
Smith, M. E., & Farah, M. J. (2011). Are prescription stimulants “smart pills”? The epidemiology and cognitive neuroscience of prescription stimulant use by normal healthy individuals. Psychology Bulletin, 137(5), 717741. doi:10.1037/a0023825Google Scholar
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., … Miller, K. L. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18(11), 15651567. doi:10.1038/nn.4125Google Scholar
Sniekers, S., Stringer, S., Watanabe, K., Jansen, P. R., Coleman, J. R. I., Krapohl, E., … Posthuma, D. (2017). Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics, 49(7), 11071112. doi:10.1038/ng.3869Google Scholar
Snyderman, M., & Rothman, S. (1988). The IQ Controversy, the Media and Public Policy. New Brunswick, NJ, USA: Transaction Books.Google Scholar
Song, M., Liu, Y., Zhou, Y., Wang, K., Yu, C., & Jiang, T. (2009). Default network and intelligence difference. Conference Proceedings of the IEEE Engineering in Medicine and Biological Society, 2009, 22122215. doi:10.1109/IEMBS.2009.5334874Google 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. doi:10.1016/j.neuroimage.2008.02.036Google Scholar
Sonmez, A. I., Camsari, D. D., Nandakumar, A. L., Voort, J. L. V., Kung, S., Lewis, C. P., & Croarkin, P. E. (2019). Accelerated TMS for Depression: A systematic review and meta-analysis. Psychiatry Research, 273, 770781. doi:10.1016/j.psychres.2018.12.041Google Scholar
Soreq, E., Violante, I. R., Daws, R. E., & Hampshire, A. (2021). Neuroimaging evidence for a network sampling theory of individual differences in human intelligence test performance. Nature Communication, 12(1), 2072. doi:10.1038/s41467-021-22199-9Google Scholar
Spearman, C. (1904). General intelligence objectively determined and measured. American Journal of Psychology, 15, 201293.Google Scholar
Sripada, C., Angstadt, M., Rutherford, S., Taxali, A., & Shedden, K. (2020). Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain. Hum Brain Mapp 41(12): 31863197.Google Scholar
Stam, C. J., & Reijneveld, J. C. (2007). Graph theoretical analysis of complex networks in the brain. Nonlinear Biomedical Physics, 1(1), 3. doi:10.1186/1753-4631-1-3Google Scholar
Stammen, C., Fraenz, C., Grazioplene, R. G., Schlüter, C., Merhof, V., Johnson, W., … Genç, E. (2022). Robust associations between white matter microstructure and general intelligence. bioRxiv, 2022.2005.2002.490274. doi:10.1101/2022.05.02.490274Google Scholar
Stanley, J., Keating, D. P., and Fox, L. H. (1974). Mathematical Talent: Discovery, Description, and Development. Baltimore: The Johns Hopkins University Press.Google Scholar
Stein, J. L., Medland, S. E., Vasquez, A. A., Hibar, D. P., Senstad, R. E., Winkler, A. M., … Enhancing Neuro Imaging Genetics through Meta-Analysis, C. (2012). Identification of common variants associated with human hippocampal and intracranial volumes. Nature Genetics, 44(5), 552561. doi:10.1038/ng.2250Google Scholar
Sternberg, R. J. (2000). Practical Intelligence in Everyday Life. Cambridge, UK; New York: Cambridge University Press.Google Scholar
Sternberg, R. J. (2003). Our research program validating the triarchic theory of successful intelligence: Reply to Gottfredson. Intelligence, 31(4), 399413.Google Scholar
Sternberg, R. J. (2008). Increasing fluid intelligence is possible after all. Proceedings of the National Academy Science USA, 105(19), 67916792. doi:10.1073/pnas.0803396105Google Scholar
Sternberg, R. J. (2014). Teaching about the nature of intelligence. Intelligence, 42, 176179. doi:10.1016/j.intell.2013.08.010Google Scholar
Sternberg, R. J. (2018). The Nature of Human Intelligence (Sternberg, R. J. Ed.). New York: Cambridge University Press.Google Scholar
Strenze, T. (2007). Intelligence and socioeconomic success: A meta-analytic review of longitudinal research. Intelligence, 35(5), 401426. doi:10.1016/j.intell.2006.09.004Google Scholar
Suthana, N., & Fried, I. (2014). Deep brain stimulation for enhancement of learning and memory. Neuroimage, 85 Pt 3, 996–1002. doi:10.1016/j.neuroimage.2013.07.066Google Scholar
Tammet, D. (2007). Born on a blue day : Inside the extraordinary mind of an autistic savant : A memoir (1st Free Press pbk. ed.). New York: Free Press.Google Scholar
Tang, C. Y., Eaves, E. L., Ng, J. C., Carpenter, D. M., Mai, X., Schroeder, D. H., … Haier, R. J. (2010). Brain networks for working memory and factors of intelligence assessed in males and females with fMRI and DTI. Intelligence, 38(3), 293303.Google Scholar
Tang, Y. P., Shimizu, E., Dube, G. R., Rampon, C., Kerchner, G. A., Zhuo, M., … Tsien, J. Z. (1999). Genetic enhancement of learning and memory in mice. Nature, 401(6748), 6369.CrossRefGoogle ScholarPubMed
te Nijenhuis, J., Jongeneel-Grimen, B., & Kirkegaard, E. O. W. (2014). Are Headstart gains on the g factor? A meta-analysis. Intelligence, 46, 209215.CrossRefGoogle Scholar
Tellier, L. C. A. M., Eccles, J., Treff, N. R., Lello, L., Fishel, S., & Hsu, S. (2021). Embryo screening for polygenic disease risk: Recent advances and ethical considerations. Genes, 12(8), 1105. Retrieved from www.mdpi.com/2073-4425/12/8/1105Google Scholar
Terman, L. M. (1925). Genetic Studies of Genius. Stanford, CA: Stanford University Press.Google Scholar
Terman, L. M. (1954). Scientists and Nonscientists in a Group of 800 Gifted Men. Washington, DC: American Psychological Association.Google Scholar
Thiele, J. A., Faskowitz, J., Sporns, O., & Hilger, K. (2022). Multitask brain network reconfiguration is inversely associated with human intelligence. Cerebral Cortex., 32(19), 41724182. doi:10.1093/cercor/bhab473Google Scholar
Thoma, R. J., Yeo, R. A., Gangestad, S., Halgren, E., Davis, J., Paulson, K. M., & Lewine, J. D. (2006). Developmental instability and the neural dynamics of the speed-intelligence relationship. Neuroimage, 32(3), 14561464.CrossRefGoogle ScholarPubMed
Thomas, P., Rammsayer, T., Schweizer, K., & Troche, S. (2015). Elucidating the functional relationship between working memory capacity and psychometric intelligence: A fixed-links modeling approach for experimental repeated-measures designs. Advances in Cognitive Psychology, 11(1), 313.Google Scholar
Thompson, P. M., Cannon, T. D., Narr, K. L., van Erp, T., Poutanen, V. P., Huttunen, M., … Toga, A. W. (2001). Genetic influences on brain structure. Nature Neuroscience, 4(12), 12531258.Google Scholar
Thompson, R., Crinella, F. M., & Yu, J. (1990). Brain Mechanisms in Problem Solving and Intelligence: A Survey of the Rat Brain. New York: Plenum Press.Google Scholar
Thompson, T. W., Waskom, M. L., Garel, K. L., Cardenas-Iniguez, C., Reynolds, G. O., Winter, R., … Gabrieli, J. D. (2013). Failure of working memory training to enhance cognition or intelligence. Plos One, 8(5), e63614. doi:10.1371/journal.pone.0063614Google Scholar
Thurstone, L. L. (1938). Primary Mental Abilities. Chicago, IL: University of Chicago Press.Google Scholar
Thurstone, L. L., & Thurstone, T. (1941). Factorial Studies of Intelligence. Chicago, IL: University of Chicago Press.Google Scholar
Tidwell, J. W., Dougherty, M. R., Chrabaszcz, J. R., Thomas, R. P., & Mendoza, J. L. (2013). What counts as evidence for working memory training? Problems with correlated gains and dichotomization. Psychonomic Bulletin Review. doi:10.3758/s13423-013-0560-7Google Scholar
Toga, A. W., & Thompson, P. M. (2005). Genetics of brain structure and intelligence. Annual Review of Neuroscience, 28, 123.Google Scholar
Tommasi, M., Pezzuti, L., Colom, R., Abad, F. J., Saggino, A., & Orsini, A. (2015). Increased educational level is related with higher IQ scores but lower g-variance: Evidence from the standardization of the WAIS-R for Italy. Intelligence, 50, 6874.Google Scholar
Trahan, L. H., Stuebing, K. K., Fletcher, J. M., & Hiscock, M. (2014). The Flynn effect: A meta-analysis. Psychological Bulletin, 140(5), 13321360. doi:10.1037/a0037173Google Scholar
Troller-Renfree, S. V., Costanzo, M. A., Duncan, G. J., Magnuson, K., Gennetian, L. A., Yoshikawa, H., … Noble, K. G. (2022). The impact of a poverty reduction intervention on infant brain activity. Proceedings of the National Academy of Sciences, 119(5), e2115649119. doi:10.1073/pnas.2115649119Google Scholar
Trzaskowski, M., Davis, O. S. P., DeFries, J. C., Yang, J., Visscher, P. M., & Plomin, R. (2013). DNA evidence for strong genome-wide pleiotropy of cognitive and learning abilities. Behavior Genetics, 43(4), 267273.Google Scholar
Trzaskowski, M., Harlaar, N., Arden, R., Krapohl, E., Rimfeld, K., McMillan, A., … Plomin, R. (2014). Genetic influence on family socioeconomic status and children’s intelligence. Intelligence, 42(100), 8388. doi:10.1016/j.intell.2013.11.002CrossRefGoogle ScholarPubMed
Trzaskowski, M., Shakeshaft, N. G., & Plomin, R. (2013). Intelligence indexes generalist genes for cognitive abilities. Intelligence, 41(5), 560565.Google Scholar
Tsukahara, J. S., & Engle, R. W. (2021). Fluid intelligence and the locus coeruleus-norepinephrine system. Proceedings of the National Academy Science USA, 118(46). doi:10.1073/pnas.2110630118Google Scholar
Turkheimer, E. (2000). Three laws of behavior genetics and what they mean. Current Directions in Psychological Science, 9(5), 160164.Google Scholar
Turkheimer, E., Haley, A., Waldron, M., D’Onofrio, B., & Gottesman, II. (2003). Socioeconomic status modifies heritability of IQ in young children. Psychological Science, 14(6), 623628.Google Scholar
Turley, P., Meyer, M. N., Wang, N., Cesarini, D., Hammonds, E., Martin, A. R., … Visscher, P. M. (2021). Problems with Using Polygenic Scores to Select Embryos. New England Journal of Medicine, 385(1), 7886. doi:10.1056/NEJMsr2105065Google Scholar
Ukkola-Vuoti, L., Kanduri, C., Oikkonen, J., Buck, G., Blancher, C., Raijas, P., … Jarvela, I. (2013). Genome-wide copy number variation analysis in extended families and unrelated individuals characterized for musical aptitude and creativity in music. Plos One, 8(2), e56356. doi:10.1371/journal.pone.0056356Google Scholar
Unsworth, N., Redick, T. S., McMillan, B. D., Hambrick, D. Z., Kane, M. J., & Engle, R. W. (2015). Is playing video games related to cognitive abilities? Psychological Science, 26(6), 759774. doi:10.1177/0956797615570367Google Scholar
Urban, D. J., & Roth, B. L. (2015). DREADDs (designer receptors exclusively activated by designer drugs): Chemogenetic tools with therapeutic utility. Annual Review of Pharmacological Toxicology, 55, 399417. doi:10.1146/annurev-pharmtox-010814-124803Google Scholar
Utz, K. S., Dimova, V., Oppenlander, K., & Kerkhoff, G. (2010). Electrified minds: Transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non-invasive brain stimulation in neuropsychology – a review of current data and future implications. Neuropsychologia, 48(10), 27892810. doi:10.1016/j.neuropsychologia.2010.06.002Google Scholar
Vakhtin, A. A., Ryman, S. G., Flores, R. A., & Jung, R. E. (2014). Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence. Neuroimage, 103, 349354. doi:10.1016/j.neuroimage.2014.09.055Google Scholar
van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. Journal of Neuroscience, 31(44), 1577515786. doi:10.1523/JNEUROSCI.3539-11.2011CrossRefGoogle ScholarPubMed
van den Heuvel, M. P., Kahn, R. S., Goni, J., & Sporns, O. (2012). High-cost, high-capacity backbone for global brain communication. Proceedings of the National Academy Science USA, 109(28), 1137211377. doi:10.1073/pnas.1203593109Google Scholar
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Pol, H. E. H. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29(23), 76197624. doi:10.1523/jneurosci.1443-09.2009Google Scholar
van der Linden, D., Dunkel, C. S., & Madison, G. (2017). Sex differences in brain size and general intelligence (g). Intelligence, 63, 7888. doi:10.1016/j.intell.2017.04.007Google Scholar
van der Maas, H. L. J., Snoek, L., & Stevenson, C. E. (2021). How much intelligence is there in artificial intelligence? A 2020 update. Intelligence, 87, 101548. DOI: 10.1016/j.intell.2021.101548Google Scholar
van der Sluis, S., Willemsen, G., de Geus, E. J. C., Boomsma, D. I., & Posthuma, D. (2008). Gene-environment interaction in adults’ IQ scores: Measures of past and present environment. Behavior Genetics, 38(4), 348360.Google Scholar
van Leeuwen, M., van den Berg, S. M., & Boomsma, D. I. (2008). A twin-family study of general IQ. Learning and Individual Differences, 18(1), 7688. doi:10.1016/j.lindif.2007.04.006Google Scholar
Vardy, E., Robinson, J. E., Li, C., Olsen, R. H., DiBerto, J. F., Giguere, P. M., … Roth, B. L. (2015). A new DREADD facilitates the multiplexed chemogenetic interrogation of behavior. Neuron, 86(4), 936946. doi:10.1016/j.neuron.2015.03.065Google Scholar
Vendetti, M. S., & Bunge, S. A. (2014). Evolutionary and developmental changes in the lateral frontoparietal network: A little goes a long way for higher-level cognition. Neuron, 84(5), 906917. doi:10.1016/j.neuron.2014.09.035Google Scholar
Vernon, P. A. (1983). Speed of information processing and general intelligence. Intelligence, 7(1), 5370.Google Scholar
Vieira, B. H., Pamplona, G. S. P., Fachinello, K., Silva, A. K., Foss, M. P., & Salmon, C. E. G. (2022). On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting. Intelligence, 93, 101654. doi:10.1016/j.intell.2022.101654Google Scholar
Villarreal, M. F., Cerquetti, D., Caruso, S., Schwarcz Lopez Aranguren, V., Gerschcovich, E. R., Frega, A. L., & Leiguarda, R. C. (2013). Neural correlates of musical creativity: Differences between high and low creative subjects. Plos One, 8(9), e75427. doi:10.1371/journal.pone.0075427Google Scholar
Visscher, P. M. (2022). Genetics of cognitive performance, education and learning: From research to policy? npj Science of Learning, 7(1), 8. doi:10.1038/s41539-022-00124-zGoogle Scholar
von Bastian, C. C., & Oberauer, K. (2013). Distinct transfer effects of training different facets of working memory capacity. Journal of Memory and Language, 69(1), 3658.Google Scholar
von Bastian, C. C., & Oberauer, K. (2014). Effects and mechanisms of working memory training: A review. Psychological Research-Psychologische Forschung, 78(6), 803820.Google Scholar
von Stumm, S., & Deary, I. J. (2013). Intellect and cognitive performance in the Lothian Birth Cohort 1936. Psychology and Aging, 28(3), 680684. doi:10.1037/A0033924Google Scholar
von Stumm, S., & Plomin, R. (2021). Using DNA to predict intelligence. Intelligence, 86, 101530. doi:10.1016/j.intell.2021.101530Google Scholar
Vuoksimaa, E., Panizzon, M. S., Chen, C. H., Fiecas, M., Eyler, L. T., Fennema-Notestine, C., … Kremen, W. S. (2015). The genetic association between neocortical volume and general cognitive ability is driven by global surface area rather than thickness. Cerebral Cortex, 25(8), 21272137. doi:10.1093/cercor/bhu018Google Scholar
Wagner, T., Robaa, D., Sippl, W., & Jung, M. (2014). Mind the methyl: Methyllysine binding proteins in epigenetic regulation. ChemMedChem, 9(3), 466483. doi:10.1002/cmdc.201300422Google Scholar
Wai, J., & Bailey, D. H. (2021). How intelligence research can inform education and public policy. In Barbey, A., Karama, S., & Haier, R. J. (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience. New York: Cambridge University Press.Google Scholar
Wai, J., & Worrell, F. C. (2021). The future of intelligence research and gifted education. Intelligence, 87, 101546. doi:10.1016/j.intell.2021.101546Google Scholar
Wai, J., Brown, M., & Chabris, C. F. (2019). No one likes the SAT. It’s still the fairest thing about admissions. In. Washington Post editorial.Google Scholar
Wai, J., Lubinski, D., & Benbow, C. P. (2005). Creativity and occupational accomplishments among intellectually precocious youths: An age 13 to age 33 longitudinal study. Journal of Educational Psychology, 97(3), 484492.Google Scholar
Walfisch, A., Sermer, C., Cressman, A., & Koren, G. (2013). Breast milk and cognitive development – the role of confounders: A systematic review. BMJ Open, 3(8), e003259. doi:10.1136/bmjopen-2013-003259Google Scholar
Wang, C., Jaeggi, S. M., Yang, L., Zhang, T., He, X., Buschkuehl, M., & Zhang, Q. (2019). Narrowing the achievement gap in low-achieving children by targeted executive function training. Journal of Applied Developmental Psychology, 63, 8795. doi:10.1016/j.appdev.2019.06.002Google Scholar
Wang, L., Wee, C. Y., Suk, H. I., Tang, X., & Shen, D. (2015). MRI-Based Intelligence Quotient (IQ) estimation with sparse learning. Plos One, 10(3), e0117295. doi:10.1371/journal.pone.0117295Google Scholar
Warne, R. T. (2019). An evaluation (and Vindication?) of Lewis Terman: What the father of gifted education can teach the 21st century. Gifted Child Quarterly, 63(1), 321.Google Scholar
Warne, R. T. (2020). In the Know : Debunking 35 Myths about Human Intelligence. Cambridge, United Kingdom; New York, NY: Cambridge University Press.Google Scholar
Waterhouse, L. (2006). Inadequate evidence for multiple intelligences, Mozart effect, and emotional intelligence theories. Educational Psychologist, 41(4), 247255. doi:10.1207/s15326985ep4104_5Google Scholar
Watrin, L., Hulur, G., & Wilhelm, O. (2022). Training working memory for two years-No evidence of transfer to intelligence. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48(5), 717733. doi:10.1037/xlm0001135Google ScholarPubMed
Watson, J. B. (1930). Behaviorism. New York,: W.W. Norton & Company.Google Scholar
Wax, A. L. (2017). The poverty of the neuroscience of poverty: Policy payoff or false promise? Jurimetrics, 57(2), 239287. Retrieved from www.jstor.org/stable/26322667Google Scholar
Weiss, D., Haier, R., & Keating, D. (1974). Personality characteristics of mathematically precocious boys. In Stanley, Keating, & Fox (Eds.), Mathematical Talent: Discovery, Description, and Development (pp. 126139). Baltimore, MD: The Johns Hopkins University Press.Google Scholar
Wendelken, C., Ferrer, E., Whitaker, K. J., & Bunge, S. A. (2015). Fronto-parietal network reconfiguration supports the development of reasoning ability. Cerebral Cortex. doi:10.1093/cercor/bhv050Google ScholarPubMed
Whalley, L. J., & Deary, I. J. (2001). Longitudinal cohort study of childhood IQ and survival up to age 76. Bmj, 322(7290), 819. Retrieved from www.ncbi.nlm.nih.gov/pubmed/11290633Google Scholar
Wharton, C. M., Grafman, J., Flitman, S. S., Hansen, E. K., Brauner, J., Marks, A., & Honda, M. (2000). Toward neuroanatomical models of analogy: A positron emission tomography study of analogical mapping. Cognitive Psychology, 40(3), 173197.Google Scholar
Widge, A. S., Zorowitz, S., Basu, I., Paulk, A. C., Cash, S. S., Eskandar, E. N., … Dougherty, D. D. (2019). Deep brain stimulation of the internal capsule enhances human cognitive control and prefrontal cortex function. Nature Communications, 10(1), 1536. doi:10.1038/s41467-019-09557-4Google Scholar
Wiemers, E. A., Redick, T. S., & Morrison, A. B. (2019). The influence of individual differences in cognitive ability on working memory training gains. Journal of Cognition Enhancement, 3(2), 174185. doi:10.1007/s41465-018-0111-2Google Scholar
Wilke, M., Sohn, J. H., Byars, A. W., & Holland, S. K. (2003). Bright spots: Correlations of gray matter volume with IQ in a normal pediatric population. Neuroimage, 20(1), 202215.Google Scholar
Willerman, L., Schultz, R., Rutledge, J. N., & Bigler, E. D. (1991). In vivo brain size and intelligence. Intelligence, 15(2), 223228.Google Scholar
Willoughby, E. A., McGue, M., Iacono, W. G., & Lee, J. J. (2021). Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring. Intelligence, 88. doi:10.1016/j.intell.2021.101579Google Scholar
Wilson, E. O. (1975). Sociobiology: The New Synthesis. Cambridge: Belknap Press of Harvard Univ. Press.Google Scholar
Witelson, S. F., Beresh, H., & Kigar, D. L. (2006). Intelligence and brain size in 100 postmortem brains: Sex, lateralization and age factors. Brain, 129(Pt 2), 386398. doi:10.1093/brain/awh696Google Scholar
Witelson, S. F., Kigar, D. L., & Harvey, T. (1999a). Albert Einstein’s brain – Reply. Lancet, 354(9192), 18221822.Google Scholar
Witelson, S. F., Kigar, D. L., & Harvey, T. (1999b). The exceptional brain of Albert Einstein. Lancet, 353(9170), 21492153.Google Scholar
Wolff, S. B., Grundemann, J., Tovote, P., Krabbe, S., Jacobson, G. A., Muller, C., … Luthi, A. (2014). Amygdala interneuron subtypes control fear learning through disinhibition. Nature, 509(7501), 453458. doi:10.1038/nature13258Google Scholar
Wooldridge, A. (2021). The Aristocracy of Talent: How Meritocracy Made the Modern World. New York: Skyhorse Publishing.Google Scholar
Wu, X., Yang, W., Tong, D., Sun, J., Chen, Q., Wei, D., … Qiu, J. (2015). A meta-analysis of neuroimaging studies on divergent thinking using activation likelihood estimation. Human Brain Mapping, 36(7), 27032718. doi:10.1002/hbm.22801Google Scholar
Yang, J. J., Yoon, U., Yun, H. J., Im, K., Choi, Y. Y., Lee, K. H., … Lee, J. M. (2013). Prediction for human intelligence using morphometric characteristics of cortical surface: Partial least square analysis. Neuroscience, 246, 351361. doi:10.1016/j.neuroscience.2013.04.051Google Scholar
Yin, L. J., Lou, Y. T., Fan, M. X., Wang, Z. X., & Hu, Y. (2015). Neural evidence for the use of digit-image mnemonic in a superior memorist: an fMRI study. Frontier in Human Neuroscience, 9, 109. doi:10.3389/fnhum.2015.00109Google Scholar
Yu, C. C., Furukawa, M., Kobayashi, K., Shikishima, C., Cha, P. C., Sese, J., … Toda, T. (2012). Genome-wide DNA methylation and gene expression analyses of monozygotic twins discordant for intelligence levels. Plos One, 7(10).Google Scholar
Zhao, M., Kong, L., & Qu, H. (2014). A systems biology approach to identify intelligence quotient score-related genomic regions, and pathways relevant to potential therapeutic treatments. Scientific Reports, 4, 4176. doi:10.1038/srep04176Google Scholar
Zhao, T., Zhu, Y., Tang, H., Xie, R., Zhu, J., & Zhang, J. H. (2019). Consciousness: New concepts and neural networks. Frontiers in Cellular Neuroscience, 13. doi:10.3389/fncel.2019.00302Google Scholar
Zisman, C., & Ganzach, Y. (2022). The claim that personality is more important than intelligence in predicting important life outcomes has been greatly exaggerated. Intelligence, 92, 101631. doi:10.1016/j.intell.2022.101631Google Scholar

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  • Book: The Neuroscience of Intelligence
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  • Book: The Neuroscience of Intelligence
  • Online publication: 13 July 2023
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