Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-26T09:23:59.310Z Has data issue: false hasContentIssue false

Part V - Translating Research on the Neuroscience of Intelligence into Action

Published online by Cambridge University Press:  11 June 2021

Edited by
Aron K. Barbey ,
Sherif Karama  and
Richard J. Haier
Aron K. Barbey
Affiliation:
University of Illinois, Urbana-Champaign
Sherif Karama
Affiliation:
McGill University, Montréal
Richard J. Haier
Affiliation:
University of California, Irvine
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Cambridge Handbook of Intelligence and Cognitive Neuroscience , pp. 365 - 468
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Adam, D. (ed.) (2018). The genius within: Smart pills, brain hacks and adventures in intelligence. London: Picador.Google Scholar
Albensi, B.C., Oliver, D. R., Toupin, J., & Odero, G. (2007). Electrical stimulation protocols for hippocampal synaptic plasticity and neuronal hyper-excitability: Are they effective or relevant? Experimental Neurology, 204, 1–13.Google Scholar
Anderson, J. R., Fincham, J. M., & Douglass, S. (1999). Practice and retention: A unifying analysis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(5), 1120–1136.Google ScholarPubMed
Baniqued, P., Kranz, M. B., Voss, M. W., Lee, H., Casman, J. D., Severson, J., & Kramer, A. F. (2014). Cognitive training with casual video games: Points to consider. Frontiers in Psychology, 4, 1010.Google Scholar
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20.Google Scholar
Bavelier, D., Green, C. S., Pouget, A., & Schrater, P. (2012). Brain plasticity through the life span: Learning to learn and action video games. Annual Reviews of Neuroscience, 35, 391–416.Google Scholar
Berman, M. G., Jonides, J., & Kaplan, S. (2008). The cognitive benefits of interacting with nature. Psychological Science, 19(12), 1207–1212. doi: 10.1111/j.1467-9280.2008.02225.x.Google Scholar
Bourrier, S. C., Berman, M. G., & Enns, J. T. (2018). Cognitive strategies and natural environments interact in influencing executive function. Frontiers in Psychology, 9, 1248. doi: 10.3389/fpsyg.2018.01248.CrossRefGoogle ScholarPubMed
Cohen-Kadosh, R. (ed.) (2014). The stimulated brain. Amsterdam: ElsevierGoogle Scholar
Daugherty, A. M., Zwillinga, C., Paula, E. J., Sherepaa, N., Allena, C., Kramer, A. F., … Barbey, A. K. (2018). Multi-modal fitness and cognitive training to enhance fluid intelligence. Intelligence, 66, 32–43. doi: 10.1016/j.intell.2017.11.001.Google Scholar
Diamond, A., & Lee, K. (2011). Interventions shown to aid executive function development in children 4–12 years old. Science, 333(6054), 959–964.CrossRefGoogle Scholar
Fan, J., McCandliss, B. D., Fossella, J., Flombaum, J. I., & Posner, M. I. (2005). The activation of attentional networks. Neuroimage, 26(2), 471–479.Google Scholar
Fan, J., McCandliss, B. D., Sommer, T., Raz, M. & Posner, M. I. (2002). Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 3(14), 340–347.CrossRefGoogle Scholar
Farah, M. J. (2015). The unknowns of cognitive enhancement. Science, 379(3), 379–380.Google Scholar
Fitts, P. M., & Posner, M. I. (1967). Human performance. Belmont, CA: Wadsworth.Google Scholar
Gallen, C. L., & D’Esposito, M. (2019). Brain modularity: A biomarker of intervention-related plasticity. Trends in Cognitive Science, 23(4), 293–304.Google Scholar
Green, C. S., & Bavelier, D. (2003). Action video games modify visual selective attention. Nature, 423(6939), 534–537.CrossRefGoogle ScholarPubMed
Green, C. S., & Bavelier, D. (2008). Exercising your brain: A review of human brain plasticity and training-induced learning. Psychology and Aging, 23(4), 692–701.Google Scholar
Green, C. S., Sugarman, M. A., Medford, K., Klobusicky, E., & Bavelier, D. (2012). The effect of action video game experience ontask-switching. Computers in Human Behavior, 28(3), 984–994.CrossRefGoogle 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), 2409–2419.CrossRefGoogle Scholar
Heathcote, A., Brown, S., & Mewhort, D. J. K. (2000) The power law repealed: The case for an exponential law of practice. Psychonomic Bulletin & Review, 7(2), 185–207.Google Scholar
Hillman, C. H., Erickson, K. I., & Kramer, A. F. (2008). Be smart, exercise your heart: Exercise effects on brain and cognition. Nature Reviews Neuroscience, 9(1), 58–65.Google Scholar
Horvath, J. C., Forte, J. D., & Carter, O. (2015). Quantitative review finds no evidence of cognitive effects in healthy populations from single-session transcranial direct current stimulation (tDCS). Brain Stimulation, 8(3), 535–550.Google Scholar
Husain, M., & Mehta, M. A. (2011). Cognitive enhancement by drugs in health and disease. Trends in Cognitive Science, 15(1), 28–36.Google Scholar
Jaeggi, A. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105(19), 196829–196833.Google Scholar
Klingberg, T., Forssberg, H., & Westerberg, H. (2002). Training of working memory in children with ADHD. Journal of Clinical and Experimental Neuropsychology, 24(6), 781–791.Google Scholar
Lynch, G. (1998). Memory and the brain: Unexpected chemistries and a new pharmacology. Neurobiology of Learning and Memory, 70(1–2), 82–100.Google Scholar
Mandolesi, L., Polverino, A., Monturori, S., Foti, F., Giampaolo, F. Sorrentino, P., & Sorrentino, G. (2018). Effects of physical exercise on cognitive functioning and wellbeing: Biological and psychological benefits. Frontiers in Psychology, 9, 509.Google Scholar
Melby-Lervag, M., Redick, T. S., & Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other mMeasures of “far transfer”: Evidence from a meta-analytic review. Perspectives on Psychological Science, 11(4), 512–534.Google Scholar
Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H. L., … Caspi, A. (2011). A gradient of childhood self-control predicts health, wealth and public safety. Proceedings of the National Academy of Sciences USA, 108(7), 2693–2698.Google Scholar
Neville, H. J., Stevens, C., Pakulak, E., Bell, T. A., Fanning, J., Klein, S., & Isbell, E. (2013). Family-based training program improves brain function, cognition, and behavior in lower socioeconomic status preschoolers. Proceedings of the National Academy of Sciences USA, 110(29), 12138–12143.Google Scholar
Peng, P., & Miller, A. C. (2016). Does attention training work? A selective meta-analysis to explore the effects of attention training and moderators. Learning and Individual Differences, 45, 77–87.Google Scholar
Petersen, S. E., & Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 71–89.CrossRefGoogle ScholarPubMed
Piscopo, D., Weible, A., Rothbart, M. K., Posner, M. I., & Niell, C. M. (2018). Changes in white matter in mice resulting from low frequency brain stimulation. Proceedings of the National Academy of Sciences USA, 115(27), 6639–6646. doi: 10.1073/pnas.1802160115.Google Scholar
Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42.Google Scholar
Posner, M. I., Tang, Y. Y., & Lynch, G. (2014) Mechanisms of white matter change induced by meditation. Frontiers in Psychology, 5, 1220. doi: 10.3389/fpsyg.2014.01220.Google Scholar
Redick, T. S. (2019) The hype cycle in working memory training. Current Directions in Psychological Science, 28(5), 1–7.Google Scholar
Reinhart, R. M. G. (2017). Disruption and rescue of interareal theta phase coupling and adaptive behavior. Proceedings of the National Academy of Sciences USA, 114(43), 11542–11547.Google Scholar
Reinhart, R. M. G., & Woodman, G. F. (2015). Enhancing long-term memory with stimulation tunes visual attention in one trial. Proceedings of the National Academy of Sciences USA, 112(2), 625–630.Google Scholar
Reinhart, R. M. G., Zhu, J., Park, S., & Woodman, G. F. (2015). Synchronizing theta oscillations with direct-current stimulation strengthens adaptive control in the human brain. Proceedings of the National Academy of Sciences USA, 112(30), 9448–9453.Google Scholar
Rinne, P., Hassan, M., Goniotakis, D., Chohan, K., Sharma, P., Langdon, D., … Bentley, P. (2013). Triple dissociation of attention networks in stroke according to lesion location. Neurology, 81(9), 812–820.Google Scholar
Roberts, B. M., Clarke, A., Addante, R. J., & Ranganath, C. (2018). Entrainment enhances theta oscillations and improves episodic memory. Cognitive Neuroscience, 9(3–4), 181–193.CrossRefGoogle ScholarPubMed
Rueda, M. R., Checa, P., & Combita, L. M. (2012). Enhanced efficiency of the executive attention network after training in preschool children: Immediate and after two month effects. Developemental Cognitive Neuroscience, 2(Supp 1), S192–S204.CrossRefGoogle Scholar
Rueda, M. R., Rothbart, M. K., McCandliss, B., Saccamanno, L., & Posner, M. I. (2005). Training, maturation and genetic influences on the development of executive attention. Proceedings of the National Academy of Sciences USA, 102(41), 14931–14936.Google 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, 171–178.CrossRefGoogle Scholar
Sasaki, S. R., Tsuiki, S., Miyaguchi, S., Kojima, S., Masaki, M., Otsuru, N., & Onishi, H. (2016). Comparison of three non-invasive transcranial electrical stimulation methods for increasing cortical excitability. Fontiers in Human Neuroscience, 10, 668. doi: 10.3389/fnhum.2016.00668.Google Scholar
Simons, D. J. , Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “brain-training” programs work? Psychological Science in the Public Interest, 17(3), 103–186.CrossRefGoogle Scholar
Sohlberg, M. M., & Mateer, C. A. (2001). Cognitive rehabilitation: An integrative neuropsychological approach. New York: Guilford.Google Scholar
Sohlberg, M. M., McLaughlin, K. A., Pavese, A., Heidrich, A., & Posner, M. I. (2000). Evaluation of attention process therapy training in persons with acquired brain injury. Journal of Clinical and Experimental Neuropsychology, 22(5), 656–676.CrossRefGoogle ScholarPubMed
Tang, Y. Y., Holzel, B. K., & Posner, M. I. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 16(5), 213–225.CrossRefGoogle Scholar
Tang, Y., Lu, Q., Geng, X., Stein, E. A., Yang, Y., & Posner, M. I. (2010) Short term mental training induces white-matter changes in the anterior cingulate. Proceedings of the National Academy of Sciences USA, 107(35), 15649–15652.Google Scholar
Tang, Y. Y., Ma, Y., Wang, J., Fan, Y., Feng, S., Lu, Q., … Posner, M. I. (2007). Short term meditation training improves attention and self regulation. Proceedings of the National Academy of Sciences USA, 104(43), 17152–17156.CrossRefGoogle ScholarPubMed
Tang, Y. Y., & Posner, M. I. (2009). Attention training and attention state training. Trends in Cognitive Science, 13(5), 222–227.CrossRefGoogle ScholarPubMed
Tang, Y. Y., & Posner, M. I. (2014). Training brain networks and states. Trends in Cognitive Science, 18(7), 345–350. doi: 10.1016/j.tics.2014.04.002.Google Scholar
Tang, Y. Y., Tang, R., & Posner, M. I. (2013). Brief meditation training induces smoking reduction. Proceedings of the National Academy of Sciences USA, 110(34), 13971–13975.Google Scholar
Thimm, M., Fink, G. R., Kust, J., Karbe, H., Sturm, W. (2006). Impact of alertness training on spatial neglect: A behavioural and fMRI study. Neuropsychology, 44(7), 1230–1246.Google Scholar
Van Kessel, M. E., Geurts, A. C. H., Brouwer, W. H., & Fasotti, L. (2013). Visual scanning training for neglect after stroke with and without a computerized lane tracking dual task. Frontiers in Human Neuroscience, 7, 358.Google Scholar
Vinogradova, O. S., Kitchigina, V. F., Kudina, T. A., & Zenchenko, K. I. (1999). Spontaneous activity and sensory responses of hippocampal neurons during persistent theta-rhythm evoked by median raphe nucleus blockade in rabbit. Neuroscience, 94(3), 745–753. doi: 10.1016/S0306-4522(99)00253-5.Google Scholar
Voelker, P., Rothbart, M. K., & Posner, M. I. (2016) A polymorphism related to methylation influences attention during performance of speeded skills. AIMS Neuroscience, 3(1), 40–55.Google Scholar
Ward, N., Paul, E., Watson, P., Cook, G. E., Hillman, C. H., Cohen, N. J., … Barbey, A. K. (2017). Enhanced learning through multimodal training: Evidence from a comprehensive cognitive, physical fitness, and neuroscience intervention. Scientific Reports, 7, 5808 doi: 10.1038/s41598–017-06237-5.CrossRefGoogle ScholarPubMed
Willis, S. L.., Tennstedt, S. L., Mariske, M., Ball, K., Elias, J., Koepke, K. M., … Wright, E. (2006). Long-term effects of cognitive training on everyday functional outcomes in older adults. Journal of the American Medical Association, 296(23), 2805–2814.CrossRefGoogle ScholarPubMed
Zhang, Q., Wang, C. P., Zhao, Q. W., Yang, L., Buschkuehl, M., & Jaeggi, S. M. (2019). The malleability of executive function in early childhood: Effects of schooling and targeted training. Development Science, 22(2), e12748.Google Scholar
Zwilling, C. E., Daugherty, A. M., Hillman, C. H., Kramer, A. F., Cohen, N. J., & Barbey, A. K. (2018). Enhanced decision-making through multimodal training. NPJ Science of Learning, 4(1). doi: 10.1038/s41539–019-0049.Google Scholar

References

Arbula, S., Ambrosini, E., Della Puppa, A., De Pellegrin, S., Anglani, M., Denaro, L., … Vallesi, A. (2020). Focal left prefrontal lesions and cognitive impairment: A multivariate lesion-symptom mapping approach. Neuropsychologia, 136, 107253. doi: 10.1016/j.neuropsychologia.2019.107253.Google Scholar
Axelrod, B. N., Vanderploeg, R. D., & Schinka, J. A. (1999). Comparing methods for estimating premorbid intellectual functioning. Archives of Clinical Neuropsychology, 14(4), 341–346. doi: 10.1016/S0887–6177(98)00028-6.CrossRefGoogle ScholarPubMed
Aziz-Zadeh, L., Kaplan, J. T., & Iacoboni, M. (2009). “Aha!”: The neural correlates of verbal insight solutions. Human Brain Mapping, 30(3), 908–916. doi: 10.1002/hbm.20554.Google Scholar
Barbey, A. K., Belli, A., Logan, A., Rubin, R., Zamroziewicz, M., & Operskalski, J. T. (2015). Network topology and dynamics in traumatic brain injury. Current Opinion in Behavioral Sciences, 4, 92–102.Google Scholar
Barbey, A. K., Colom, R., & Grafman, J. (2013). Dorsolateral prefrontal contributions to human intelligence. Neuropsychologia, 51(7), 1361–1369. doi: 10.1016/j.neuropsychologia.2012.05.017.CrossRefGoogle ScholarPubMed
Barbey, A. K., Colom, R., & Grafman, J. (2014). Distributed neural system for emotional intelligence revealed by lesion mapping. Social Cognitive and Affective Neuroscience, 9(3), 265–272. doi: 10.1093/scan/nss124.Google Scholar
Barbey, A. K., Colom, R., Paul, E. J., Chau, A., Solomon, J., & Grafman, J. H. (2014). Lesion mapping of social problem solving. Brain: A Journal of Neurology, 137(10), 2823–2833. doi: 10.1093/brain/awu207.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), e838733.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 & Function, 219(2), 485–494. doi: 10.1007/s00429–013-0512-z.CrossRefGoogle ScholarPubMed
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(4), 1154–1164. doi: 10.1093/brain/aws021.CrossRefGoogle ScholarPubMed
Barona, A., Reynolds, C. R., & Chastain, R. (1984). A demographically based index of premorbid intelligence for the WAIS-R. Journal of Consulting and Clinical Psychology, 52(5), 885.Google Scholar
Benedictus, M. R., Spikman, J. M., & van der Naalt, J. (2010). Cognitive and behavioral impairment in traumatic brain injury related to outcome and return to work. Archives of Physical Medicine and Rehabilitation, 91(9), 1436–1441.CrossRefGoogle ScholarPubMed
Bianchi, L. (1922). The mechanism of the brain and the function of the frontal lobes. Edinburgh: Livingstone. doi: 10.1192/bjp.68.283.402.Google Scholar
Blair, J. R., & Spreen, O. (1989). Predicting premorbid IQ: A revision of the National Adult Reading Test. The Clinical Neuropsychologist, 3(2), 129–136.Google Scholar
Boes, A. D., Prasad, S., Liu, H., Liu, Q., Pascual-Leone, A., Caviness, V. S. Jr, & Fox, M. D. (2015). Network localization of neurological symptoms from focal brain lesions. Brain, 138(10), 3061–3075.Google Scholar
Bright, P., & van der Linde, I. (2020). Comparison of methods for estimating premorbid intelligence. Neuropsychological Rehabilitation, 30(1), 1–14. doi: 10.1080/09602011.2018.1445650.CrossRefGoogle ScholarPubMed
Cohen-Zimerman, S., Kachian, Z. R., Krueger, F., Gordon, B., & Grafman, J. (2019). Childhood socioeconomic status predicts cognitive outcomes across adulthood following traumatic brain injury. Neuropsychologia, 124, 1–8. doi: 10.1016/j.neuropsychologia.2019.01.001.Google Scholar
Cohen-Zimerman, S., Salvi, C., Krueger, F., Gordon, B., & Grafman, J. (2018). Intelligence across the seventh decade in patients with brain injuries acquired in young adulthood. Trends in Neuroscience and Education, 13, 1–7. doi: 10.1016/j.tine.2018.08.001.Google Scholar
Colom, R., & Flores-Mendoza, C. E. (2007). Intelligence predicts scholastic achievement irrespective of SES factors: Evidence from Brazil. Intelligence, 35(3), 243–251. doi: 10.1016/j.intell.2006.07.008.Google Scholar
Crawford, J. R., Deary, I. J., Starr, J., & Whalley, L. J. (2001). The NART as an index of prior intellectual functioning: A retrospective validity study covering a 66-year interval. Psychological Medicine, 31(3), 451–458. doi: 10.1017/S0033291701003634.Google Scholar
Danek, A., & Salvi, C. (2018). Moment of truth: Why Aha! experiences are correct. Journal of Creative Behavior. doi: 10.1002/jocb.380.CrossRefGoogle 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), 457–460. doi: 10.1126/science.289.5478.457.CrossRefGoogle ScholarPubMed
Eimontaite, I., Goel, V., Raymont, V., Krueger, F., Schindler, I., & Grafman, J. (2018). Differential roles of polar orbital prefrontal cortex and parietal lobes in logical reasoning with neutral and negative emotional content. Neuropsychologia, 119, 320–329.Google Scholar
Gläscher, 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, 107(10), 4705–4709. Retrieved from www.pnas.org/content/107/10/4705.abstractGoogle Scholar
Goel, V., Grafman, J., Tajik, J., Gana, S., & Danto, D. (1997). A study of the performance of patients with frontal lobe lesions in a financial planning task. Brain, 120(10), 1805–1822. doi: 10.1093/brain/120.10.1805.CrossRefGoogle Scholar
Goel, V., Lam, E., Smith, K. W., Goel, A., Raymont, V., Krueger, F., & Grafman, J. (2017). Lesions to polar/orbital prefrontal cortex selectively impair reasoning about emotional material. Neuropsychologia, 99, 236–245.Google Scholar
Goel, V., Makale, M., & Grafman, J. (2004). The hippocampal system mediates logical reasoning about familiar spatial environments. Journal of Cognitive Neuroscience, 16(4), 654–664.Google Scholar
Goel, V., Marling, M., Raymont, V., Krueger, F., & Grafman, J. (2019). Patients with lesions to left prefrontal cortex (BA 9 and BA 10) have less entrenched beliefs and are more skeptical reasoners. Journal of Cognitive Neuroscience, 31(11), 1674–1688.Google Scholar
Goel, V., Stollstorff, M., Nakic, M., Knutson, K., & Grafman, J. (2009). A role for right ventrolateral prefrontal cortex in reasoning about indeterminate relations. Neuropsychologia, 47(13), 2790–2797.CrossRefGoogle ScholarPubMed
Gottfredson, L., & Saklofske, D. H. (2009). Intelligence: Foundations and issues in assessment. Canadian Psychology/Psychologie Canadienne, 50(3), 183.Google Scholar
Grafman, J., Jonas, B. S., Martin, A., Salazar, A. M., Weingartner, H., Ludlow, C., … Vance, S. C. (1988). Intellectual function following penetrating head-injury in Vietnam veterans. Brain, 111(1), 169–184.Google Scholar
Hamburg, S., Lowe, B., Startin, C. M., Padilla, C., Coppus, A., Silverman, W., … Strydom, A. (2019). Assessing general cognitive and adaptive abilities in adults with Down syndrome: A systematic review. Journal of Neurodevelopmental Disorders, 11(1), 20. doi: 10.1186/s11689–019-9279-8.Google Scholar
Harlow, J. M. (1868). Recovery from the passage of an iron bar through the head. Publications of the Massachusetts Medical Society, 2, 327–347. doi: 10.1177/0957154X9300401407.Google Scholar
Hillary, F. G., & Grafman, J. H. (2017). Injured brains and adaptive networks: The benefits and costs of hyperconnectivity. Trends in Cognitive Sciences, 21(5), 385–401. doi: 10.1016/j.tics.2017.03.003.CrossRefGoogle ScholarPubMed
Ip, R. Y., Dornan, J., & Schentag, C. (1995). Traumatic brain injury: Factors predicting return to work or school. Brain Injury, 9(5), 517–532. doi: 10.3109/02699059509008211.Google Scholar
Jouandet, M., & Gazzaniga, M. S. (1979). The frontal lobes. In Gazzaniga, M. S. (ed.), Neuropsychology. Handbook of behavioral neurobiology, vol 2 (pp. 25–59). Boston, MA: Springer. doi: 10.1007/978-1-4613-3944-1_2Google Scholar
Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154.Google Scholar
Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R., … Kounios, J. (2004). Neural activity when people solve verbal problems with insight. PLoS Biology, 2(4), 500–510. doi: 10.1371/journal.pbio.0020097.Google Scholar
Kesler, S. R., Adams, H. F., Blasey, C. M., & Bigler, E. D. (2003). Premorbid intellectual functioning, education, and brain size in traumatic brain injury: An investigation of the cognitive reserve hypothesis. Applied Neuropsychology, 10(3), 153–162.Google Scholar
Königs, M., Engenhorst, P. J., & Oosterlaan, J. (2016). Intelligence after traumatic brain injury: Meta-analysis of outcomes and prognosis. European Journal of Neurology, 23(1), 21–29. doi: 10.1111/ene.12719.Google Scholar
Kounios, J., & Beeman, M. (2014). The cognitive neuroscience of insight. Annual Review of Psychology, 65(1), 71–93. doi: 10.1146/annurev-psych-010213-115154.Google Scholar
Krueger, F., Pardini, M., Huey, E. D., Raymont, V., Solomon, J., Lipsky, R. H., … Grafman, J. (2011). The role of the Met66 brain-derived neurotrophic factor allele in the recovery of executive functioning after combat-related traumatic brain injury. The Journal of Neuroscience, 31(2), 598–606.Google Scholar
Laukkonen, R., Webb, M., Salvi, C., Tange, J., and Shooler, J. (2020). Eureka heuristics: How feelings of insight signal the quality of a new idea. PsyArXiv. doi: 10.31234/osf.io/ez3tn.Google Scholar
Luria, A. (1966). Higher cortical functions in man. Boston, MA: Springer. doi: 10.1007/978-1-4684-7741-2.Google Scholar
Mani, K., Cater, B., & Hudlikar, A. (2017). Cognition and return to work after mild/moderate traumatic brain injury: A systematic review. Work, 58(1), 51–62.CrossRefGoogle ScholarPubMed
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), 130.Google Scholar
Nunnari, D., Bramanti, P., & Marino, S. (2014). Cognitive reserve in stroke and traumatic brain injury patients. Neurological Sciences, 35(10), 1513–1518. doi: 10.1007/s10072–014-1897-z.Google Scholar
O’Connell, M. J. (2000). Prediction of return to work following traumatic brain injury: Intellectual, memory, and demographic variables. Rehabilitation Psychology, 45(2), 212–217. doi: 10.1037/0090-5550.45.2.212.CrossRefGoogle Scholar
Orme, D. R., Brehm, W., & Ree, M. J. (2001). Armed Forces Qualification Test as a measure of premorbid intelligence. Military Psychology, 13(4), 187–197. doi: 10.1207/S15327876MP1304_1.Google Scholar
Palmiero, C., Piccardi, M., Nori, L., Palermo, R., Salvi, L., & Guariglia, C. (2019). Creativity: Education and rehabilitation. Frontiers in Psychology, 10, 1500. doi: 10.3389/fpsyg.2019.01500.Google Scholar
Raymont, V., Greathouse, A., Reding, K., Lipsky, R., Salazar, A., & Grafman, J. (2008). Demographic, structural and genetic predictors of late cognitive decline after penetrating head injury. Brain, 131(2), 543–558. doi: 10.1093/brain/awm300.Google Scholar
Raymont, V., Salazar, A. M., Krueger, F., & Grafman, J. (2011). Studying injured minds – the Vietnam head injury study and 40 years of brain injury research. Frontiers in Neurology, 2, 15. doi: 10.3389/fneur.2011.00015Google Scholar
Ree, M. J., & Earles, J. A. (1992). Intelligence is the best predictor of job performance. Current Directions in Psychological Science, 1(3), 86–89.Google Scholar
Reverberi, C., Toraldo, A., D’Agostini, S., & Skrap, M. (2005). Better without (lateral) frontal cortex? Insight problems solved by frontal patients. Brain, 128(12), 2882–2890. doi: 10.1093/brain/awh577.Google Scholar
Rostami, E., Krueger, F., Zoubak, S., Dal Monte, O., Raymont, V., Pardini, M., … Grafman, J. (2011). BDNF polymorphism predicts general intelligence after penetrating traumatic brain injury. PLoS One, 6(11), e27389. doi: org/10.1371/journal.pone.0027389.Google Scholar
Salvi, C., Beeman, M., Bikson, M., McKinley, R., & Grafman, J. (2020). TDCS to the right anterior temporal lobe facilitates insight problem-solving. Science Reports, 10, 946. doi: 10.1038/s41598-020-57724-1.Google Scholar
Salvi, C., Bricolo, E., Franconeri, S., Kounios, J., & Beeman, M. (2015). Sudden insight is associated with shutting down visual inputs. Psychonomic Bulletin & Review, 22(6), 1814–1819. doi: 10.3758/s13423-015-0845-0.Google Scholar
Salvi, C., Bricolo, E., Kounios, J., Bowden, E. M., & Beeman, M. (2016). Insight solutions are correct more often than analytic solutions. Thinking & Reasoning, 22(4), 1–18. doi: 10.1080/13546783.2016.1141798.Google Scholar
Salvi, C., Simoncini, C., Grafman, J., & Beeman, M. (2020). Oculometric signature of switch into awareness? Pupil size predicts sudden insight whereas microsaccades predict problem-solving via analysis. Neuroimage, 217, 116933. doi: 10.1016/j.neuroimage.2020.116933.Google Scholar
Santarnecchi, E., Sprugnoli, G., Bricolo, E., Constantini, G., Liew, S. L., Musaeus, C. S., … Rossi, S. (2019). Gamma tACS over the temporal lobe increases the occurrence of Eureka! moments. Scientific Reports, 9, 5778. doi: 10.1038/s41598–019-42192-z.Google Scholar
Schoenberg, M. R., Lange, R. T., Brickell, T. A., & Saklofske, D. H. (2007). Estimating premorbid general cognitive functioning for children and adolescents using the American Wechsler Intelligence Scale for Children – Fourth edition: Demographic and current performance approaches. Journal of Child Neurology, 22(4), 379–388. doi: 10.1177/0883073807301925.Google Scholar
Schwab, K., Grafman, J., Salazar, A. M., & Kraft, J. (1993). Residual impairments and work status 15 years after penetrating head injury. Neurology, 43(1 Part 1), 95. doi: 10.1212/WNL.43.1_Part_1.95.Google Scholar
Shallice, T. (1988). From neuropsychology to mental structure. Cambridge University Press. doi: 10.1017/CBO9780511526817.Google Scholar
Shuren, J. E., & Grafman, J. (2002). The neurology of reasoning. Archives of Neurology, 59(6), 916–919.Google Scholar
Simon, H. A. (1973). The structure of ill structured problems. Artificial Intelligence, 4(3–4), 181–201. doi: 10.1016/0004-3702(73)90011-8.Google Scholar
Spikman, J. M., Timmerman, M. E., Milders, M. V, Veenstra, W. S., & van der Naalt, J. (2011). Social cognition impairments in relation to general cognitive deficits, injury severity, and prefrontal lesions in traumatic brain injury patients. Journal of Neurotrauma, 29(1), 101–111. doi: 10.1089/neu.2011.2084.Google Scholar
Sprugnoli, G., Rossi, S., Emmerdorfer, A., Rossi, A., Liew, S., Tatti, E., … Santarnecchi, E. (2017). Neural correlates of Eureka moment. Intelligence, 62, 99–118. doi: 10.1016/j.intell.2017.03.004.Google Scholar
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028. doi: 10.1016/j.neuropsychologia.2009.03.004.Google Scholar
Tik, M., Sladky, S., Luft, C., Willinger, D., Hoffmann, A., Banissy, M., … Windischberger, C. (2018). Ultra-high-field fMRI insights on insight: Neural correlates of the Aha!-moment. Human Brain Mapping, 39, 3241–3252.Google Scholar
Unterrainer, J. M., & Owen, A. M. (2006). Planning and problem solving: From neuropsychology to functional neuroimaging. Journal of Physiology-Paris, 99(4), 308–317. doi: 10.1016/j.jphysparis.2006.03.014.Google 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, 349–354. doi: 10.1016/j.neuroimage.2014.09.055.CrossRefGoogle Scholar
Waechter, R. L., Goel, V., Raymont, V., Kruger, F., & Grafman, J. (2013). Transitive inference reasoning is impaired by focal lesions in parietal cortex rather than rostrolateral prefrontal cortex. Neuropsychologia, 51(3), 464–471.Google Scholar
Wardlaw, C., Hicks, A. J., Sherer, M., & Ponsford, J. L. (2018). Psychological resilience is associated with participation outcomes following mild to severe traumatic brain injury. Frontiers in Neurology, 9, 563. doi: 10.3389/fneur.2018.00563.Google Scholar
Wechsler, D. (1958). The measurement and appraisal of adult intelligence (4th ed.). Philadelphia, PA: Williams & Wilkins Co. doi: 10.1037/11167-000.Google Scholar
Wechsler, D. (2001). Wechsler Test of Adult Reading: WTAR. San Antonio, TX: Psychological Corporation.Google Scholar
Wechsler, D. (2008a). WAIS-IV technical and interpretive manual. San Antonio, TX: Pearson.Google Scholar
Wechsler, D. (2008b). Wechsler adult intelligence scale – Fourth edition (WAIS–IV). San Antonio, TX: NCS Pearson.Google Scholar

References

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), 819–827.Google Scholar
Ansari, D., Coch, D., & De Smedt, B. (2011). Connecting education and cognitive neuroscience: Where will the journey take us? Educational Philosophy and Theory, 43(1), 37–42.Google Scholar
Asbury, K. (2015). Can genetics research benefit educational interventions for all? Hastings Center Report, 45(S1), S39–S42.Google Scholar
Asbury, K., & Plomin, R. (2013). G is for genes: The impact of genetics on education and achievement, Vol. 24. Chichester, UK: John Wiley & Sons.Google Scholar
Blakemore, S. J. (2018). Inventing ourselves: The secret life of the teenage brain. London: Penguin Random House.Google Scholar
Boliver, V., & Swift, A. (2011). Do comprehensive schools reduce social mobility? The British Journal of Sociology, 62(1), 89–110.Google Scholar
Bowers, J. S. (2016a). The practical and principled problems with educational neuroscience. Psychological Review, 123(5), 600–612.Google Scholar
Bowers, J. S. (2016b). Psychology, not educational neuroscience, is the way forward for improving educational outcomes for all children: Reply to Gabrieli (2016) and Howard-Jones et al. (2016). Psychological Review, 123(5), 628–635.Google Scholar
Broer, T., Cunningham-Burley, S., Deary, I., & Pickersgill, M. (2016). Neuroscience, policy and family life. Edinburgh: University of Edinburgh.Google Scholar
Bruer, J. T. (1999). The myth of the first three years: A new understanding of early brain development and lifelong learning. New York: Free Press.Google Scholar
Calvin, C. M., Batty, G. D., Der, G., Brett, C. E., Taylor, A., Pattie, A., … & Deary, I. J. (2017). Childhood intelligence in relation to major causes of death in 68 year follow-up: Prospective population study. British Medical Journal, 357(8112), j2708.Google Scholar
Calvin, C. M., Deary, I. J., Fenton, C., Roberts, B. A., Der, G., Leckenby, N., & Batty, G. D. (2010). Intelligence in youth and all-cause-mortality: Systematic review with meta-analysis. International journal of epidemiology, 40(3), 626–644.Google Scholar
Crosswaite, M., & Asbury, K. (2016). “Mr Cummings clearly does not understand the science of genetics and should maybe go back to school on the subject”: An exploratory content analysis of the online comments beneath a controversial news story. Life Sciences, Society and Policy, 12(1), 11.Google Scholar
Crosswaite, M., & Asbury, K. (2019). Teacher beliefs about the aetiology of individual differences in cognitive ability, and the relevance of behavioural genetics to education. British Journal of Educational Psychology, 89(1), 95–110.Google Scholar
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35(1), 13–21.Google Scholar
Dehaene, S., Piazza, M., Pinel, P., & Cohen, L. (2003). Three parietal circuits for number processing. Cognitive Neuropsychology, 20(3–6), 487–506.Google Scholar
Detterman, D. K. (2016). Education and intelligence: Pity the poor teacher because student characteristics are more significant than teachers or schools. The Spanish Journal of Psychology, 19(e93), 1–11.Google Scholar
Eagleman, D. (2011). Incognito: The hidden life of the brain. New York: Pantheon Books.Google Scholar
Erickson, K. I., Voss, M. W., Prakash, R. S., Basak, C., Szabo, A., Chaddock, L., … Wojcicki, T. R. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences, 108(7), 3017–3022.Google Scholar
Francis, B., Connolly, P., Archer, L., Hodgen, J., Mazenod, A., Pepper, D., … Travers, M. C. (2017). Attainment grouping as self-fulfilling prophesy? A mixed methods exploration of self confidence and set level among year 7 students. International Journal of Educational Research, 86, 96–108.CrossRefGoogle Scholar
Frey, M. C., & Detterman, D. K. (2004). Scholastic assessment or g? The relationship between the scholastic assessment test and general cognitive ability. Psychological Science, 15(6), 373–378.Google Scholar
Frith, U., Bishop, D., Blakemore, C., Blakemore, S. J., Butterworth, B., & Goswami, U. (2011). Neuroscience: Implications for education and lifelong learning. The Royal Society.Google Scholar
Gabrieli, J. D. (2016). The promise of educational neuroscience: Comment on Bowers (2016). Psychological Review, 123(5), 613–619.Google Scholar
Gale, C. R., Batty, G. D., Tynelius, P., Deary, I. J., & Rasmussen, F. (2010). Intelligence in early adulthood and subsequent hospitalisation and admission rates for the whole range of mental disorders: Longitudinal study of 1,049,663 men. Epidemiology, 21(1), 70–77.Google Scholar
Gorard, S., & Siddiqui, N. (2018). Grammar schools in England: A new analysis of social segregation and academic outcomes. British Journal of Sociology of Education, 39(7), 909–924.Google Scholar
Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79–132.Google Scholar
Grantham-McGregor, S. (2005). Can the provision of breakfast benefit school performance? Food and Nutrition Bulletin, 26(2 suppl 2), S144–S158.Google Scholar
Hagenauer, M. H., Perryman, J. I., Lee, T. M., & Carskadon, M. A. (2009). Adolescent changes in the homeostatic and circadian regulation of sleep. Developmental Neuroscience, 31(4), 276–284.Google Scholar
Haier, R. J. (2016). The neuroscience of intelligence. Cambridge University Press.Google Scholar
Haworth, C. M., Wright, M. J., Luciano, M., Martin, N. G., de Geus, E. J., van Beijsterveldt, C. E., … Kovas, Y. (2010). The heritability of general cognitive ability increases linearly from childhood to young adulthood. Molecular Psychiatry, 15(11), 1112–1120.Google Scholar
Holmes, J., Bryant, A., Gathercole, S. E., & CALM Team. (2019). Protocol for a transdiagnostic study of children with problems of attention, learning and memory (CALM). BMC Pediatrics, 19(1), 10.Google Scholar
Howard-Jones, P. A., Varma, S., Ansari, D., Butterworth, B., De Smedt, B., Goswami, U., … & Thomas, M. S. (2016). The principles and practices of educational neuroscience: Comment on Bowers (2016), Psychological Review, 123(5), 620–627.Google Scholar
Johnson, W., Brett, C. E., & Deary, I. J. (2010). The pivotal role of education in the association between ability and social class attainment: A look across three generations. Intelligence, 38(1), 55–65.Google Scholar
Krapohl, E., Rimfeld, K., Shakeshaft, N. G., Trzaskowski, M., McMillan, A., Pingault, J. B., … & Plomin, R. (2014). The high heritability of educational achievement reflects many genetically influenced traits, not just intelligence. Proceedings of the National Academy of Sciences, 111(42), 15273–15278.Google Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … & Fontana, M. 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), 1112–1121.Google Scholar
Ludwig, J., & Phillips, D. A. (2007). The benefits and costs of Head Start (No. w12973). Cambridge, MA: National Bureau of Economic Research.Google Scholar
Mackintosh, N., & Mackintosh, N. J. (2011). IQ and human intelligence. Oxford University Press.Google Scholar
Malanchini, M., Engelhardt, L. E., Grotzinger, A. D., Harden, K. P., & Tucker-Drob, E. M. (2019). “Same but different”: Associations between multiple aspects of self-regulation, cognition, and academic abilities. Journal of Personality and Social Psychology, 117(6), 1164–1188.Google Scholar
Nicholas, S., Andrieu, B., Croizet, J. C., Sanitioso, R. B., & Burman, J. T. (2013). Sick? Or slow? On the origins of intelligence as a psychological object. Intelligence, 41(5), 699–711.Google Scholar
Owens, J. A., Belon, K., & Moss, P. (2010). Impact of delaying school start time on adolescent sleep, mood, and behavior. Archives of Pediatrics & Adolescent Medicine, 164(7), 608–614.Google Scholar
Parker, P. D., Jerrim, J., Schoon, I., & Marsh, H. W. (2016). A multination study of socioeconomic inequality in expectations for progression to higher education: The role of between-school tracking and ability stratification. American Educational Research Journal, 53(1), 6–32.Google Scholar
Pearson, K. (1914). On the handicapping of the first-born, Vol. 10. Cambridge University Press.Google Scholar
Penke, L., Maniega, S. M., Bastin, M. E., Hernández, M. 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), 1026–1030.Google Scholar
Plomin, R., & Deary, I. J. (2015). Genetics and intelligence differences: Five special findings. Molecular Psychiatry, 20(1), 98–108.Google Scholar
Pugh, K. R., Shaywitz, B. A., Shaywitz, S. E., Constable, R. T., Skudlarski, P., Fulbright, R. K., … Gore, J. C. (1996). Cerebral organization of component processes in reading. Brain, 119(4), 1221–1238.Google Scholar
Puma, M., Bell, S., Cook, R., Heid, C., Broene, P., Jenkins, F., … Downer, J. (2012). Third grade follow-up to the Head Start impact study: Final report. OPRE Report 2012-45b. Administration for Children & Families (pp. 1–346) Washington, DC.Google Scholar
Puma, M., Bell, S., Cook, R., Heid, C., Shapiro, G., Broene, P., … Ciarico, J. (2010). Head Start impact study. Final report. Administration for Children & Families (611 pp).Google Scholar
Rimfeld, K., Kovas, Y., Dale, P. S., & Plomin, R. (2016). True grit and genetics: Predicting academic achievement from personality. Journal of Personality and Social Psychology, 111(5), 780–789.Google Scholar
Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A meta-analysis. Psychological Science, 29(8), 1358–1369.Google Scholar
Rose, N. (2010). “Screen and intervene”: Governing risky brains. History of the Human Sciences, 23(1), 79–105.Google Scholar
Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence, 53, 118–137.Google Scholar
Schneider, W. J., & Kaufman, A. S. (2017). Let’s not do away with comprehensive cognitive assessments just yet. Archives of Clinical Neuropsychology, 32(1), 8–20.Google Scholar
Schofield, J. W. (2010). International evidence on ability grouping with curriculum differentiation and the achievement gap in secondary schools. Teachers College Record, 112(5), 1492–1528.Google Scholar
Shager, H. M., Schindler, H. S., Magnuson, K. A., Duncan, G. J., Yoshikawa, H., & Hart, C. M. (2013). Can research design explain variation in Head Start research results? A meta-analysis of cognitive and achievement outcomes. Educational Evaluation and Policy Analysis, 35(1), 76–95.Google Scholar
Sigman, M., Peña, M., Goldin, A. P., & Ribeiro, S. (2014). Neuroscience and education: Prime time to build the bridge. Nature Neuroscience, 17(4), 497–502.Google Scholar
Smith-Woolley, E., Ayorech, Z., Dale, P. S., von Stumm, S., & Plomin, R. (2018). The genetics of university success. Scientific Reports, 8(1), 14579.Google Scholar
Smith-Woolley, E., Pingault, J. B., Selzam, S., Rimfeld, K., Krapohl, E., von Stumm, S., … Kovas, Y. (2018). Differences in exam performance between pupils attending selective and non-selective schools mirror the genetic differences between them. NPJ Science of Learning, 3(1), 3.Google Scholar
Sokolowski, H. M., & Ansari, D. (2018). Understanding the effects of education through the lens of biology. NPJ Science of Learning, 3(1), 17.Google Scholar
Steenbergen-Hu, S., Makel, M. C., & Olszewski-Kubilius, P. (2016). What one hundred years of research says about the effects of ability grouping and acceleration on K–12 students’ academic achievement: Findings of two second-order meta-analyses. Review of Educational Research, 86(4), 849–899.Google Scholar
Supekar, K., Swigart, A. G., Tenison, C., Jolles, D. D., Rosenberg-Lee, M., Fuchs, L., & Menon, V. (2013). Neural predictors of individual differences in response to math tutoring in primary-grade school children. Proceedings of the National Academy of Sciences, 110(20), 8230–8235.Google Scholar
Taylor, B., Francis, B., Archer, L., Hodgen, J., Pepper, D., Tereshchenko, A., & Travers, M. C. (2017). Factors deterring schools from mixed attainment teaching practice. Pedagogy, Culture & Society, 25(3), 327–345.Google Scholar
Tucker-Drob, E. M., & Harden, K. P. (2017). A behavioral genetic perspective on noncognitive factors and academic achievement. Genetics, Ethics and Education, 134–158.Google Scholar
von Stumm, S., Smith-Woolley, E., Ayorech, Z., McMillan, A., Rimfeld, K., Dale, P., & Plomin, R. (2020). Predicting educational achievement from genomic measures and socioeconomic status. Developmental Science, 23(3), e12925.Google Scholar
Wai, J., Brown, M., & Chabris, C. (2018). Using standardized test scores to include general cognitive ability in education research and policy. Journal of Intelligence, 6(3), 37.Google Scholar
Walker, S. O., & Plomin, R. (2005). The nature–nurture question: Teachers’ perceptions of how genes and the environment influence educationally relevant behaviour. Educational Psychology, 25(5), 509–516.CrossRefGoogle Scholar
Wastell, D., & White, S. (2012). Blinded by neuroscience: Social policy, the family and the infant brain. Families, Relationships and Societies, 1(3), 397–414.Google Scholar

References

Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20.Google Scholar
Berry, J. W. (1974). Radical cultural relativism and the concept of intelligence. In Berry, J. W. & Dasen, P. R. (eds.), Culture and cognition: Readings in cross-cultural psychology (pp. 225–229). London: Methuen.Google Scholar
Carroll, J. B. (1993). Human cognitive abilities. A survey of factor-analytic studies. Cambridge University Press.Google Scholar
Ceci, S. J. (2009). On intelligence … A bioecological treatise on intellectual development. Cambridge, MA: Harvard University Press.Google Scholar
Cole, M., Gay, J., Glick, J. A., & Sharp, D. W. (1971). The cultural context of thinking and learning. New York: Basic Books.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), 457–460.Google Scholar
Flynn, J. R. (2020). Secular changes in intelligence: The “Flynn effect.” In Sternberg, R. J. (ed.), Cambridge handbook of intelligence (2nd ed.), pp. 940–963. New York: Cambridge University Press.Google Scholar
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books.Google Scholar
Gardner, H. (2017). Taking a multiple intelligences (MI) perspective. Behavioral and Brain Sciences, doi: 10.1017/S0140525X16001631.Google Scholar
Glick, J. (1974). Cognitive development in cross-cultural perspective. In Hetherington, E. M. (ed.), Review of child development research, 4 (pp. 891–1008). Chicago: SRCD.Google Scholar
Gottfredson, L. S. (1997). Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography. Intelligence, 24(1), 13–23.Google Scholar
Greenfield, P. M. (2020). Historical evolution of intelligence. In Sternberg, R. J. (ed.), Cambridge handbook of intelligence (2nd ed.) (pp. 916–933). New York: Cambridge University Press.Google Scholar
Grigorenko, E. L., Meier, E., Lipka, J., Mohatt, G., Yanez, E., & Sternberg, R. J. (2004). Academic and practical intelligence: A case study of the Yup’ik in Alaska. Learning and Individual Differences, 14(4), 183–207.Google Scholar
Grigorenko, E. L., & Sternberg, R. J. (2001). Analytical, creative, and practical intelligence as predictors of self–reported adaptive functioning: A case study in Russia. Intelligence, 29(1), 57–73.Google Scholar
Guilford, J. P. (1967). The nature of human intelligence. New York: McGraw-Hill.Google Scholar
Guilford, J. P. (1982). Cognitive psychology’s ambiguities: Some suggested remedies. Psychological Review, 89(1), 48–59.Google Scholar
Haier, R. J. (2016). The neuroscience of intelligence. New York: Cambridge University Press.Google Scholar
Haier, R. J. (2020). The biological basis of intelligence. In Sternberg, R. J. (ed.), Cambridge handbook of intelligence (2nd ed.) (pp. 451–468). New York: Cambridge University Press.Google Scholar
Hebb, D. O. (1962/2002). The organization of behavior. New York: Psychology Press.Google Scholar
Hedlund, J. (2020). Practical intelligence. In Sternberg, R. J. (ed.), Cambridge handbook of intelligence (2nd ed.) (pp. 736–755). New York: Cambridge University Press.Google Scholar
Horn, J. L., & Knapp, J. R. (1973). On the subjective character of the empirical base of Guilford’s Structure-of-Intellect model. Psychological Bulletin, 80(1), 33–43.Google Scholar
Intelligence and its measurement: A symposium (1921). Journal of Educational Psychology, 12, 123–147, 195–216, 271–275.Google Scholar
Kaufman, J. C., & Sternberg, R. J. (eds.) (2019). Cambridge handbook of creativity (2nd ed.). New York: Cambridge University Press.Google Scholar
Kihlstrom, J. F., & Cantor, N. (2020). Social intelligence. In Sternberg, R. J. (ed.), Cambridge handbook of intelligence (2nd ed.) (pp. 756–779). New York: Cambridge University Press.Google Scholar
Krechevsky, M. (1998). Project Spectrum: Preschool assessment handbook. New York: Teachers College Press.Google Scholar
Kunzmann, U. (2019). Performance-based measures of wisdom: State of the art and future directions. In Sternberg, R. J. & Glueck, J. (eds.), Cambridge handbook of wisdom (pp. 277–296). New York: Cambridge University Press.Google Scholar
Laboratory of Comparative Human Cognition (1982). Culture and intelligence. In Sternberg, R. J. (ed.), Handbook of human intelligence (pp. 642–719). New York: Cambridge University Press.Google Scholar
Luria, A. R. (1976a). Cognitive development: Its cultural and social foundations. Cambridge, MA: Harvard University Press.Google Scholar
Luria, A. R. (1976b). The working brain: An introduction to neuropsychology. New York: Basic Books.Google Scholar
Mackintosh, N. J. (2011). History of theories and measurement of intelligence. In Sternberg, R. J. & Kaufman, S. B. (eds.), Cambridge handbook of intelligence (pp. 1–19). New York: Cambridge University Press.Google Scholar
Pinker, S. (2018). Enlightenment now! The case for reason, science, humanism, and progress. New York: Viking.Google Scholar
Rivers, S. E., Handley-Miner, I. J., Mayer, J. D., & Caruso, D. R. (2020). Emotional intelligence. In Sternberg, R. J. (ed.), Cambridge handbook of intelligence (2nd ed.) (pp. 709–735). New York: Cambridge University Press.Google Scholar
Sackett, P. R., Shewach, O. R., & Dahlke, J A. (2020). The predictive value of general intelligence. In Sternberg, R. J. (ed.), Human intelligence: An introduction (pp. 381–414). New York: Cambridge University Press.Google Scholar
Serpell, R. (2000). Intelligence and culture. In Sternberg, R. J. (ed.), Handbook of intelligence (pp. 549–577). New York: Cambridge University Press.Google Scholar
Spearman, C. (1927). The abilities of man. New York: Macmillan.Google Scholar
Sternberg, R. J. (1977). Intelligence, information processing, and analogical reasoning: The componential analysis of human abilities. Hillsdale, NJ: Erlbaum.Google Scholar
Sternberg, R. J. (1985). Human intelligence: The model is the message. Science, 230(4730), 1111–1118.Google Scholar
Sternberg, R. J. (1990). Metaphors of mind: Conceptions of the nature of intelligence. New York: Cambridge University Press.Google Scholar
Sternberg, R. J. (2004a). Culture and intelligence. American Psychologist, 59(5), 325–338.Google Scholar
Sternberg, R. J. (ed.) (2004b). International handbook of intelligence. New York: Cambridge University Press.Google Scholar
Sternberg, R. J. (2005). Foolishness. In Sternberg, R. J. & Jordan, J. (eds.), Handbook of wisdom: Psychological perspectives (pp. 331–352). New York: Cambridge University Press.Google Scholar
Sternberg, R. J. (2010). College admissions for the 21st century. Cambridge, MA: Harvard University Press.Google Scholar
Sternberg, R. J. (2017). Measuring creativity: A 40+ year retrospective. Journal of Creative Behavior, 53(4), 600–604. doi: 10.1002/jocb.218.Google Scholar
Sternberg, R. J. (2018a). A triangular theory of creativity. Psychology of Aesthetics, Creativity, and the Arts, 12(1), 50–67.Google Scholar
Sternberg, R. J. (2018b). Wisdom, foolishness, and toxicity in human development. Research in Human Development, 15(3–4), 200–210. doi: 10.1080/15427609.2018.1491216.Google Scholar
Sternberg, R. J. (2019a). Intelligence. In Sternberg, R. J. & Pickren, W. (eds.), Handbook of the intellectual history of psychology: How psychological ideas have evolved from past to present (pp. 267–286). New York: Cambridge University Press.Google Scholar
Sternberg, R. J. (2019b). Wisdom, foolishness, and toxicity: How does one know which is which? In Mumford, M. & Higgs, C. A. (eds.), Leader thinking skills (pp. 362–381). New York: Taylor & Francis.Google Scholar
Sternberg, R. J. (ed.) (2020). Cambridge handbook of intelligence (2nd ed.). New York: Cambridge University Press.Google Scholar
Sternberg, R. J., & Detterman, D. K. (eds.) (1986). What is intelligence? Norwood, NJ: Ablex Publishing Corporation.Google Scholar
Sternberg, R. J., Forsythe, G. B., Hedlund, J., Horvath, J., Snook, S., Williams, W. M., … Grigorenko, E. L. (2000). Practical intelligence in everyday life. New York: Cambridge University Press.Google Scholar
Sternberg, R. J., & Glueck, J. (eds.) (2019). Cambridge handbook of wisdom. New York: Cambridge University Press.Google Scholar
Sternberg, R. J., & Grigorenko, E. L. (2000). Practical intelligence and its development. In Bar-On, R. & Parker, J. D. A. (eds.), The handbook of emotional intelligence: Theory, development, assessment, and application at home, school, and in the workplace (pp. 215–243). San Francisco: Jossey-Bass.Google Scholar
Sternberg, R. J., & Grigorenko, E. L. (eds.) (2002). The general factor of intelligence: How general is it? Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Sternberg, R. J., Lipka, J., Newman, T., Wildfeuer, S., & Grigorenko, E. L. (2007). Triarchically-based instruction and assessment of sixth-grade mathematics in a Yup’ik cultural setting in Alaska. International Journal of Giftedness and Creativity, 21(2), 6–19.Google Scholar
Sternberg, R. J., Nokes, K., Geissler, P. W., Prince, R., Okatcha, F., Bundy, D. A., & Grigorenko, E. L. (2001). The relationship between academic and practical intelligence: A case study in Kenya. Intelligence, 29(5), 401–418.Google Scholar
Sternberg, R. J., & Smith, C. (1985). Social intelligence and decoding skills in nonverbal communication. Social Cognition, 3(2), 168–192.Google Scholar
Sternberg, R. J., & The Rainbow Project Collaborators (2006). The Rainbow Project: Enhancing the SAT through assessments of analytical, practical and creative skills. Intelligence, 34(4), 321–350.Google Scholar
Thurstone, L. L. (1938). Primary mental abilities. Chicago: University of Chicago Press.Google Scholar
Walrath, R., Willis, J. O., Dumont, R., & Kaufman, A. S. (2020). Factor-analytic models of intelligence. In Sternberg, R. J. (ed.), Cambridge handbook of intelligence (2nd ed.) (pp. 75–98). New York: Cambridge University Press.Google Scholar
Warne, R. T., & Burningham, C. (2019). Spearman’s g found in 21 non-Western nations: Strong evidence that g is a universal phenomenon. Psychological Bulletin, 145(3), 237–272. doi: 10.1037/bul000184.Google Scholar
Yang, S., & Sternberg, R. J. (1997). Taiwanese Chinese people’s conceptions of intelligence. Intelligence, 25(1), 21–36.Google Scholar

References

Ackerman, P. L. (2017). Adult intelligence: The construct and the criterion problem. Perspectives on Psychological Science, 12(6), 987–998. doi: 10.1177/1745691617703437.Google Scholar
Bailey, D. H., Duncan, G. J., Cunha, F., Foorman, B. R., & Yeager, D. S. (2020). Persistence and fade-out of educational-intervention effects: Mechanisms and potential solutions. Psychological Science in the Public Interest, 21(2), 55–97.Google Scholar
Bailey, D. H., Duncan, G. J., Watts, T., Clements, D. H., & Sarama, J. (2018). Risky business: Correlation and causation in longitudinal studies of skill development. American Psychologist, 73(1), 81–94.Google Scholar
Bailey, D. H., Fuchs, L. S., Gilbert, J. K., Geary, D. C., & Fuchs, D. (2020). Prevention: Necessary but insufficient? A two-year follow-up of effective first-grade mathematics intervention. Child Development, 91(2), 382–400.Google Scholar
Card, D., & Giuliano, L. (2016). Universal screening increases the representation of low-income and minority students in gifted education. Proceedings of the National Academy of Sciences, 113(48), 13678–13683. doi: 10.1073/pnas.1605043113.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), 703–722. doi: 10.1037/0012-1649.27.5.703.Google Scholar
Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. American Economic Review, 104(9), 2633–2679. doi: 10.1257/aer.104.9.2633.Google Scholar
Detterman, D. K. (2016). Pity the poor teacher because student characteristics are more significant than teachers or schools. Spanish Journal of Psychology, 19, E93. doi: 10.1017/sip.2016.88.Google Scholar
Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., … Japel, C. (2007). School readiness and later achievement. Developmental Psychology, 43(6), 1428–1446.Google Scholar
Elango, S., García, J. L., Heckman, J. J., & Hojman, A. (2015). Early childhood education (No. w21766). Cambridge, MA: National Bureau of Economic Research.Google Scholar
Gelman, A., & Imbens, G. (2013). Why ask why? Forward causal inference and reverse causal questions. NBER Working Paper 19614. www.nber.org/papers/w19614.pdfGoogle Scholar
Gottfredson, L. S. (2004). Schools and the g factor. The Wilson Quarterly, 28(3), 35–45.Google Scholar
Haier, R. J. (2014). Increased intelligence is a myth (so far). Frontiers in Systems Neuroscience, 8, 34. doi: 10.3389/fnsys.2014.00034.Google Scholar
Haier, R. J. (2017). The neuroscience of intelligence. Cambridge University Press.Google Scholar
Hurwitz, M., Smith, J., Niu, S., & Howell, J. (2015). The Maine question: How is 4-year college enrollment affected by mandatory college entrance exams? Educational Evaluation and Policy Analysis, 37(1), 138–159. doi: 10.3102/0162373714521866.Google Scholar
Hyman, J. (2017). ACT for all: The effect of mandatory college entrance exams on postsecondary attainment and choice. Education Finance and Policy, 12(3), 281–311. doi: 10.1162/EDFP_a_00206.Google Scholar
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.Google Scholar
Johnson, R. C., & Jackson, C. K. (2019). Reducing inequality through dynamic complementarity: Evidence from Head Start and public school spending. American Economic Journal: Economic Policy, 11(4), 310–349.Google 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 of Sciences, 112(15), 4612–4617. doi: 10.1073/pnas.1417106112.Google Scholar
Kraft, M. A. (2019). Teacher effects on complex cognitive skills and social-emotional competencies. Journal of Human Resources, 54(1), 1–36.Google Scholar
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance. Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148–161. doi: 10.1037/0022-3514.86.1.148.Google Scholar
Lewis, N. A. Jr., & Wai, J. (in press). Communicating what we know, and what isn’t so: Science communication in psychology. Perspectives on Psychological Science. https://psyarxiv.com/cfmzkGoogle Scholar
Li, W., Leak, J., Duncan, G. J., Magnuson, K., Schindler, H., & Yoshikawa, H. (2017). Is timing everything? How early childhood education program impacts vary by starting age, program duration and time since the end of the program. Working Paper, National Forum on Early Childhood Policy and Programs, Meta-analytic Database Project. Center on the Developing Child, Harvard University.Google Scholar
Lubinski, D., & Benbow, C. P. (2000). States of excellence. American Psychologist, 55(1), 137–150. doi: 10.1037/0003-066X.55.1.137.Google Scholar
Macnamara, B. N., Hambrick, D. Z., & Oswald, F. L. (2014). Deliberate practice and performance in music, games, sports, education, and professions: A meta-analysis. Psychological Science, 25(8), 1608–1618. doi: 10.1177/0956797614535810.Google Scholar
Moreau, D., Macnamara, B. N., & Hambrick, D. Z. (2018). Overstating the role of environmental factors in success: A cautionary note. Current Directions in Psychological Science, 28(1), 28–33. doi: 10.1177/0963721418797300.Google Scholar
Pinker, S. A. (2015). The sense of style: The thinking person’s guide to writing in the 21st century. New York: Penguin Books.Google Scholar
Protzko, J. (2015). The environment in raising early intelligence: A meta-analysis of the fadeout effect. Intelligence, 53, 202–210.Google Scholar
Protzko, J. (2016). Does the raising IQ-raising g distinction explain the fadeout effect? Intelligence, 56, 65–71.Google Scholar
Protzko, J. (2017). Effects of cognitive training on the structure of intelligence. Psychonomic Bulletin & Review, 24(4), 1022–1031. doi: 10.3758/s13423-016-1196-1.Google Scholar
Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A meta-analysis. Psychological Science, 29(8), 1358–1369. doi: 10.1177/0956797618774253.Google Scholar
Sacerdote, B. (2007). How large are the effects from changes in family environment? A study of Korean American adoptees. The Quarterly Journal of Economics, 122(1), 119–157. doi: 10.1162/qjec.122.1.119.Google Scholar
Sackett, P. R., Borneman, M. J., & Connelly, B. S. (2008). High-stakes testing in higher education and employment. American Psychologist, 63(4), 215–227. doi: 10.1037/0003-066X.63.4.215.Google Scholar
Sackett, P. R., Kuncel, N. R., Beatty, A. S., Rigdon, J. L., Shen, W., & Kiger, T. B. (2012). The role of socioeconomic status in SAT-grade relationships and in college admissions decisions. Psychological Science, 23(9), 1000–1007. doi: 10.1177/0956797612438732.Google Scholar
Schmidt, F. L. (2017). Beyond questionable research methods: The role of omitted relevant research in the credibility of research. Archives of Scientific Psychology, 5(1), 32–41. doi: 10.1037/arc0000033.Google Scholar
Schmidt, F. L., & Hunter, J. E. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86(1), 162–173. doi: 10.1037/0022-3514.86.1.162.Google Scholar
Shipstead, Z., Redick, T. S., & Engle, R. W. (2012). Is working memory training effective? Psychological Bulletin, 138(4), 628–654.Google Scholar
Terman, L. M. (1925). Genetic studies of genius: Volume 1. Mental and physical traits of a thousand gifted children. Palo Alto, CA: Stanford University Press.Google Scholar
Tucker-Drob, E. M. (2013). How many pathways underlie socioeconomic differences in the development of cognition and achievement? Learning and Individual Differences, 25, 12–20. doi: 10.1016/j.lindif.2013.01.015.Google Scholar
Tucker-Drob, E. M., & Bates, T. C. (2016). Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychological Science, 27(2), 138–149. doi: 10.1177/0956797615612727.Google Scholar
Tucker-Drob, E. M., & Briley, D. A. (2014). Continuity of genetic and environmental influences on cognition across the life span: A meta-analysis of longitudinal twin and adoption studies. Psychological Bulletin, 140(4), 949. doi: 10.1037/a0035893.Google Scholar
Turkheimer, E. (1991). Individual and group differences in adoption studies of IQ. Psychological Bulletin, 110(3), 392–405.Google Scholar
van Ijzendoorn, M. H., Juffer, F., & Poelhuis, C. W. (2005). Adoption and cognitive development: A meta-analytic comparison of adopted and nonadopted children’s IQ and school performance. Psychological Bulletin, 131(2), 301–316. doi: 10.1037/0033-2909.131.2.301.Google Scholar
Wai, , (2020). Communicating intelligence research. Journal of Intelligence, 8(4), 40. https://doi.org/10.3390/jintelligence8040040Google Scholar
Wai, J., Brown, M. I., & Chabris, C. F. (2018). Using standardized test scores to include general cognitive ability in education research and policy. Journal of Intelligence, 6(3), 37. doi: 10.3390/jintelligence6030037.Google Scholar
Wai, J., & Miller, D. I. (2015). Here’s why academics should write for the public. The Conversation. https://theconversation.com/heres-why-academics-should-write-for-the-public-50874Google Scholar

References

Anderson, A. J., Kiela, D., Clark, S., & Poesio, M. (2017). Visually grounded and textual semantic models differentially decode brain activity associated with concrete and abstract nouns. Transactions of the Association for Computational Linguistics, 5, 17–30.Google Scholar
Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22(4), 577–660.Google Scholar
Barsalou, L. W. (2003). Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society B: Biological Sciences, 358(1435), 1177–1187. doi.org/10.1098/rstb.2003.1319Google Scholar
Benn, Y., Zheng, Y., Wilkinson, I. D., Siegal, M., & Varley, R. (2012). Language in calculation: A core mechanism? Neuropsychologia, 50(1), 1–10. https://doi.org/10.1016/j.neuropsychologia.2011.09.045Google Scholar
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904–911. https://doi.org/10.3758/s13428-013-0403-5Google Scholar
Chen, J. L., Zatorre, R. J., & Penhune, V. B. (2006). Interactions between auditory and dorsal premotor cortex during synchronization to musical rhythms. NeuroImage, 32(4), 1771–1781. https://doi.org/10.1016/j.neuroimage.2006.04.207Google Scholar
Clarke, A., Taylor, K. I., Devereux, B., Randall, B., & Tyler, L. K. (2013). From perception to conception: How meaningful objects are processed over time. Cerebral Cortex, 23(1), 187–197. https://doi.org/10.1093/cercor/bhs002Google Scholar
Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407–428. https://doi.org/10.1037/0033-295X.82.6.407Google Scholar
Coutanche, M. N., & Thompson-Schill, S. L. (2015). Creating concepts from converging features in human cortex. Cerebral Cortex, 25(9), 2584–2593. https://doi.org/10.1093/cercor/bhu057Google Scholar
Crutch, S. J., & Warrington, E. K. (2005). Abstract and concrete concepts have structurally different representational frameworks. Brain, 128(3), 615–627. https://doi.org/10.1093/brain/awh349Google Scholar
Crutch, S. J., & Warrington, E. K. (2010). The differential dependence of abstract and concrete words upon associative and similarity-based information: Complementary semantic interference and facilitation effects. Cognitive Neuropsychology, 27(1), 46–71. https://doi.org/10.1080/02643294.2010.491359Google Scholar
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the Association for Information Science and Technology, 41(6), 391–407.Google Scholar
Fugelsang, J. A., & Dunbar, K. N. (2005). Brain-based mechanisms underlying complex causal thinking. Neuropsychologia, 43(8), 1204–1213. https://doi.org/10.1016/j.neuropsychologia.2004.10.012Google Scholar
Fugelsang, J. A., Roser, M. E., Corballis, P. M., Gazzaniga, M. S., & Dunbar, K. N. (2005). Brain mechanisms underlying perceptual causality. Cognitive Brain Research, 24(1), 41–47. https://doi.org/10.1016/j.cogbrainres.2004.12.001Google Scholar
Grill-Spector, K., & Malach, R. (2004). The human visual cortex. Annual Review of Neuroscience, 27(1), 649–677. https://doi.org/10.1146/annurev.neuro.27.070203.144220Google Scholar
Hauk, O., & Pulvermüller, F. (2004) Neurophysiological distinction of action words in the fronto‐central cortex. Human Brain Mapping, 21(3), 191–201. DOI: 10.1002/hbm.10157Google Scholar
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex, Science, 293(5539), 2425–2430. doi: 10.1126/science.1063736.Google Scholar
Hayes, J. C., & Kraemer, D. J. M. (2017). Grounded understanding of abstract concepts: The case of STEM learning. Cognitive Research: Principles and Implications, 2(1), 7. https://doi.org/10.1186/s41235-016-0046-zGoogle Scholar
Haynes, J. D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7(7), 523–534. https://doi.org/10.1038/nrn1931Google Scholar
Hoffman, P. (2016). The meaning of “life” and other abstract words: Insights from neuropsychology. Journal of Neuropsychology, 10(2), 317–343. https://doi.org/10.1111/jnp.12065Google Scholar
Hoffman, P., Jefferies, E., & Lambon Ralph, M. A. (2010). Ventrolateral prefrontal cortex plays an executive regulation role in comprehension of abstract words: Convergent neuropsychological and repetitive TMS evidence. Journal of Neuroscience, 30(46), 15450–15456. https://doi.org/10.1523/JNEUROSCI.3783-10.2010Google Scholar
Huth, A. G., De Heer, W. A., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2016). Natural speech reveals the semantic maps that tile human cerebral cortex. Nature, 532(7600), 453–458. https://doi.org/10.1038/nature17637Google Scholar
Just, M. A., Cherkassky, V. L., Aryal, S., & Mitchell, T. M. (2010). A neurosemantic theory of concrete noun representation based on the underlying brain codes. PLoS ONE, 5(1), e8622. https://doi.org/10.1371/journal.pone.0008622Google Scholar
Just, M. A., Cherkassky, V. L., Buchweitz, A., Keller, T. A., & Mitchell, T. M. (2014). Identifying autism from neural representations of social interactions: Neurocognitive markers of autism. PLoS ONE, 9(12), e113879. https://doi.org/10.1371/journal.pone.0113879Google Scholar
Kassam, K. S., Markey, A. R., Cherkassky, V. L., Loewenstein, G., & Just, M. A. (2013). Identifying emotions on the basis of neural activation. PLoS ONE, 8(6), e66032. https://doi.org/10.1371/journal.pone.0066032Google Scholar
Klasen, M., Kenworthy, C. A., Mathiak, K. A., Kircher, T. T. J., & Mathiak, K. (2011). Supramodal representation of emotions. Journal of Neuroscience, 31(38), 13635–13643. https://doi.org/10.1523/JNEUROSCI.2833-11.2011Google Scholar
Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist, K., & Wager, T. D. (2008). Functional grouping and cortical–subcortical interactions in emotion. NeuroImage, 42(2), 998–1031. https://doi.org/10.1016/j.neuroimage.2008.03.059Google Scholar
Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103(10), 3863–3868. https://doi.org/10.1073/pnas.0600244103Google Scholar
Kriegeskorte, N., Mur, M., & Bandettini, P. (2008a). Representational similarity analysis – Connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2(11), 1–28. https://doi.org/10.3389/neuro.06.004.2008Google Scholar
Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., … Bandettini, P. A. (2008b). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60(6), 1126–1141. https://doi.org/10.1016/j.neuron.2008.10.043Google Scholar
Lambon Ralph, M. A. (2014). Neurocognitive insights on conceptual knowledge and its breakdown. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1634), 20120392. https://doi.org/10.1098/rstb.2012.0392Google Scholar
Landauer, T. K., & Dumas, S. T. (1997) A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240.Google Scholar
Martin, A. (2007). The representation of object concepts in the brain. Annual Review of Psychology, 58, 25–45.Google Scholar
Mason, R. A., & Just, M. A. (2016). Neural representations of physics concepts. Psychological Science, 27(6), 904–913. https://doi.org/10.1177/0956797616641941Google Scholar
Meyer, K., & Damasio, A. (2009). Convergence and divergence in a neural architecture for recognition and memory. Trends in Neurosciences, 32(7), 376–382. https://doi.org/10.1016/j.tins.2009.04.002Google Scholar
Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K. M., Malave, V. L., Mason, R. A., & Just, M. A. (2008). Predicting human brain activity associated with the meanings of nouns. Science, 320(5880), 1191–1195. https://doi.org/10.1126/science.1152876Google Scholar
Mur, M., Bandettini, P. A., & Kriegeskorte, N. (2009). Revealing representational content with pattern-information fMRI – An introductory guide. Social Cognitive and Affective Neuroscience, 4(1), 101–109. https://doi.org/10.1093/scan/nsn044Google Scholar
Naselaris, T., Kay, K. N., Nishimoto, S., & Gallant, J. L. (2011). Encoding and decoding in fMRI. Neuroimage, 56(2), 400–410.Google Scholar
Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant, J. L. (2011). Reconstructing visual experiences from brain activity evoked by natural movies. Current Biology, 21(19), 1641–1646. https://doi.org/10.1016/j.cub.2011.08.031Google Scholar
Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 1532–1543.Google Scholar
Pereira, F., Lou, B., Pritchett, B., Ritter, S., Gershman, S. J., Kanwisher, N., … Fedorenko, E. (2018). Toward a universal decoder of linguistic meaning from brain activation. Nature Communications, 9(1), 963. https://doi.org/10.1038/s41467-018-03068-4Google Scholar
Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002). Functional neuroanatomy of emotion: A meta-analysis of emotion activation studies in PET and fMRI. NeuroImage, 16(2), 331–348. https://doi.org/10.1006/nimg.2002.1087Google Scholar
Vargas, R., & Just, M. A. (2019). Neural representations of abstract concepts: Identifying underlying neurosemantic dimensions. Cerebral Cortex, 30(4), 2157–2166. https://doi.org/10.1093/cercor/bhz229Google Scholar
Wang, J., Conder, J. A., Blitzer, D. N., & Shinkareva, S. V. (2010). Neural representation of abstract and concrete concepts: A meta-analysis of neuroimaging studies. Human Brain Mapping, 31(10), 1459–1468. https://doi.org/10.1002/hbm.20950Google Scholar
Wang, X., Wu, W., Ling, Z., Xu, Y., Fang, Y., Wang, X., … Bi, Y. (2018). Organizational principles of abstract words in the human brain. Cerebral Cortex, 28(12), 4305–4318. https://doi.org/10.1093/cercor/bhx283Google Scholar
Xu, J., Kemeny, S., Park, G., Frattali, C., & Braun, A. (2005). Language in context: Emergent features of word, sentence, and narrative comprehension. NeuroImage, 25(3), 1002–1015. https://doi.org/10.1016/j.neuroimage.2004.12.013Google Scholar
Yang, Y., Wang, J., Bailer, C., Cherkassky, V., & Just, M. A. (2017). Commonality of neural representations of sentences across languages: Predicting brain activation during Portuguese sentence comprehension using an English-based model of brain function. NeuroImage, 146, 658–666. https://doi.org/10.1016/j.neuroimage.2016.10.029Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×