Element contents
The Evolved Mind and Modern Education
Status of Evolutionary Educational Psychology
Published online by Cambridge University Press: 12 March 2024
Summary
Humans have an extraordinary ability to create evolutionarily novel knowledge, such as writing systems and mathematics. This accumulated knowledge over several millennia supports large, dynamic societies that now require children to learn this novel knowledge in educational settings. This Element provides a framework for understanding the evolution of the brain systems that enable innovation and novel learning and how these systems can act on human cognitive universals, such as language, to create evolutionarily novel abilities, such as reading and writing. Critical features of these networks include the top-down control of attention, which is central to the formation of evolutionarily novel abilities, as well as self-awareness and mental time travel that support academic self-concepts and the generation of long-term educational goals. The basics of this framework are reviewed and updated here, as are implications for instructional practices.
Keywords
Information
- Type
- Element
- Information
- Online ISBN: 9781009454858Publisher: Cambridge University PressPrint publication: 23 May 2024
References
Abrams, D., & Hogg, M. A. (Eds.) (1990). Social identity theory: Constructive and critical advances. Springer-Verlag.Google Scholar
Adolphs, R. (1999). Social cognition and the human brain. Trends in Cognitive Sciences, 3(12), 469–479. https://doi.org/10.1016/S1364-6613(99)01399-6.CrossRefGoogle ScholarPubMed
Alexander, R. D. (1989). Evolution of the human psyche. In Mellars, P., & Stringer, C. (Eds.), The human revolution: Behavioural and biological perspectives on the origins of modern humans (pp. 455–513). Princeton University Press.Google Scholar
Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013). Imaging structural co-variance between human brain regions. Nature Reviews Neuroscience, 14(5), 322–336. https://doi.org/10.1038/nrn3465.CrossRefGoogle ScholarPubMed
Alfieri, L., Brooks, P. J., Aldrich, N. J., & Tenenbaum, H. R. (2011). Does discovery-based instruction enhance learning? Journal of Educational Psychology, 103(1), 1–18. https://doi.org/10.1037/a0021017.Google Scholar
Alipour, M., Aminifar, E., Geary, D. C., & Ebrahimpour, R. (2023). Framing mathematical content in evolutionarily salient contexts improves students’ learning motivation. Learning and Motivation, 82, 101894. https://doi.org/10.1016/j.lmot.2023.101894.CrossRefGoogle Scholar
Amalric, M., & Dehaene, S. (2018). Cortical circuits for mathematical knowledge: Evidence for a major subdivision within the brain’s semantic networks. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1740), 20160515. https://doi.org/10.1098/rstb.2016.0515.CrossRefGoogle Scholar
Andrews-Hanna, J. R., Irving, Z. C., Fox, K. C. R., Spreng, R. N., & Christoff, K. (2017). The neuroscience of spontaneous thought: An evolving, interdisciplinary field. In Kieran, F., & Kieran, C. (Eds.), Oxford handbook of spontaneous thought and creativity (pp. 1–47). Oxford University Press.Google Scholar
Andrews-Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). The default network and self- generated thought: Component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences, 1316(1), 29–52. https://doi.org/10.1111/nyas.12360.CrossRefGoogle ScholarPubMed
Anglim, J., Dunlop, P. D., Wee, S. et al. (2022). Personality and intelligence: A meta-analysis. Psychological Bulletin, 148(5–6), 301–336. https://doi.org/10.1037/bul0000373.CrossRefGoogle Scholar
Arnatkeviciute, A., Fulcher, B. D., Oldham, S. et al. (2021). Genetic influences on hub connectivity of the human connectome. Nature Communications, 12(1), 4237. https://doi.org/10.1038/s41467-021-24306-2.CrossRefGoogle ScholarPubMed
Ashcraft, M. H. (2002). Math anxiety: Personal, educational, and cognitive consequences. Current Directions in Psychological Science, 11(5), 181–185. https://doi.org/10.1111/1467-8721.00196.CrossRefGoogle Scholar
Ashcraft, M. H., & Krause, J. A. (2007). Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review, 14(2), 243–248. https://doi.org/10.3758/BF03194059.CrossRefGoogle ScholarPubMed
Assaf, Y., Bouznach, A., Zomet, O., Marom, A., & Yovel, Y. (2020). Conservation of brain connectivity and wiring across the mammalian class. Nature Neuroscience, 23(7), 805–808. https://doi.org/10.1038/s41593-020-0641-7.CrossRefGoogle ScholarPubMed
Atit, K., Power, J. R., Pigott, T. et al. (2022).Examining the relations between spatial skills and mathematical performance: A meta-analysis. Psychonomic Bulletin & Review, 29, 699–720. https://doi.org/10.3758/s13423-021-02012-w.Google Scholar
Atran, S. (1998). Folk biology and the anthropology of science: Cognitive universals and cultural particulars. Behavioral and Brain Sciences, 21(4), 547–609. https://doi.org/10.1017/S0140525X98001277.CrossRefGoogle ScholarPubMed
Baddeley, A., Gathercole, S., & Papagno, C. (1998). The phonological loop as a language learning device. Psychological Review, 105(1), 158–173. https://doi.org/10.1037/0033-295X.105.1.158.CrossRefGoogle ScholarPubMed
Bae, C. J., Douka, K., & Petraglia, M. D. (2017). On the origin of modern humans: Asian perspectives. Science, 358(6368), eaai9067. https://doi.org/10.1126/science.aai9067.CrossRefGoogle ScholarPubMed
Bailey, D. H., & Geary, D. C. (2009). Hominid brain evolution: Testing climatic, ecological, and social competition models. Human Nature, 20(1), 67–79. https://doi.org/10.1007/s12110-008-9054-0.CrossRefGoogle Scholar
Baloglu, M., & Koçak, R. (2006). A multivariate investigation of the differences in mathematics anxiety. Personality and Individual Differences, 40(7), 1325–1335. https://doi.org/10.1016/j.paid.2005.10.009.CrossRefGoogle Scholar
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. https://doi.org/10.1146/annurev.psych.52.1.1.CrossRefGoogle ScholarPubMed
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20. https://doi.org/10.1016/j.tics.2017.10.001.CrossRefGoogle ScholarPubMed
Barkow, J. H. (1975). Prestige and culture: A biosocial interpretation. Current Anthropology, 16, 553–572. www.jstor.org/stable/2741630.CrossRefGoogle Scholar
Barks, S. K., Parr, L. A., & Rilling, J. K. (2015). The default mode network in chimpanzees (Pan troglodytes) is similar to that of humans. Cerebral Cortex, 25(2), 538–544. https://doi.org/10.1093/cercor/bht253.CrossRefGoogle ScholarPubMed
Barth, H. C., & Paladino, A. M. (2011). The development of numerical estimation: Evidence against a representational shift. Developmental Science, 14, 125–135. https://doi.org/10.1111/j.1467-7687.2010.00962.x.CrossRefGoogle ScholarPubMed
Barton, R. A., & Dean, P. (1993). Comparative evidence indicating neural specialization for predatory behaviour in mammals. Proceedings of the Royal Society of London B: Biological Sciences, 254(1339), 63–68. https://doi.org/10.1098/rspb.1993.0127.Google ScholarPubMed
Bassett, D. S., Wymbs, N. F., Porter, M. A. et al. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences of the United States of America, 108(18), 7641–7646. https://doi.org/10.1073/pnas.1018985108.CrossRefGoogle ScholarPubMed
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, 10–27. https://doi.org/10.1016/j.intell.2015.04.009.CrossRefGoogle Scholar
Beaty, R. E., Benedek, M., Barry Kaufman, S., & Silvia, P. J. (2015). Default and executive network coupling supports creative idea production. Scientific Reports, 5(1), 10964. https://doi.org/10.1038/srep10964.CrossRefGoogle Scholar
Bennett, S. H., Kirby, A. J., & Finnerty, G. T. (2018). Rewiring the connectome: Evidence and effects. Neuroscience & Biobehavioral Reviews, 88, 51–62. https://doi.org/10.1016/j.neubiorev.2018.03.001.CrossRefGoogle ScholarPubMed
Beran, M. J., Menzel, C. R., Parrish, A. E. et al. (2016). Primate cognition: Attention, episodic memory, prospective memory, self-control, and metacognition as examples of cognitive control in nonhuman primates. Wiley Interdisciplinary Reviews: Cognitive Science, 7(5), 294–316. https://doi.org/10.1002/wcs.1397.Google ScholarPubMed
Berlin, B., Breedlove, D. E., & Raven, P. H. (1966). Folk taxonomies and biological classification. Science, 154(3746), 273–275. https://doi.org/10.1126/science.154.3746.27.CrossRefGoogle ScholarPubMed
Berlin, B., Breedlove, D. E., & Raven, P. H. (1973). General principles of classification and nomenclature in folk biology. American Anthropologist, 75(1), 214–242. https://doi.org/10.1525/aa.1973.75.1.02a00140.CrossRefGoogle Scholar
Bernieri, F. J., Reznick, J. S., & Rosenthal, R. (1988). Synchrony, pseudosynchrony, and dissynchrony: Measuring the entrainment process in mother-infant interactions. Journal of Personality and Social Psychology, 54, 243–253. https://doi.org/10.1037/0022-3514.54.2.243.CrossRefGoogle Scholar
Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016). Optimally controlling the human connectome: The role of network topology. Scientific Reports, 6(1), 30770. https://doi.org/10.1038/srep30770.CrossRefGoogle ScholarPubMed
Bhaduri, A., Sandoval-Espinosa, C., Otero-Garcia, M. et al. (2021). An atlas of cortical arealization identifies dynamic molecular signatures. Nature, 598(7879), 200–204. https://doi.org/10.1038/s41586-021-03910-8.CrossRefGoogle ScholarPubMed
Bi, X., Zhou, L., Zhang, J. J. et al. (2023). Lineage-specific accelerated sequences underlying primate evolution. Science Advances, 9(22), eadc9507. https://doi.org/10.1126/sciadv.adc9507.CrossRefGoogle ScholarPubMed
Bjorklund, D. F. (2018). How children invented humanity. Child Development, 89(5), 1462–1466. https://doi.org/10.1111/cdev.13020.CrossRefGoogle ScholarPubMed
Bjorklund, D. F., & Pellegrini, A. D. (2002). The origins of human nature: Evolutionary developmental psychology. American Psychological Association.CrossRefGoogle Scholar
Bonfanti, L., & Charvet, C. J. (2021). Brain plasticity in humans and model systems: Advances, challenges, and future directions. International Journal of Molecular Sciences, 22(17), 9358. https://doi.org/10.3390/ijms22179358.CrossRefGoogle ScholarPubMed
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624–652. https://doi.org/10.1037/0033-295X.108.3.624.CrossRefGoogle ScholarPubMed
Bouhali, F., de Schotten, M. T., Pinel, P. et al. (2014). Anatomical connections of the visual word form area. Journal of Neuroscience, 34(46), 15402–15414. https://doi.org/10.1523/JNEUROSCI.4918-13.2014.CrossRefGoogle ScholarPubMed
Bradley, L., & Bryant, P. E. (1983). Categorizing sounds and learning to read – a causal connection. Nature, 301(5899), 419–421. https://doi.org/10.1038/301419a0.CrossRefGoogle Scholar
Brem, S., Maurer, U., Kronbichler, M. et al. (2020). Visual word form processing deficits driven by severity of reading impairments in children with developmental dyslexia. Scientific Reports, 10(1), 18728. https://doi.org/10.1038/s41598-020-75111-8.Google ScholarPubMed
Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. [Comparative localization of the cerebral cortex based on cell composition.] Barth.Google Scholar
Broglio, C., Martín-Monzón, I., Ocaña, F. M. et al. (2015). Hippocampal pallium and map-like memories through vertebrate evolution. Journal of Behavioral and Brain Science, 5(3), 54939. https://doi.org/10.4236/jbbs.2015.53011.CrossRefGoogle Scholar
Brothers, L., & Ring, B. (1992). A neuroethological framework for the representation of minds. Journal of Cognitive Neuroscience, 4(2), 107–118. https://doi.org/10.1162/jocn.1992.4.2.107.CrossRefGoogle ScholarPubMed
Brown, R. (1973). A first language: The early stages. Harvard University Press. https://doi.org/10.4159/harvard.9780674732469.CrossRefGoogle Scholar
Buckner, R. L., & DiNicola, L. M. (2019). The brain’s default network: Updated anatomy, physiology and evolving insights. Nature Reviews Neuroscience, 20(10), 593–608. https://doi.org/10.1038/s41583-019-0212-7.CrossRefGoogle ScholarPubMed
Bugental, D. B. (2000). Acquisition of the algorithms of social life: A domain-based approach. Psychological Bulletin, 126(2), 187–219. https://doi.org/10.1037/0033-2909.126.2.187.CrossRefGoogle ScholarPubMed
Burgos-Robles, A., Gothard, K. M., Monfils, M. H., Morozov, A., & Vicentic, A. (2019). Conserved features of anterior cingulate networks support observational learning across species. Neuroscience & Biobehavioral Reviews, 107, 215–228. https://doi.org/10.1016/j.neubiorev.2019.09.009.CrossRefGoogle ScholarPubMed
Burgoyne, A. P., & Engle, R. W. (2020). Attention control: A cornerstone of higher-order cognition. Current Directions in Psychological Science, 29(6), 624–630. https://doi.org/10.1177/0963721420969371.CrossRefGoogle Scholar
Cadwell, C. R., Bhaduri, A., Mostajo-Radji, M. A., Keefe, M. G., & Nowakowski, T. J. (2019). Development and arealization of the cerebral cortex. Neuron, 103(6), 980–1004. https://doi.org/10.1016/j.neuron.2019.07.009.CrossRefGoogle ScholarPubMed
Caporael, L. R. (1997). The evolution of truly social cognition: The core configurations model. Personality & Social Psychology Review, 1(4), 276–298. https://doi.org/10.1207/s15327957pspr0104_1.CrossRefGoogle ScholarPubMed
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press.CrossRefGoogle Scholar
Casey, B. M., & Ganley, C. M. (2021). An examination of gender differences in spatial skills and math attitudes in relation to mathematics success: A bio-psycho-social model. Developmental Review, 60, 100963. https://doi.org/10.1016/j.dr.2021.100963.CrossRefGoogle Scholar
Castaldi, E., Vignaud, A., & Eger, E. (2020). Mapping subcomponents of numerical cognition in relation to functional and anatomical landmarks of human parietal cortex. Neuroimage, 221, 117210. https://doi.org/10.1016/j.neuroimage.2020.117210.CrossRefGoogle ScholarPubMed
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22. https://doi.org/10.1037/h0046743.CrossRefGoogle Scholar
Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: A review of its functional anatomy and behavioural correlates. Brain, 129(3), 564–583. https://doi.org/10.1093/brain/awl004.CrossRefGoogle ScholarPubMed
Caviola, S., Toffalini, E., Giofrè, D. et al. (2022). Math performance and academic anxiety forms, from sociodemographic to cognitive aspects: A meta-analysis on 906,311 participants. Educational Psychology Review, 34(1), 363–399. https://doi.org/10.1007/s10648-021-09618-5.CrossRefGoogle Scholar
Chen, L., Wassermann, D., Abrams, D. A. et al. (2019). The visual word form area (VWFA) is part of both language and attention circuitry. Nature Communications, 10(1), 5601. https://doi.org/10.1038/s41467-019-13634-z.CrossRefGoogle ScholarPubMed
Cheung, P., Rubenson, M., & Barner, D. (2017). To infinity and beyond: Children generalize the successor function to all possible numbers years after learning to count. Cognitive Psychology, 92, 22–36. http://doi.org/10.1016/j.cogpsych.2016.11.002.CrossRefGoogle ScholarPubMed
Christov-Moore, L., Simpson, E. A., Coudé, G. et al. (2014). Empathy: Gender effects in brain and behavior. Neuroscience & Biobehavioral Reviews, 46(4), 604–627. https://doi.org/10.1016/j.neubiorev.2014.09.001.CrossRefGoogle ScholarPubMed
Clark, G. (2008). A farewell to alms: A brief economic history of the world. Princeton University Press.Google Scholar
Clark, G. (2016). Microbes and markets: Was the Black Death an economic revolution? Journal of Demographic Economics, 82(2), 139–165. https://doi.org/10.1017/dem.2016.6.CrossRefGoogle Scholar
Clement, J. (1982). Students’ preconceptions in introductory mechanics. American Journal of Physics, 50(1), 66–71. https://doi.org/10.1119/1.12989.CrossRefGoogle Scholar
Cohen, D. J., & Blanc-Goldhammer, D. (2011). Numerical bias in bounded and unbounded number line tasks. Psychonomic Bulletin & Review, 18, 331–338. https://doi.org/10.3758/s13423-011-0059-z.CrossRefGoogle ScholarPubMed
Cohen Kadosh, R., Bahrami, B., Walsh, V. et al. (2011). Specialization in the human brain: The case of numbers. Frontiers in Human Neuroscience, 5, 62. https://doi.org/10.3389/fnhum.2011.00062.Google ScholarPubMed
Cooper, G., & Sweller, J. (1987). Effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79(4), 347–362. https://doi.org/10.1037/0022-0663.79.4.347.CrossRefGoogle Scholar
Costantini, G., & Perugini, M. (2016). The network of conscientiousness. Journal of Research in Personality, 65, 68–88. https://doi.org/10.1016/j.jrp.2016.10.003.CrossRefGoogle Scholar
Coste, C. P., & Kleinschmidt, A. (2016). Cingulo-opercular network activity maintains alertness. Neuroimage, 128, 264–272. https://doi.org/10.1016/j.neuroimage.2016.01.026.CrossRefGoogle ScholarPubMed
Cowan, N. (1998). Attention and memory: An integrated framework. Oxford University Press.CrossRefGoogle Scholar
Cox, C., Bergmann, C., Fowler, E. et al. (2023). A systematic review and Bayesian meta-analysis of the acoustic features of infant-directed speech. Nature Human Behaviour, 7(1), 114–133. https://doi.org/10.1038/s41562-022-01452-1.CrossRefGoogle ScholarPubMed
Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. Irvington.Google Scholar
Csikszentmihalyi, M., & Hunter, J. (2003). Happiness in everyday life: The uses of experience sampling. Journal of Happiness Studies, 4, 185–199. https://doi.org/10.1023/A:1024409732742.CrossRefGoogle Scholar
Currie, T. E., Turchin, P., Turner, E., & Gavrilets, S. (2020). Duration of agriculture and distance from the steppe predict the evolution of large-scale human societies in Afro-Eurasia. Humanities and Social Sciences Communications, 7(1), 1–8. https://doi.org/10.1057/s41599-020-0516-2.CrossRefGoogle Scholar
Darwin, C., & Wallace, A. (1858). On the tendency of species to form varieties, and on the perpetuation of varieties and species by natural means of selection. Journal of the Linnean Society of London, Zoology, 3(9), 45–62. https://doi.org/10.1111/j.1096-3642.1858.tb02500.x.CrossRefGoogle Scholar
Davey, C. G., Pujol, J., & Harrison, B. J. (2016). Mapping the self in the brain’s default mode network. NeuroImage, 132, 390–397. https://doi.org/10.1016/j.neuroimage.2016.02.022.CrossRefGoogle ScholarPubMed
Davis, C. P., & Yee, E. (2019). Features, labels, space, and time: Factors supporting taxonomic relationships in the anterior temporal lobe and thematic relationships in the angular gyrus. Language, Cognition and Neuroscience, 34(10), 1347–1357. https://doi.org/10.1080/23273798.2018.1479530.CrossRefGoogle Scholar
De Dreu, C., Nijstad, B. A., & Baas, M. (2023). Human creativity: Functions, mechanisms and social conditioning. Advances in Experimental Social Psychology.Google Scholar
de Jong, T., Lazonder, A. W., Chinn, C. A. et al. (2023). Let’s talk evidence–The case for combining inquiry-based and direct instruction. Educational Research Review, 39, 100536. https://doi:10.1016/j.edurev.2023.100536.CrossRefGoogle Scholar
Dean, L. G., Vale, G. L., Laland, K. N., Flynn, E., & Kendal, R. L. (2014). Human cumulative culture: A comparative perspective. Biological Reviews, 89(2), 284–301. https://doi.org/10.1111/brv.12053.CrossRefGoogle ScholarPubMed
DeCasien, A. R., & Higham, J. P. (2019). Primate mosaic brain evolution reflects selection on sensory and cognitive specialization. Nature Ecology & Evolution, 3(10), 1483–1493. https://doi.org/10.1038/s41559-019-0969-0.CrossRefGoogle ScholarPubMed
DeCasien, A. R., Barton, R. A., & Higham, J. P. (2022). Understanding the human brain: insights from comparative biology. Trends in Cognitive Sciences, 26(5), 432–445.https://doi.org/10.1016/j.tics.2022.02.003.CrossRefGoogle ScholarPubMed
Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56(2), 384–398. https://doi.org/10.1016/j.neuron.2007.10.004.CrossRefGoogle ScholarPubMed
Dehaene, S., Izard, V., Spelke, E., & Pica, P. (2008). Log or linear? Distinct intuitions of the number scale in Western and Amazonian indigene cultures. Science, 320, 1217–1220. https://doi.org/10.1126/science.1156540.CrossRefGoogle ScholarPubMed
Dehaene, S., Pegado, F., Braga, L. W. et al. (2010). How learning to read changes the cortical networks for vision and language. Science, 330(6009), 1359–1364. https://doi.org/10.1126/science.1194140.CrossRefGoogle ScholarPubMed
Dehaene, S., Piazza, M., Pinel, P., & Cohen, L. (2003). Three parietal circuits for number processing. Cognitive Neuropsychology, 20(3–6), 487–506. https://doi.org/10.1080/02643290244000239.CrossRefGoogle ScholarPubMed
Dehaene-Lambertz, G., Montavont, A., Jobert, A. et al. (2010). Language or music, mother or Mozart? Structural and environmental influences on infants’ language networks. Brain and Language, 114(2), 53–65. https://doi.org/10.1016/j.bandl.2009.09.003.CrossRefGoogle ScholarPubMed
Demetriou, A., Spanoudis, G., Shayer, M. et al. (2014). Relations between speed, working memory, and intelligence from preschool to adulthood: Structural equation modeling of 14 studies. Intelligence, 46, 107–121. https://doi.org/10.1016/j.intell.2014.05.013.CrossRefGoogle Scholar
Derex, M. (2022). Human cumulative culture and the exploitation of natural phenomena. Philosophical Transactions of the Royal Society B, 377(1843), 20200311. https://doi.org/10.1098/rstb.2020.0311.CrossRefGoogle ScholarPubMed
Dillon, M. R., Huang, Y., & Spelke, E. S. (2013). Core foundations of abstract geometry. Proceedings of the National Academy of Sciences of the United States of America, 110, 14191–14195. https://doi.org/10.1073/pnas.1312640110.CrossRefGoogle ScholarPubMed
Dotan, D., & Dehaene, S. (2016). On the origins of logarithmic number-to-position mapping. Psychological Review, 123, 637–666. https://doi.org/10.1037/rev0000038.CrossRefGoogle ScholarPubMed
Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics anxiety: What have we learned in 60 years? Frontiers in Psychology, 7, 508. https://doi.org/10.3389/fpsyg.2016.00508.CrossRefGoogle ScholarPubMed
Dukas, R. (Ed.) (1998). Cognitive ecology: The evolutionary ecology of information processing and decision making. University of Chicago Press.Google Scholar
Dunbar, R. I. (1998). The social brain hypothesis. Evolutionary Anthropology, 6(5), 178–190.https://doi.org/10.1002/(SICI)1520-6505(1998)6:5<178::AID-EVAN5>3.0.CO;2-8.3.0.CO;2-8>CrossRefGoogle Scholar
Dunbar, R. I. M. (1993). Coevolution of neocortical size, group size and language in humans. Behavioral and Brain Sciences, 16(4), 681–735. https://doi.org/10.1017/S0140525X00032325.CrossRefGoogle Scholar
Durkee, P. K., Lukaszewski, A. W., & Buss, D. M. (2019). Pride and shame: Key components of a culturally universal status management system. Evolution and Human Behavior, 40, 470–478. https://doi.org/10.1016/j.evolhumbehav.2019.06.004.CrossRefGoogle Scholar
Eagly, A. H. (1987). Sex differences in social behavior: A social-role interpretation. Erlbaum.Google Scholar
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153.CrossRefGoogle ScholarPubMed
Elton, S., Bishop, L. C., & Wood, B. (2001). Comparative context of Plio-Pleistocene hominin brain evolution. Journal of Human Evolution, 41(1), 1–27. https://doi.org/10.1006/jhev.2001.0475.CrossRefGoogle ScholarPubMed
Engle, R. W., Kane, M. J., & Tuholski, S. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In Miyake, A., & Shah, P. (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 102–134). Cambridge University Press.CrossRefGoogle Scholar
Eskelson, T. C. (2020). How and why formal education originated in the emergence of civilization. Journal of Education and Learning, 9(2), 29–47. https://doi.org/10.5539/jel.v9n2p29.CrossRefGoogle Scholar
Esnaola, I., Sesé, A., Antonio-Agirre, I., & Azpiazu, L. (2020). The development of multiple self-concept dimensions during adolescence. Journal of Research on Adolescence, 30, 100–114. https://doi.org/10.1111/jora.12451.CrossRefGoogle ScholarPubMed
Fanta, V., Šálek, M., Zouhar, J., Sklenicka, P., & Storch, D. (2018). Equilibrium dynamics of European pre-industrial populations: The evidence of carrying capacity in human agricultural societies. Proceedings of the Royal Society B: Biological Sciences, 285(1871), 20172500. https://doi.org/10.1098/rspb.2017.2500.CrossRefGoogle ScholarPubMed
Faust, T. E., Gunner, G., & Schafer, D. P. (2021). Mechanisms governing activity-dependent synaptic pruning in the developing mammalian CNS. Nature Reviews Neuroscience, 22(11), 657–673. https://doi.org/10.1038/s41583-021-00507-y.CrossRefGoogle ScholarPubMed
Fedorenko, E., & Blank, I. A. (2020). Broca’s area is not a natural kind. Trends in Cognitive Sciences, 24(4), 270–284. https://doi.org/10.1016/j.tics.2020.01.001.Google ScholarPubMed
Feigenson, L. , Dehaene, S., & Spelke, E. (2004). Core systems of number. Trends in Cognitive Sciences, 8(7), 307–314. https://doi.org/10.1016/j.tics.2004.05.002.CrossRefGoogle ScholarPubMed
Feigenson, L., Libertus, M. E., & Halberda, J. (2013). Links between the intuitive sense of number and formal mathematics ability. Child Development Perspectives, 7, 74–79. http://dx.doi.org/10.1111/cdep.12019.CrossRefGoogle ScholarPubMed
Flinn, M. V., Geary, D. C., & Ward, C. V. (2005). Ecological dominance, social competition, and coalitionary arms races: Why humans evolved extraordinary intelligence. Evolution and Human Behavior, 26(1), 10–46. https://doi.org/10.1016/j.evolhumbehav.2004.08.005.CrossRefGoogle Scholar
Frey, S. H. (2008). Tool use, communicative gesture and cerebral asymmetries in the modern human brain. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1499), 1951–1957. https://doi.org/10.1098/rstb.2008.0008.CrossRefGoogle ScholarPubMed
Gallistel, C. R., & Gelman, R. (2000). Non-verbal numerical cognition: From reals to integers. Trends in Cognitive Sciences, 4(2), 59–65. https://doi.org/10.1016/s1364-6613(99)01424-2.CrossRefGoogle ScholarPubMed
Gallup, G. G. Jr (1998). Self-awareness and the evolution of social intelligence. Behavioural Processes, 42, 239–247. https://doi.org/10.1016/S0376-6357(97)00079-X.CrossRefGoogle ScholarPubMed
Galor, O., & Klemp, M. (2019). Human genealogy reveals a selective advantage to moderate fecundity. Nature Ecology & Evolution, 3(5), 853–857. https://doi.org/10.1038/s41559-019-0846-x.CrossRefGoogle ScholarPubMed
Gangopadhyay, P., Chawla, M., Dal Monte, O., & Chang, S. W. (2021). Prefrontal–amygdala circuits in social decision-making. Nature Neuroscience, 24(1), 5–18. https://doi.org/10.1038/s41593-020-00738-9.CrossRefGoogle ScholarPubMed
Garin, C. M., Hori, Y., Everling, S. et al. (2022). An evolutionary gap in primate default mode network organization. Cell Reports, 39(2), 110669. https://doi.org/10.1016/j.celrep.2022.110669.CrossRefGoogle ScholarPubMed
Geary, D. C. (1995). Reflections of evolution and culture in children’s cognition: Implications for mathematical development and instruction. American Psychologist, 50(1), 24–37. https://doi.org/10.1037/0003-066X.50.1.24.CrossRefGoogle ScholarPubMed
Geary, D. C. (2002). Principles of evolutionary educational psychology. Learning and Individual Differences, 12(4), 317–345. https://doi.org/10.1016/S1041-6080(02)00046-8.CrossRefGoogle Scholar
Geary, D. C. (2005). The origin of mind: Evolution of brain, cognition, and general intelligence. American Psychological Association. https://doi.org/10.1037/10871-000.CrossRefGoogle Scholar
Geary, D. C. (2007). Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology. In Carlson, J. S., & Levin, J. R. (Eds.), Educating the evolved mind (pp. 1–99, 177–202, Vol. 2, Psychological perspectives on contemporary educational issues). Information Age.Google Scholar
Geary, D. C. (2008). An evolutionarily informed education science. Educational Psychologist, 43(4), 279–295. https://doi.org/10.1080/00461520802392133.CrossRefGoogle Scholar
Geary, D. C. (2018). Efficiency of mitochondrial functioning as the fundamental biological mechanism of general intelligence (g). Psychological Review, 125(6), 1028–1050. https://doi.org/10.1037/rev0000124.CrossRefGoogle ScholarPubMed
Geary, D. C. (2020). Mitochondrial functions, cognition, and the evolution of intelligence: Reply to commentaries and moving forward. Journal of Intelligence, 8(4), 42. https://doi.org//10.3390/jintelligence8040042.CrossRefGoogle ScholarPubMed
Geary, D. C. (2021). Male, female: The evolution of human sex differences (3rd ed.). American Psychological Association.CrossRefGoogle ScholarPubMed
Geary, D. C. (2022). Sex, brain, and mathematics: An evolutionary perspective. Developmental Review, 63(1), 101010. https://doi.org/10.1016/j.dr.2021.101010.CrossRefGoogle Scholar
Geary, D. C., & Berch, D. B. (2016). Evolution and children’s cognitive and academic development. In Geary, D. C., & Berch, D. B. (Eds.), Evolutionary perspectives on child development and education (pp. 217–249). Springer.CrossRefGoogle Scholar
Geary, D. C., & Bjorklund, D. F. (2000). Evolutionary developmental psychology. Child Development, 71(1), 57–65. https://doi.org/10.1111/1467-8624.00118.CrossRefGoogle ScholarPubMed
Geary, D. C. & Huffman, K. J. (2002). Brain and cognitive evolution: Forms of modularity and functions of mind. Psychological Bulletin, 128(5), 667–698. https://doi.org/10.1037/0033-2909.128.5.667.CrossRefGoogle ScholarPubMed
Geary, D. C., & vanMarle, K. (2016). Young children’s core symbolic and non-symbolic quantitative knowledge in the prediction of later mathematics achievement. Developmental Psychology, 52, 2130–2144. http://doi.org/10.1037/dev0000214.CrossRefGoogle Scholar
Geary, D. C., & Xu, K. M. (2022). Evolution of self-awareness and the cultural emergence of academic and non-academic self-concepts. Educational Psychology Review, 34(4), 2323–2349. https://doi.org/10.1007/s10648-022-09669-2.CrossRefGoogle ScholarPubMed
Geary, D. C., Berch, D. B., & Mann Koepke, K. (Eds.) (2015). Evolutionary origins and early development of number processing. Elsevier Academic Press.Google Scholar
Geary, D. C., Scofield, J. E., Hoard, M. K., & Nugent, L. (2021). Boys’ advantage on the fractions number line is mediated by visuospatial attention: Evidence for a parietal-spatial contribution to number line learning. Developmental Science, 24, e13063. https://doi.org//10.1111/desc.13063.CrossRefGoogle ScholarPubMed
Geary, D. C., Hoard, M. K., Nugent, L., & Ünal, Z. E. (2023). Sex differences in developmental pathways to mathematical competence. Journal of Educational Psychology, 115, 212–228. https://doi.org/10.1037/edu0000763.CrossRefGoogle ScholarPubMed
Geary, D. C., Hoard, M. K., Nugent, L., Ünal, Z. E., & Greene, N. R. (2023). Sex differences and similarities in relations between mathematics achievement, attitudes, and anxiety: A 7th-to-9th grade longitudinal study. Journal of Educational Psychology, 115(5), 767–782. http://dx.doi.org/10.1037/edu0000793.CrossRefGoogle ScholarPubMed
Geary, D. C., vanMarle, K., Chu, F., Hoard, M. K., & Nugent, L. (2019). Predicting age of becoming a cardinal principle knower. Journal of Educational Psychology, 111(2), 256–267. https://doi.org/10.1037/edu0000277.CrossRefGoogle ScholarPubMed
Gelman, R. (1990). First principles organize attention to and learning about relevant data: Number and animate-inanimate distinction as examples. Cognitive Science, 14(1), 79–106. https://doi.org/10.1207/s15516709cog1401_5.Google Scholar
Gelman, S. A. (2003). The essential child: Origins of essentialism in everyday thought. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195154061.001.0001.CrossRefGoogle Scholar
Gernsbacher, M. A., & Kaschak, M. P. (2003). Neuroimaging studies of language production and comprehension. Annual Review of Psychology, 54, 91–114. https://doi.org//10.1146/annurev.psych.54.101601.145128.CrossRefGoogle ScholarPubMed
Ghio, M., Cara, C., & Tettamanti, M. (2021). The prenatal brain readiness for speech processing: A review on foetal development of auditory and primordial language networks. Neuroscience & Biobehavioral Reviews, 128, 709–719. https://doi.org/10.1016/j.neubiorev.2021.07.009.CrossRefGoogle ScholarPubMed
Goldin, C. (1999). A brief history of education in the United States (0898–2937). Cambridge, MA: National Bureau of Economic Research. www.nber.org/papers/h0119.CrossRefGoogle Scholar
Gong, Y., Greenbaum, J., & Deng, H.-W. (2019). A statistical approach to fine-mapping for the identification of potential causal variants related to human intelligence. Journal of Human Genetics, 64, 781–787. https://doi.org/10.1038/s10038-019-0623-3.CrossRefGoogle ScholarPubMed
Gopnik, A., & Wellman, H. M. (2012). Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory. Psychological Bulletin, 138(6), 1085–1108. https://doi.org/10.1037/a0028044.CrossRefGoogle ScholarPubMed
Gotlieb, R. J., Hyde, E., Immordino-Yang, M. H., & Kaufman, S. B. (2019). Imagination is the seed of creativity. In Kaufman, J. C., & Sternberg, R. J. (Eds.), The Cambridge handbook of creativity (2nd ed., pp. 709–731). Cambridge University Press.CrossRefGoogle Scholar
Gould, S. J., & Vrba, E. S. (1982). Exaptation – a missing term in the science of form. Paleobiology, 8(1), 4–15. https://doi.org/10.1017/S0094837300004310.CrossRefGoogle Scholar
Grasby, K. L., Jahanshad, N., Painter, J. N. et al. (2020). The genetic architecture of the human cerebral cortex. Science, 367(6484), eaay6690. https://doi.org/10.1126/science.aay6690.CrossRefGoogle ScholarPubMed
Gray, P. (2016). Children’s natural ways of educating themselves still work: Even for the three Rs. In Geary, D. C., & Berch, D. B. (Eds.), Evolutionary perspectives on child development and education (pp. 67–93). Springer. https://doi.org/10.1007/978-3-319-29986-0_3.CrossRefGoogle Scholar
Greenough, W. T. (1991). Experience as a component of normal development: Evolutionary considerations. Developmental Psychology, 27(1), 14–17. https://doi.org/10.1037/0012-1649.27.1.14.CrossRefGoogle Scholar
Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child Development, 58(3), 539–559. www.jstor.org/stable/1130197.CrossRefGoogle ScholarPubMed
Hadamard, J. (1945). The mathematician’s mind: Psychology of invention in the mathematical field. Dover.Google Scholar
Hagmann, P., Sporns, O., Madan, N. et al. (2010). White matter maturation reshapes structural connectivity in the late developing human brain. Proceedings of the National Academy of Sciences of the United States of America, 107(44), 19067–19072. https://doi.org/10.1073/pnas.1009073107.CrossRefGoogle ScholarPubMed
Halberda, J., & Feigenson, L. (2008). Developmental change in the acuity of the “Number Sense”: The approximate number system in 3-, 4-, 5-, and 6-year-olds and adults. Developmental Psychology, 44(5), 1457–1465. https://doi.org/10.1037/a0012682.CrossRefGoogle ScholarPubMed
Hamilton, M. J., & Walker, R. S. (2018). A stochastic density-dependent model of long-term population dynamics in hunter-gatherer populations. Evolutionary Ecology Research, 19(1), 85–102. www.evolutionary-ecology.com/issues/v19/n01/iiar3085.pdf.Google Scholar
Hamilton, W. D. (1964). The genetical evolution of social behaviour. II. Journal of Theoretical Biology, 7(1), 17–52. https://doi.org/10.1016/0022-5193(64)90039-6.CrossRefGoogle ScholarPubMed
Harter, S. (2006). The self. In Eisenberg, N., Damon, W., & Lerner, R. M. (Eds.), Handbook of child psychology: Social, emotional, and personality development (pp. 505–570). John Wiley.Google Scholar
Hawes, Z., & Ansari, D. (2020). What explains the relationship between spatial and mathematical skills? A review of evidence from brain and behavior. Psychonomic Bulletin & Review, 27, 465–482. https://doi.org/10.3758/s13423-019-01694-7.CrossRefGoogle ScholarPubMed
Hecht, E. E., Gutman, D. A., Bradley, B. A., Preuss, T. M., & Stout, D. (2015). Virtual dissection and comparative connectivity of the superior longitudinal fasciculus in chimpanzees and humans. Neuroimage, 108, 124–137. https://doi.org/10.1016/j.neuroimage.2014.12.039.CrossRefGoogle ScholarPubMed
Hegarty, M. (2004). Mechanical reasoning by mental simulation. Trends in Cognitive Sciences, 8(6), 280–285. https://doi.org/10.1016/j.tics.2004.04.001.CrossRefGoogle ScholarPubMed
Heinonen, J., Numminen, J., Hlushchuk, Y. et al. (2016). Default mode and executive networks areas: Association with the serial order in divergent thinking. PLoS ONE, 11(9), e0162234. https://doi.org/10.1371/journal.pone.0162234.CrossRefGoogle ScholarPubMed
Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21(1), 33–46. https://doi.org/10.2307/749455.CrossRefGoogle Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–135. https://doi.org/10.1017/S0140525X0999152X.CrossRefGoogle ScholarPubMed
Herbet, G., & Duffau, H. (2020). Revisiting the functional anatomy of the human brain: Toward a meta-networking theory of cerebral functions. Physiological Reviews, 100(3), 1181–1228. https://doi.org/10.1152/physrev.00033.2019.Google Scholar
Hermann, R., Dolfini, A., Crellin, R. J., Wang, Q., & Uckelmann, M. (2020). Bronze age swordsmanship: New insights from experiments and wear analysis. Journal of Archaeological Method and Theory, 27(4), 1040–1083. https://doi.org/10.1007/s10816-020-09451-0.CrossRefGoogle Scholar
Hewstone, M., Rubin, M., & Willis, H. (2002). Intergroup bias. Annual Review of Psychology, 53, 575–604. https://doi.org/10.1146/annurev.psych.53.100901.135109.CrossRefGoogle ScholarPubMed
Hirschfeld, K. (2015). Gangster states: Organized crime, kleptocracy and political collapse. Palgrave-MacMillan.CrossRefGoogle Scholar
Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized general intelligence. Journal of Educational Psychology, 57(5), 253–270. https://doi.org/10.1037/h0023816.CrossRefGoogle Scholar
Horowitz, D. L. (2001). The deadly ethnic riot. University of California Press.CrossRefGoogle Scholar
House, E., Glass, G., McLean, L., & Walker, D. (1978). No simple answer: Critique of the Follow through evaluation. Harvard Educational Review, 48, 462–464. https://doi.org/10.17763/haer.48.2.j2167r4594027x87.CrossRefGoogle Scholar
Howard-Jones, P. A., Jay, T., Mason, A., & Jones, H. (2016). Gamification of learning deactivates the default mode network. Frontiers in Psychology, 6, 1891. https://doi.org/10.3389/fpsyg.2015.01891.CrossRefGoogle ScholarPubMed
Hsin, A., & Xie, Y. (2014). Explaining Asian Americans’ academic advantage over whites. Proceedings of the National Academy of Sciences of the United America, 111, 8416–8421. https://doi.org/10.1073/pnas.1406402111.CrossRefGoogle ScholarPubMed
Hubbard, E. M., Piazza, M., Pinel, P., & Dehaene, S. (2005). Interactions between number and space in parietal cortex. Nature Reviews Neuroscience, 6, 435–448. https://doi.org/10.1038/nrn1684.CrossRefGoogle ScholarPubMed
Huffman, K. J., Nelson, J., Clarey, J., & Krubitzer, L. (1999). Organization of somatosensory cortex in three species of marsupials, Dasyurus hallucatus, Dactylopsila trivirgata, and Monodelphis domestica: Neural correlates of morphological specializations. Journal of Comparative Neurology, 403(1), 5–32. https://doi.org/10.1002/(SICI)1096-9861(19990105)403:1<5::AID-CNE2>3.0.CO;2-F.Google ScholarPubMed
Humphrey, N. K. (1976). The social function of intellect. In Bateson, P. P. G., & Hinde, R. A. (Eds.), Growing points in ethology (pp. 303–317). Cambridge University Press.Google Scholar
Hunter, J. E., & Schmidt, F. L. (1996). Intelligence and job performance: Economic and social implications. Psychology, Public Policy, and Law, 2(3–4), 447–472. https://doi.org/10.1037/1076-8971.2.3-4.447.CrossRefGoogle Scholar
Imuta, K., Henry, J. D., Slaughter, V., Selcuk, B., & Ruffman, T. (2016). Theory of mind and prosocial behavior in childhood: A meta-analytic review. Developmental Psychology, 52(8), 1192–1205. https://doi.org/10.1037/dev0000140.CrossRefGoogle ScholarPubMed
Jensen, A. R., & Munro, E. (1979). Reaction time, movement time, and intelligence. Intelligence, 3(2), 121–126. https://doi.org/10.1016/0160-2896(79)90010-2.CrossRefGoogle Scholar
Jernigan, T. L., Baaré, W. F., Stiles, J., & Madsen, K. S. (2011). Postnatal brain development: Structural imaging of dynamic neurodevelopmental processes. Progress in Brain Research, 189, 77–92. https://doi.org/10.1016/B978-0-444-53884-0.00019-1.CrossRefGoogle ScholarPubMed
Jitendra, A. K., & Woodward, J. (2019). The role of visual representations in mathematical word problems. In Geary, D. C., Berch, D. B., & Koepke, K. Mann (Eds.), Cognitive foundations for improving mathematical learning (pp. 269–293). Elsevier Academic Press.CrossRefGoogle Scholar
Johnson, M. H., Senju, A., & Tomalski, P. (2015). The two-process theory of face processing: Modifications based on two decades of data from infants and adults. Neuroscience & Biobehavioral Reviews, 50, 169–179. https://doi.org/10.1016/j.neubiorev.2014.10.009.CrossRefGoogle 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. https://doi.org/10.1017/S0140525X07001185.CrossRefGoogle ScholarPubMed
Kaas, J. H. (1982). The segregation of function in the nervous system: Why do the sensory systems have so many subdivisions? Contributions to Sensory Physiology, 7, 201–240. https://doi.org/10.1016/B978-0-12-151807-3.50012-4.CrossRefGoogle Scholar
Kaiser, M. K., McCloskey, M., & Proffitt, D. R. (1986). Development of intuitive theories of motion: Curvilinear motion in the absence of external forces. Developmental Psychology, 22(1), 67–71. doi.org/10.1037/0012-1649.22.1.67.CrossRefGoogle Scholar
Kanazawa, S. (2008). Temperature and evolutionary novelty as forces behind the evolution of general intelligence. Intelligence, 36(2), 99–108. https://doi.org/10.1016/j.intell.2007.04.001.CrossRefGoogle 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), 637–671. https://doi.org/10.3758/BF03196323.CrossRefGoogle ScholarPubMed
Kaplan, H., Hill, K., Lancaster, J., & Hurtado, A. M. (2000). A theory of human life history evolution: Diet, intelligence, and longevity. Evolutionary Anthropology, 9(4), 156–185. https://doi.org/10.1002/1520-6505(2000)9:4<156::AID-EVAN5>3.0.CO;2-7.3.0.CO;2-7>CrossRefGoogle Scholar
Kell, H. J., Lubinski, D., Benbow, C. P., & Steiger, J. H. (2013). Creativity and technical innovation: Spatial ability’s unique role. Psychological Science, 24, 1831–1836. https://doi.org/10.1177/0956797613478615.CrossRefGoogle ScholarPubMed
Keunen, K., Counsell, S. J., & Benders, M. J. (2017). The emergence of functional architecture during early brain development. Neuroimage, 160, 2–14. https://doi.org/10.1016/j.neuroimage.2017.01.047.CrossRefGoogle ScholarPubMed
Kim, D., & Opfer, J. E. (2018). Dynamics and development in number-to-space mapping. Cognitive Psychology, 107, 44–66. https://doi.org/10.1016/j.cogpsych.2018.10.001.CrossRefGoogle ScholarPubMed
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1.CrossRefGoogle Scholar
Kline, M. A. (2015). How to learn about teaching: An evolutionary framework for the study of teaching behavior in humans and other animals. Behavioral and Brain Sciences, 38, e31. https://doi.org/10.1017/S0140525X14000090.CrossRefGoogle Scholar
Konner, M. (2010). The evolution of childhood: Relationships, emotion, mind. Harvard University Press.Google Scholar
Konu, D., Turnbull, A., Karapanagiotidis, T., et al. (2020). A role for the ventromedial prefrontal cortex in self-generated episodic social cognition. Neuroimage, 218, 116977. https://doi.org/10.1016/j.neuroimage.2020.116977.CrossRefGoogle ScholarPubMed
Kriegbaum, K., Becker, N., & Spinath, B. (2018). The relative importance of intelligence and motivation as predictors of school achievement: A meta-analysis. Educational Research Review, 25, 120–148. https://doi.org/10.1016/j.edurev.2018.10.001.CrossRefGoogle Scholar
Krubitzer, L. (1995). The organization of neocortex in mammals: Are species differences really so different? Trends in Neurosciences, 18(9), 408–417. https://doi.org/10.1016/0166-2236(95)93938-T.CrossRefGoogle ScholarPubMed
Kuhl, P. K. (2010). Brain mechanisms in early language acquisition. Neuron, 67(5), 713–727. https://doi.org/10.1016/j.neuron.2010.08.038.CrossRefGoogle ScholarPubMed
Kuhl, P. K., Andruski, J. E., Chistovich, I. A. et al. (1997). Cross-language analysis of phonetic units in language addressed to infants. Science, 277(5326), 684–686. https://doi.org/10.1126/science.277.5326.684.CrossRefGoogle ScholarPubMed
Kühn, S., Ritter, S. M., Müller, B. C. et al. (2014). The importance of the default mode network in creativity – A structural MRI study. The Journal of Creative Behavior, 48(2), 152–163. https://doi.org/10.1002/jocb.45CrossRefGoogle Scholar
Lancy, D. F. (2016). Teaching: Natural or cultural? In Geary, D. C., & Berch, D. B. (Eds.), Evolutionary perspectives on child development and education (pp. 33–65). Springer.CrossRefGoogle Scholar
Leary, M. R., & Buttermore, N. R. (2003). The evolution of the human self: Tracing the natural history of self-awareness. Journal for the Theory of Social Behaviour, 33, 365–404. https://doi.org/10.1046/j.1468-5914.2003.00223.x.CrossRefGoogle Scholar
LeFevre, J. A., Fast, L., Skwarchuk, S. L. et al. (2010). Pathways to mathematics: Longitudinal predictors of performance. Child Development, 81, 1753–1767. https://doi.org/10.1111/j.1467-8624.2010.01508.x.CrossRefGoogle ScholarPubMed
Legare, C. H. (2017). Cumulative cultural learning: Development and diversity. Proceedings of the National Academy of Sciences of the United States of America, 114(30), 7877–7883. https://doi.org/10.1073/pnas.1620743114.CrossRefGoogle ScholarPubMed
Lehman, H. C. (1946). The exponential increase of man’s cultural output. Social Forces, 25, 281–290. https://doi.org/10.2307/3005665.CrossRefGoogle Scholar
Lenneberg, E. H. (1969). On Explaining Language: The development of language in children can best be understood in the context of developmental biology. Science, 164(3880), 635–643. https://doi.org/10.1126/science.164.3880.635.CrossRefGoogle Scholar
Leslie, A. M., Friedman, O., & German, T. P. (2004). Core mechanisms in “theory of mind.” Trends in Cognitive Sciences, 8(12), 528–533. https://doi.org/10.1016/j.tics.2004.10.001.CrossRefGoogle ScholarPubMed
Lespiau, F., & Tricot, A. (2019). Using primary knowledge: An efficient way to motivate students and promote the learning of formal reasoning. Educational Psychology Review, 31, 915–938. https://doi.org/10.1007/s10648-019-09482-4.CrossRefGoogle Scholar
Lespiau, F., & Tricot, A. (2022a). Primary vs. secondary knowledge contents in reasoning: Motivated and efficient vs. overburdened. Acta Psychologica, 227, 103610. https://doi.org/10.1016/j.actpsy.2022.103610.CrossRefGoogle ScholarPubMed
Lespiau, F., & Tricot, A. (2022b). Using primary knowledge in unpopular statistics exercises. Educational Psychology Review, 34(4), 2297–2322. https://doi.org/10.1007/s10648-022-09699-w.CrossRefGoogle Scholar
Levine, S. C., & Pantoja, N. (2021). Development of children’s math attitudes: Gender differences, key socializers, and intervention approaches. Developmental Review, 62(1), 100997. doi.org/10.1016/j.dr.2021.100997.CrossRefGoogle Scholar
Lewis, A. B., & Mayer, R. E. (1987). Students’ miscomprehension of relational statements in arithmetic word problems. Journal of Educational Psychology, 79, 363–371. https://doi.org/10.1037/0022-0663.79.4.363.CrossRefGoogle Scholar
Lewis, A. B. (1989). Training students to represent arithmetic word problems. Journal of Educational Psychology, 81, 521–531. https://doi.org/10.1037/0022-0663.81.4.521CrossRefGoogle Scholar
Li, J., Osher, D. E., Hansen, H. A., & Saygin, Z. M. (2020). Innate connectivity patterns drive the development of the visual word form area. Scientific Reports, 10(1), 18039. https://doi.org/10.1038/s41598-020-75015-7.Google ScholarPubMed
Libertus, M. E., Halberda, J., & Feigenson, L. (2011). Preschool acuity of the Approximate Number System correlates with math abilities. Developmental Science, 14, 1292–1300. dx.doi.org/10.1111/j.1467-7687.2011.01080.x.CrossRefGoogle Scholar
Longo, M. R., & Lourenco, S. F. (2007). Spatial attention and the mental number line: Evidence for characteristic biases and compression. Neuropsychologia, 45, 1400–1407. https://doi.org/10.1016/j.neuropsychologia.2006.11.002.CrossRefGoogle ScholarPubMed
Lou, H. C., Changeux, J. P., & Rosenstand, A. (2017). Towards a cognitive neuroscience of self- awareness. Neuroscience & Biobehavioral Reviews, 83(1), 765–773. https://doi.org/10.1016/j.neubiorev.2016.04.004.CrossRefGoogle ScholarPubMed
Lubinski, D. (2000). Scientific and social significance of assessing individual differences: “Sinking shafts at a few critical points.” Annual Review of Psychology, 51, 405–444. https://doi.org/10.1146/annurev.psych.51.1.405.CrossRefGoogle Scholar
Lukas, D., & Clutton-Brock, T. H. (2018). Social complexity and kinship in animal societies. Ecology Letters, 21(8), 1129–1134. https://doi.org/10.1111/ele.13079.CrossRefGoogle ScholarPubMed
Lyons, I. M., & Beilock, S. L. (2012). When math hurts: Math anxiety predicts pain network activation in anticipation of doing math. PLoS ONE, 7(10), e48076. https://doi.org/10.1371/journal.pone.0048076.CrossRefGoogle ScholarPubMed
Ma, X. (1999). A meta-analysis of the relationship between anxiety toward mathematics and achievement in mathematics. Journal for Research in Mathematics Education, 30(5), 520–540. https://doi.org/10.2307/749772.CrossRefGoogle Scholar
Ma, X., & Xu, J. (2004). The causal ordering of mathematics anxiety and mathematics achievement: A longitudinal panel analysis. Journal of Adolescence, 27(2), 165–179. https://doi.org/10.1016/j.adolescence.2003.11.003.CrossRefGoogle ScholarPubMed
Mac Arthur, R. H., & Wilson, E. O. (1967). The theory of island biogeography. Princeton University Press.Google Scholar
Mackintosh, N. J., & Bennett, E. S. (2003). The fractionation of working memory maps onto different components of intelligence. Intelligence, 31(6), 519–531. https://doi.org/10.1016/S0160-2896(03)00052-7.CrossRefGoogle Scholar
Malt, B. C. (1995). Category coherence in cross-cultural perspective. Cognitive Psychology, 29(2), 85–148. doi.org/10.1006/cogp.1995.1013.CrossRefGoogle Scholar
Malthus, T. R. (1798). An essay on the principle of population as it affects the future improvement of society with remarks on the speculations of Mr. Godwin, M. Condorcet, and other writers. Printed for J. Johnson, in St. Paul’s church-yard.Google Scholar
Mancuso, L., Cavuoti-Cabanillas, S., Liloia, D. et al. (2022). Tasks activating the default mode network map multiple functional systems. Brain Structure and Function, 227(5), 1711–1734. https://doi.org/10.1007/s00429-022-02467-0.CrossRefGoogle ScholarPubMed
Mann, V. A. (1984). Reading skill and language skill. Developmental Review, 4(1), 1–15. https://doi.org/10.1016/0273-2297(84)90014-5.CrossRefGoogle Scholar
Margett-Jordan, T., Falcon, R. G., & Witherington, D. C. (2017). The development of preschoolers’ living kinds concept: A longitudinal study. Child Development, 88(4), 1350–1367. https://doi.org/10.1111/cdev.12709.CrossRefGoogle ScholarPubMed
Marron, T. R., Lerner, Y., Berant, E. et al. (2018). Chain free association, creativity, and the default mode network. Neuropsychologia, 118, 40–58. https://doi.org/10.1016/j.neuropsychologia.2018.03.018.CrossRefGoogle ScholarPubMed
Marsh, H. W., Hau, K.-T., Artelt, C., Baumert, J., & Peschar, J. L. (2006). OECD’s brief self- report measure of educational psychology’s most useful affective constructs: Cross-cultural, psychometric comparisons across 25 countries. International Journal of Testing, 6, 311–360. https://doi.org/10.1207/s15327574ijt0604_1.CrossRefGoogle Scholar
Marsh, H. W., & Martin, A. J. (2011). Academic self-concept and academic achievement: Relations and causal ordering. British Journal of Educational Psychology, 81, 59–77. https://doi.org/10.1348/000709910X503501.CrossRefGoogle ScholarPubMed
Marsh, H. W., & Shavelson, R. (1985). Self-concept: Its multifaceted, hierarchical structure. Educational Psychologist, 20, 107–123. https://doi.org/10.1207/s15326985ep2003_1.CrossRefGoogle Scholar
Martin, P. S. (1973). The discovery of America: The first Americans may have swept the Western Hemisphere and decimated its fauna in 1000 years. Science, 179(4077), 969–974. https://doi.org/10.1126/science.179.4077.969.CrossRefGoogle ScholarPubMed
Martin, A. E., & Slepian, M. L. (2021). The primacy of gender: Gendered cognition underlies the big two dimensions of social cognition. Perspectives on Psychological Science, 16(6), 1143–1158. https://doi.org/10.1177/1745691620904961.CrossRefGoogle ScholarPubMed
Martin-Ordas, G. (2020). It is about time: Conceptual and experimental evaluation of the temporal cognitive mechanisms in mental time travel. Wiley Interdisciplinary Reviews: Cognitive Science, 11(6), e1530. https://doi.org/10.1002/wcs.1530.Google ScholarPubMed
Matchin, W. G. (2018). A neuronal retuning hypothesis of sentence-specificity in Broca’s area. Psychonomic Bulletin & Review, 25, 1682–1694. https://doi.org/10.3758/s13423-017-1377-6.CrossRefGoogle ScholarPubMed
Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59(1), 14–19. https://doi.org/10.1037/0003-066X.59.1.14.CrossRefGoogle ScholarPubMed
McCandliss, B. D., Cohen, L., & Dehaene, S. (2003). The visual word form area: Expertise for reading in the fusiform gyrus. Trends in Cognitive Sciences, 7(7), 293–299. https://doi.org/10.1016/S1364-6613(03)00134-7.CrossRefGoogle ScholarPubMed
McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10. https://doi.org/10.1016/j.intell.2008.08.004.CrossRefGoogle Scholar
McNally, R. J. (1987). Preparedness and phobias: A review. Psychological Bulletin, 101(2), 283–303. https://doi.org/10.1037/0033-2909.101.2.283.CrossRefGoogle ScholarPubMed
Medin, D. L., & Atran, S. (2004). The native mind: Biological categorization and reasoning in development and across cultures. Psychological Review, 111(4), 960–983. https://doi.org/10.1037/0033-295X.111.4.960.CrossRefGoogle ScholarPubMed
Medin, D. L., Ross, N. O., Atran, S. et al. (2006). Folkbiology of freshwater fish. Cognition, 99(3), 237–273. https://doi.org/10.1016/j.cognition.2003.12.005.CrossRefGoogle ScholarPubMed
Menon, V., & Chang, H. (2021). Emerging neurodevelopmental perspectives on mathematical learning. Developmental Review, 60, 100964. https://doi.org/10.1016/j.dr.2021.100964.CrossRefGoogle ScholarPubMed
Menon, V., & D’Esposito, M. (2022). The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology, 47(1), 90–103. https://doi.org/10.1038/s41386-021-01152-w.CrossRefGoogle ScholarPubMed
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214, 655–667. https://doi.org/10.1007/s00429-010-0262-0.CrossRefGoogle ScholarPubMed
Mesoudi, A., & Thornton, A. (2018). What is cumulative cultural evolution?. Proceedings of the Royal Society B, 285(1880), 20180712. https://doi.org/10.1098/rspb.2018.0712.CrossRefGoogle ScholarPubMed
Meyer, L. A. (1984). Long-term academic effects of the direct instruction project follow through. The Elementary School Journal, 84, 380–394. https://doi.org/10.1086/461371.CrossRefGoogle Scholar
Meyer, L. A., Gersten, R. M., & Gutkin, J. (1983). Direct instruction: A project follow through success story in an inner-city school. The Elementary School Journal, 84, 241–252. https://doi.org/10.1086/461360.CrossRefGoogle Scholar
Middleton, W. K. (1963). The place of Torricelli in the history of the barometer. Isis, 54(1), 11–28. www.jstor.org/stable/228726.Google Scholar
Milner, A. D., & Goodale, M. A. (2006). The visual brain in action (2nd ed). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198524724.001.0001.CrossRefGoogle Scholar
Mithen, S. (1996). The prehistory of the mind: The cognitive origins of art and science. Thames and Hudson.Google Scholar
Mix, K. S. (2019). Why are spatial skill and mathematics related? Child Development Perspectives, 13, 121–126. https://doi.org/10.1111/cdep12323.CrossRefGoogle Scholar
Miyake, A., & Friedman, N. P. (2012). The nature and organization of individual differences in executive functions: Four general conclusions. Current Directions in Psychological Science, 21(1), 8–14. https://doi.org/10.1177/0963721411429458.CrossRefGoogle ScholarPubMed
Molnar-Szakacs, I., & Uddin, L. Q. (2022). Anterior insula as a gatekeeper of executive control. Neuroscience & Biobehavioral Reviews, 139, 104736. https://doi.org/10.1016/j.neubiorev.2022.104736.CrossRefGoogle ScholarPubMed
Monosov, I. E., Haber, S. N., Leuthardt, E. C., & Jezzini, A. (2020). Anterior cingulate cortex and the control of dynamic behavior in primates. Current Biology, 30(23), R1442–R1454. https://doi.org/10.1016/j.cub.2020.10.009.CrossRefGoogle ScholarPubMed
Mou, Y., & vanMarle, K. (2014). Two core systems of numerical representation in infants. Developmental Review, 34(1), 1–25. https://doi.org/10.1016/j.dr.2013.11.001.CrossRefGoogle Scholar
Moulton, E., Bouhali, F., Monzalvo, K. et al. (2019). Connectivity between the visual word form area and the parietal lobe improves after the first year of reading instruction: A longitudinal MRI study in children. Brain Structure and Function, 224, 1519–1536. https://doi.org/10.1007/s00429-019-01855-3.Google ScholarPubMed
Murray, C. (2003). Human accomplishment: The pursuit of excellence in the arts and sciences, 800 B.C. to 1950. HarperCollins.Google Scholar
Murray, E. A., Wise, S. P., & Graham, K. S. (2018). Representational specializations of the hippocampus in phylogenetic perspective. Neuroscience Letters, 680(1), 4–12. https://doi.org/10.1016/j.neulet.2017.04.065.CrossRefGoogle ScholarPubMed
Mussolin, C., Nys, J., Leybaert, J., & Content, A. (2016). How approximate and exact number skills are related to each other across development: A review. Developmental Review, 39, 1–15. https://doi.org/10.1016/j.dr.2014.11.001.CrossRefGoogle Scholar
Neisser, U. (1988). Five kinds of self-knowledge. Philosophical Psychology, 1, 35–59. https://doi.org/10.1080/09515088808572924.CrossRefGoogle Scholar
Newcombe, N. S., Uttal, D. H., & Sauter, M. (2013). Spatial development. In Zelazo, P. (Ed.), Oxford handbook of developmental psychology (pp. 564–590). Oxford University Press.Google Scholar
Newton, I. (1995). The principia (A. Motte, Trans.). Prometheus Books. (Original work published in 1687).Google Scholar
Nguyen, V., Versyp, O., Cox, C., & Fusaroli, R. (2022). A systematic review and Bayesian meta-analysis of the development of turn taking in adult–child vocal interactions. Child Development, 93(4), 1181–1200. https://doi.org/10.1111/cdev.13754.CrossRefGoogle ScholarPubMed
Nicolson, R. I., & Fawcett, A. J. (2019). Development of dyslexia: The delayed neural commitment framework. Frontiers in Behavioral Neuroscience, 13, 112. https://doi.org/10.3389/fnbeh.2019.00112.CrossRefGoogle ScholarPubMed
Northcutt, R. G., & Kaas, J. H. (1995). The emergence and evolution of mammalian neocortex. Trends in Neurosciences, 18(9), 373–379. https://doi.org/10.1016/0166-2236(95)93932-N.CrossRefGoogle ScholarPubMed
Núñez, R. E. (2008). Reading between the number lines. Science, 321(5894), 1293–1294. https://doi.org/10.1126/science.321.5894.1293.CrossRefGoogle ScholarPubMed
Öhman, A., & Mineka, S. (2001). Fears, phobias, and preparedness: Toward an evolved module of fear and fear learning. Psychological Review, 108(3), 483–522. https://doi.org/10.1037/0033-295X.108.3.483.CrossRefGoogle ScholarPubMed
O’Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map. Oxford University Press.Google Scholar
O’Leary, D. D. M., Schlaggar, B. L., & Tuttle, R. (1994). Specification of neocortical areas and thalamocortical connections. Annual Review of Neuroscience, 17, 419–439. https://doi.org/10.1146/annurev.ne.17.030194.002223.CrossRefGoogle ScholarPubMed
Park, G., Lubinski, D., & Benbow, C. P. (2007). Contrasting intellectual patterns predict creativity in the arts and sciences: Tracking intellectually precocious youth over 25 years. Psychological Science, 18(11), 948–952. https://doi.org/10.1111/j.1467-9280.2007.02007.CrossRefGoogle ScholarPubMed
Pascalis, O., de Haan, M., & Nelson, C. A. (2002). In face processing species-specific during the first year of life? Science, 296(5571), 1321–1323. https://doi.org/10.1126/science.1070223.CrossRefGoogle ScholarPubMed
Paulesu, E., Démonet, J. F., Fazio, F. et al. (2001). Dyslexia: Cultural diversity and biological unity. Science, 291(5511), 2165–2167. https://doi.org/10.1126/science.1057179.CrossRefGoogle ScholarPubMed
Penn, D. C., Holyoak, K. J., & Povinelli, D. J. (2008). Darwin’s mistake: Explaining the discontinuity between human and nonhuman minds. Behavioral and Brain Sciences, 31(2), 109–130. https://doi.org/10.1017/S0140525X08003543.CrossRefGoogle ScholarPubMed
Peter, V., Kalashnikova, M., Santos, A., & Burnham, D. (2016). Mature neural responses to infant-directed speech but not adult-directed speech in pre-verbal infants. Scientific Reports, 6(1), 34273. https://doi.org/10.1038/srep34273.CrossRefGoogle Scholar
Piazza, M., Pinel, P., Le Bihan, D., & Dehaene, S. (2007). A magnitude code common to numerosities and number symbols in human intraparietal cortex. Neuron, 53, 293–305. https://doi.org/10.1016/j.neuron.2006.11.022.CrossRefGoogle ScholarPubMed
Pinker, S., & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences, 13(4), 707–784. https://doi.org/10.1017/S0140525X00081061.CrossRefGoogle Scholar
Pinsof, D., Sears, D. O., & Haselton, M. G. (2023). Strange bedfellows: The Alliance Theory of political belief systems. Psychological Inquiry, 34(3), 139–160. https://doi.org/10.1080/1047840X.2023.2274433.CrossRefGoogle Scholar
Pletzer, B., Kronbichler, M., Nuerk, H. C., & Kerschbaum, H. H. (2015). Mathematics anxiety reduces default mode network deactivation in response to numerical tasks. Frontiers in Human Neuroscience, 9, 202. https://doi.org/10.3389/fnhum.2015.00202.CrossRefGoogle ScholarPubMed
Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135(2), 322–338. https://doi.org/10.1037/a0014996.CrossRefGoogle ScholarPubMed
Posner, M. I. (2023). The evolution and future development of attention networks. Journal of Intelligence, 11(6), 98. https://doi.org/10.3390/jintelligence11060098.CrossRefGoogle ScholarPubMed
Posner, M. I., & Rothbart, M. K. (2009). Toward a physical basis of attention and self-regulation. Physics of Life Reviews, 6(2), 103–120. https://doi.org/10.1016/j.plrev.2009.02.001.CrossRefGoogle Scholar
Potts, R. (1998). Variability selection in hominid evolution. Evolutionary Anthropology, 7(3), 81–96. https://doi.org/10.1002/(SICI)1520-6505(1998)7:3<81::AID-EVAN3>3.0.CO;2-A.3.0.CO;2-A>CrossRefGoogle Scholar
Povinelli, D. J. (2000). Folk physics for apes: The chimpanzee’s theory of how the world works. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198572190.001.0001.Google Scholar
Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79, 798–813. https://doi.org/10.1016/j.neuron.2013.07.035.CrossRefGoogle ScholarPubMed
Preuss, T. M., & Wise, S. P. (2022). Evolution of prefrontal cortex. Neuropsychopharmacology, 47(1), 3–19. https://doi.org/10.1038/s41386-021-01076-5.CrossRefGoogle ScholarPubMed
Price, C. J., & Mechelli, A. (2005). Reading and reading disturbance. Current Opinion in Neurobiology, 15(2), 231–238. https://doi.org/10.1016/j.conb.2005.03.003.CrossRefGoogle ScholarPubMed
Pugh, K. R., Shaywitz, B. A., Shaywitz, S. E. et al. (1997). Predicting reading performance from neuroimaging profiles: The cerebral basis of phonological effects in printed word identification. Journal of Experimental Psychology: Human Perception and Performance, 23(2), 299–318. https://doi.org/10.1037/0096-1523.23.2.299.Google ScholarPubMed
Qin, S., Cho, S., Chen, T. et al. (2014). Hippocampal-neocortical functional reorganization underlies children’s cognitive development. Nature Neuroscience, 17, 1263–1269. https://doi.org/10.1038/nn.3788.CrossRefGoogle ScholarPubMed
Raichle, M. E. (2015). The brain’s default mode network. Annual Review of Neuroscience, 38, 433–447. https://doi.org/abs/10.1146/annurev-neuro-071013-014030.CrossRefGoogle ScholarPubMed
Rakic, P. (1988). Specification of cerebral cortical areas. Science, 241(4862), 170–176. www.jstor.org/stable/1701135.CrossRefGoogle ScholarPubMed
Ramani, G. B., Rowe, M. L., Eason, S. H., & Leech, K. A. (2015). Math talk during informal learning activities in Head Start families. Cognitive Development, 35, 15–33. https://doi.org/10.1016/j.cogdev.2014.11.002.CrossRefGoogle Scholar
Ramirez, F. O., & Boli, J. (1987). The political construction of mass schooling: European origins and worldwide institutionalization. Sociology of Education, 60(1), 2–17. https://doi.org/10.2307/2112615.CrossRefGoogle Scholar
Ramirez, G., Shaw, S. T., & Maloney, E. A. (2018). Math anxiety: Past research, promising interventions, and a new interpretation framework. Educational Psychologist, 53(3), 145–164. https://doi.org/10.1080/00461520.2018.1447384.CrossRefGoogle Scholar
Redick, T. S., Shipstead, Z., Meier, M. E. et al. (2016). Cognitive predictors of a common multitasking ability: Contributions from working memory, attention control, and fluid intelligence. Journal of Experimental Psychology: General, 145(11), 1473–1492. https://doi.org/10.1037/xge0000219.CrossRefGoogle ScholarPubMed
Reynaud, E., Lesourd, M., Navarro, J., & Osiurak, F. (2016). On the neurocognitive origins of human tool use: A critical review of neuroimaging data. Neuroscience & Biobehavioral Reviews, 64(1), 421–437. https://doi.org/10.1016/j.neubiorev.2016.03.009.CrossRefGoogle ScholarPubMed
Richerson, P., & Boyd, R. (2005). Not by genes alone: How culture transformed human evolution. University of Chicago Press.Google Scholar
Richlan, F., Kronbichler, M., & Wimmer, H. (2011). Meta-analyzing brain dysfunctions in dyslexic children and adults. Neuroimage, 56(3), 1735–1742. https://doi.org/10.1016/j.neuroimage.2011.02.040.CrossRefGoogle ScholarPubMed
Richmond-Rakerd, L. S., D’Souza, S., Andersen, S. H. et al. (2020). Clustering of health, crime and social-welfare inequality in 4 million citizens from two nations. Nature Human Behaviour, 4, 255–264. https://doi.org/10.1038/s41562-019-0810-4.CrossRefGoogle ScholarPubMed
Riek, B. M., Mania, E. W., & Gaertner, S. L. (2006). Intergroup threat and outgroup attitudes: A meta-analytic review. Personality and Social Psychology Review, 10(4), 336–353. https://doi.org/10.1207/s15327957pspr1004_4.CrossRefGoogle ScholarPubMed
Ritchie, S. J., & Bates, T. C. (2013). Enduring links from childhood mathematics and reading achievement to adult socioeconomic status. Psychological Science, 24(7), 1301–1308. https://doi.org/10.1177/0956797612466268.CrossRefGoogle ScholarPubMed
Rivera, S. M., Reiss, A. L., Eckert, M. A., & Menon, V. (2005). Developmental changes in mental arithmetic: Evidence for increased functional specialization in the left inferior parietal cortex. Cerebral Cortex, 15, 1779–1790. https://doi.org/10.1093/cercor/bhi05.CrossRefGoogle ScholarPubMed
Rivera-Batiz, F. L. (1992). Quantitative literacy and the likelihood of employment among young adults in the United States. Journal of Human Resources, 27(2), 313–328. www.jstor.org/stable/145737.CrossRefGoogle Scholar
Rouder, J. N., & Geary, D. C. (2014). Children’s cognitive representation of the mathematical number line. Developmental Science, 17, 525–536. https://doi.org/10.1111/desc.12166.CrossRefGoogle ScholarPubMed
Rousseau, J.-J. (1979). Emile: Or, on education (A. Bloom, Trans.). Basic Books. (Original work published 1762).Google Scholar
Roy, D. S., Zhang, Y., Halassa, M. M., & Feng, G. (2022). Thalamic subnetworks as units of function. Nature Neuroscience, 25(2), 140–153. https://doi.org/10.1038/s41593-021-00996-1.CrossRefGoogle ScholarPubMed
Rozin, P. (1976). The evolution of intelligence and access to the cognitive unconscious. In Sprague, J. M., & Epstein, A. N. (Eds.), Progress in psychobiology and physiological psychology (Vol. 6, pp. 245–280). Academic Press.Google Scholar
Rueter, A. R., Abram, S. V., MacDonald, A. W. III, Rustichini, A., & DeYoung, C. G. (2018). The goal priority network as a neural substrate of conscientiousness. Human Brain Mapping, 39(9), 3574–3585. https://doi.org/10.1002/hbm.24195.CrossRefGoogle ScholarPubMed
Rugg, M. D., & Vilberg, K. L. (2013). Brain networks underlying episodic memory retrieval. Current Opinion in Neurobiology, 23(2), 255–260. https://doi.org/10.1016/j.conb.2012.11.005.CrossRefGoogle ScholarPubMed
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.CrossRefGoogle Scholar
Sakamoto, A., Goyette, K. A., & Kim, C. (2009). Socioeconomic attainments of Asian Americans. Annual Review of Sociology, 35, 255–276. http://doi.org/10.1146/annurev-soc-070308-115958.CrossRefGoogle Scholar
Sampaio-Baptista, C., & Johansen-Berg, H. (2017). White matter plasticity in the adult brain. Neuron, 96(6), 1239–1251. https://doi.org/10.1016/j.neuron.2017.11.026.CrossRefGoogle ScholarPubMed
Santarnecchi, E., Emmendorfer, A., & Pascual-Leone, A. (2017). Dissecting the parieto-frontal correlates of fluid intelligence: A comprehensive ALE meta-analysis study. Intelligence, 63, 9–28. https://doi.org/10.1016/j.intell.2017.04.008.CrossRefGoogle Scholar
Sassenberg, T. A., Burton, P. C., Mwilambwe-Tshilobo, L. et al. (2023). Conscientiousness associated with efficiency of the salience/ventral attention network: Replication in three samples using individualized parcellation. NeuroImage, 272, 120081. https://doi.org/10.1016/j.neuroimage.2023.120081.CrossRefGoogle ScholarPubMed
Saygin, Z. M., Osher, D. E., Norton, E. S. et al. (2016). Connectivity precedes function in the development of the visual word form area. Nature Neuroscience, 19(9), 1250–1255. https://doi.org/10.1038/nn.4354.Google ScholarPubMed
Scarr, S. (1992). Developmental theories of the 1990s: Developmental and individual differences. Child Development, 63(1), 1–19. https://doi.org/10.1111/j.1467-8624.1992.tb03591.x.CrossRefGoogle ScholarPubMed
Schimmelpfennig, J., Topczewski, J., Zajkowski, W., & Jankowiak-Siuda, K. (2023). The role of the salience network in cognitive and affective deficits. Frontiers in Human Neuroscience, 17, 1133367. https://doi.org/10.3389/fnhum.2023.1133367.CrossRefGoogle ScholarPubMed
Schlaggar, B. L., & McCandliss, B. D. (2007). Development of neural systems for reading. Annual Review of Neuroscience, 30, 475–503. https://doi.org/10.1146/annurev.neuro.28.061604.135645.CrossRefGoogle ScholarPubMed
Schlegel, A. A., Rudelson, J. J., & Tse, P. U. (2012). White matter structure changes as adults learn a second language. Journal of Cognitive Neuroscience, 24(8), 1664–1670. https://doi.org/10.1162/jocn_a_00240.CrossRefGoogle 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), 162–173. https://doi.org/10.1037/0022-3514.86.1.162.CrossRefGoogle ScholarPubMed
Schneider, M., & Preckel, F. (2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143(6), 565–600. https://doi.org/10.1037/bul0000098.CrossRefGoogle ScholarPubMed
Schöpf, V., Kasprian, G., Brugger, P. C., & Prayer, D. (2012). Watching the fetal brain at “rest.” International Journal of Developmental Neuroscience, 30(1), 11–17. https://doi.org/10.1016/j.ijdevneu.2011.10.006.CrossRefGoogle ScholarPubMed
Schurz, M., Radua, J., Aichhorn, M., Richlan, F., & Perner, J. (2014). Fractionating theory of mind: A meta-analysis of functional brain imaging studies. Neuroscience & Biobehavioral Reviews, 42, 9–34. https://doi.org/10.1016/j.neubiorev.2014.01.009.CrossRefGoogle ScholarPubMed
Seligman, M. E. (1971). Phobias and preparedness. Behavior Therapy, 2(3), 307–320. https://doi.org/10.1016/S0005-7894(71)80064-3.CrossRefGoogle Scholar
Setoh, P., Wu, D., Baillargeon, R., & Gelman, R. (2013). Young infants have biological expectations about animals. Proceedings of the National Academy of Sciences of the United States of America, 110(40), 15937–15942. https://doi.org/10.1073/pnas.1314075110.CrossRefGoogle ScholarPubMed
Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Self-concept: Validation of construct interpretations. Review of Educational Research, 46, 407–441. https://doi.org/10.3102/00346543046003407.CrossRefGoogle Scholar
Siegler, R. S., & Braithwaite, D. W. (2017). Numerical development. Annual Review of Psychology, 68, 187–213. https://doi.org/100.1146/annurev-psych-010416-044101.CrossRefGoogle ScholarPubMed
Siegler, R. S., & Opfer, J. E. (2003). The development of numerical estimation: Evidence for multiple representations of numerical quantity. Psychological Science, 14, 237–250. https://doi.org/10.1111/1467-9280.02438.CrossRefGoogle ScholarPubMed
Siegler, R. S., Thompson, C. A., & Schneider, M. (2011). An integrated theory of whole number and fractions development. Cognitive Psychology, 62, 273–296. https://doi.org/10.1016/j.cogpsych.2011.03.001.CrossRefGoogle ScholarPubMed
Simonton, D. K. (2003). Scientific creativity as constrained stochastic behavior: The integration of product, person, and process perspectives. Psychological Bulletin, 129(4), 475–494. https://doi.org/10.1037/0033-2909.129.4.475.CrossRefGoogle ScholarPubMed
Skipper, J. I., Goldin-Meadow, S., Nusbaum, H. C., & Small, S. L. (2007). Speech-associated gestures, Broca’s area, and the human mirror system. Brain and Language, 101(3), 260–277. https://doi.org/10.1016/j.bandl.2007.02.008.CrossRefGoogle ScholarPubMed
Slater, A., Mattock, A., & Brown, E. (1990). Size constancy at birth: Newborn infants’ responses to retinal and real size. Journal of Experimental Child Psychology, 49, 314–322. https://doi.org/10.1016/0022-0965(90)90061-C.CrossRefGoogle ScholarPubMed
Sliwa, J., & Freiwald, W. A. (2017). A dedicated network for social interaction processing in the primate brain. Science, 356(6339), 745–749. https://doi.org/10.1126/science.aam6383.CrossRefGoogle Scholar
Slusser, E. B., Santiago, R. T., & Barth, H. C. (2013). Developmental change in numerical estimation. Journal of Experimental Psychology: General, 142, 193–208. https://doi.org/10.1037/a0028560.CrossRefGoogle ScholarPubMed
Smaers, J. B., & Vanier, D. R. (2019). Brain size expansion in primates and humans is explained by a selective modular expansion of the cortico-cerebellar system. Cortex, 118, 292–305. https://doi.org/10.1016/j.cortex.2019.04.023.CrossRefGoogle ScholarPubMed
Smallwood, J., Bernhardt, B. C., Leech, R. et al. (2021). The default mode network in cognition: A topographical perspective. Nature Reviews Neuroscience, 22(8), 503–513. https://doi.org/10.1038/s41583-021-00474-4.CrossRefGoogle ScholarPubMed
Sniekers, S., Stringer, S., Watanabe, K. et al. (2017). Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nature Genetics, 49(7), 1107–1112. https://doi.org/10.1038/ng.3869.CrossRefGoogle Scholar
Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. https://doi.org/10.2307/1412107.CrossRefGoogle Scholar
Spelke, E. S. (2017). Core knowledge, language, and number. Language Learning and Development, 13(2), 147–170. https://doi.org/10.1080/15475441.2016.1263572.Google Scholar
Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. (1992). Origins of knowledge. Psychological Review, 99(4), 605–632. https://doi.org/10.1037/0033-295X.99.4.605.CrossRefGoogle ScholarPubMed
Spelke, E., Lee, S. A., & Izard, V. (2010). Beyond core knowledge: Natural geometry. Cognitive Science, 34, 863–884. https://doi.org/10.1111/j.1551-6709.2010.01110.x.CrossRefGoogle ScholarPubMed
Spielmann, J., Yoon, H. J. R., Ayoub, M. et al. (2022). An in-depth review of conscientiousness and educational issues. Educational Psychology Review, 34(4), 2745–2781. https://doi.org/10.1007/s10648-022-09693-2.CrossRefGoogle Scholar
Srinivasan, S., Bettella, F., Frei, O. et al. (2018). Enrichment of genetic markers of recent human evolution in educational and cognitive traits. Scientific Reports, 8(1), 12585. https://doi.org/10.1038/s41598-018-30387-9.Google ScholarPubMed
Stanek, K. C., & Ones, D. S. (2023). Meta-analytic relations between personality and cognitive ability. Proceedings of the National Academy of Sciences of the United States of America, 120(23), e2212794120. https://doi.org/10.1073/pnas.2212794120.CrossRefGoogle ScholarPubMed
Stanovich, K. E. (1988). Explaining the differences between the dyslexic and the garden-variety poor reader: The phonological-core variable-difference model. Journal of Learning Disabilities, 21(10), 590–604. https://doi.org/10.1177/002221948802101003.CrossRefGoogle ScholarPubMed
Stanovich, K. E., West, R. F., & Toplak, M. E. (2016). The rationality quotient: Toward a test of rational thinking. The MIT Press.CrossRefGoogle Scholar
Stebbins, L. B. (1977). Education as experimentation: A planned variation model (Vol. 4). University Press of America.Google Scholar
Sternberg, R. J. (2021). Transformational creativity: The link between creativity, wisdom, and the solution of global problems. Philosophies, 6(3), 75. https://doi.org/10.3390/philosophies6030075.CrossRefGoogle Scholar
Stevens, W. D., Kravitz, D. J., Peng, C. S., Tessler, M. H., & Martin, A. (2017). Privileged functional connectivity between the visual word form area and the language system. Journal of Neuroscience, 37(21), 5288–5297. https://doi.org/10.1523/JNEUROSCI.0138-17.2017.CrossRefGoogle ScholarPubMed
Stevenson, H., & Stigler, J. W. (1992). Learning gap: Why our schools are failing and what we can learn from Japanese and Chinese education. Summit Books.Google Scholar
Suddendorf, T., & Corballis, M. C. (2007). The evolution of foresight: What is mental time travel, and is it unique to humans? Behavioral and Brain Sciences, 30(3), 299–313. https://doi.org/10.1017/S0140525X07001975.CrossRefGoogle ScholarPubMed
Sullivan, J., & Barner, D. (2014). Inference and association in children’s early numerical estimation. Child Development, 85(4), 1740–1755. https://doi.org/10.1111/cdev.12211.CrossRefGoogle ScholarPubMed
Summerfield, C., Luyckx, F., & Sheahan, H. (2020). Structure learning and the posterior parietal cortex. Progress in Neurobiology, 184, 101717. https://doi.org/10.1016/j.pneurobio.2019.101717.CrossRefGoogle ScholarPubMed
Supekar, K., Iuculano, T., Chen, L., & Menon, V. (2015). Remediation of childhood math anxiety and associated neural circuits through cognitive tutoring. Journal of Neuroscience, 35(36), 12574–12583. https://doi.org/10.1523/JNEUROSCI.0786-15.2015.CrossRefGoogle ScholarPubMed
Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59–89. https://doi.org/10.1207/s1532690xci0201_3.CrossRefGoogle Scholar
Sweller, J., van Merriënboer, J. J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292. https://doi.org/10.1007/s10648-019-09465-5.CrossRefGoogle Scholar
Szkudlarek, E., & Brannon, E. M. (2017). Does the approximate number system serve as a foundation for symbolic mathematics? Language Learning and Development, 13, 171–190. https://doi.org/10.1080/15475441.2016.1263573.CrossRefGoogle ScholarPubMed
Szűcs, D., & Myers, T. (2017). A critical analysis of design, facts, bias and inference in the approximate number system training literature: A systematic review. Trends in Neuroscience and Education, 6, 187–203. https://doi.org/10.1016/j.tine.2016.11.002.CrossRefGoogle Scholar
Thompson, P. M., Cannon, T. D., Narr, K. L. et al. (2001). Genetic influences on brain structure. Nature Neuroscience, 4(12), 1253–1258. https://doi.org/10.1038/nn758.CrossRefGoogle ScholarPubMed
Thurstone, L. L., & Thurstone, T. G. (1941). Factorial studies of intelligence. Psychometric Monographs (No. 2). University of Chicago Press.Google Scholar
Tomasello, M. (1999). The cultural origins of human cognition. Harvard University Press. https://doi.org/10.2307/j.ctvjsf4jc.Google Scholar
Toub, T. S., Rajan, V., Golinkoff, R. M., & Hirsh-Pasek, K. (2016). Guided play: A solution to the play versus learning dichotomy. In Geary, D. C., & Berch, D. B. (Eds.), Evolutionary perspectives on child development and education (pp. 117–141). Springer.CrossRefGoogle Scholar
Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53, 1–25. https://doi.org/10.1146/annurev.psych.53.100901.135114.CrossRefGoogle Scholar
Turchin, P. (2009). A theory for formation of large empires. Journal of Global History, 4(2), 191–217. https://doi.org/10.1017/S174002280900312X.CrossRefGoogle Scholar
Turchin, P., Currie, T. E., Turner, E. A., & Gavrilets, S. (2013). War, space, and the evolution of Old World complex societies. Proceedings of the National Academy of Sciences of the United States of America, 110(41), 16384–16389. https://doi.org/10.1073/pnas.1308825110.CrossRefGoogle ScholarPubMed
Turk, E., Van Den Heuvel, M. I., Benders, M. J. et al. (2019). Functional connectome of the fetal brain. Journal of Neuroscience, 39(49), 9716–9724. https://doi.org/10.1523/JNEUROSCI.2891-18.2019.CrossRefGoogle ScholarPubMed
Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003). Development of neural mechanisms for reading. Nature Neuroscience, 6(7), 767–773. https://doi.org/10.1038/nn1065.CrossRefGoogle ScholarPubMed
Uddin, L. Q. (2015). Salience processing and insular cortical function and dysfunction. Nature Reviews Neuroscience, 16(1), 55–61. https://doi.org/10.1038/nrn3857.CrossRefGoogle Scholar
Udochi, A. L., Blain, S. D., Sassenberg, T. A. et al. (2022). Activation of the default network during a theory of mind task predicts individual differences in agreeableness and social cognitive ability. Cognitive, Affective, & Behavioral Neuroscience, 22, 383–402. https://doi.org/10.3758/s13415-021-00955-0.CrossRefGoogle ScholarPubMed
Ünal, Z. E., Ala, A. M., Kartal, G., Özel, S., & Geary, D. C. (2023). Visual and symbolic representations as a component of algebraic reasoning. Journal of Numerical Cognition, 9(2), 327–345. https://doi.org/10.5964/jnc.11151.CrossRefGoogle Scholar
Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval. Cognitive Psychology, 71, 1–26. https://doi.org/10.1016/j.cogpsych.2014.01.003.CrossRefGoogle ScholarPubMed
Valentine, J. C., DuBois, D. L., & Cooper, H. (2004). The relation between self-beliefs and academic achievement: A meta-analytic review. Educational Psychologist, 39, 111–133. https://doi.org/10.1207/s15326985ep3902_3.CrossRefGoogle Scholar
van Garderen, D., Scheuermann, A., & Jackson, C. (2013). Examining how students with diverse abilities use diagrams to solve mathematics word problems. Learning Disability Quarterly, 36(3), 145–160. https://doi.org/10.1177/0731948712438558.CrossRefGoogle Scholar
Vrba, E. (1996). The fossil record of African antelopes (Mammalia, Bovidae) in relation to human evolution and paleoclimate. In Vrba, E. S., Denton, G. H., Partridge, T. C., & Burckle, L. H. (Eds.), Paleoclimate and evolution, with emphasis on human origins (pp. 385–424). Yale University Press.Google Scholar
Wagner, J., & Rusconi, E. (2023). Causal involvement of the left angular gyrus in higher functions as revealed by transcranial magnetic stimulation: A systematic review. Brain Structure and Function, 228(1), 169–196. https://doi.org/10.1007/s00429-022-02576-w.CrossRefGoogle ScholarPubMed
Walberg, H. J. (1984). Improving the productivity of America’s schools. Educational Leadership, 41(8), 19–27.Google Scholar
Wallace, A. R. (1869). Geological climate and origin of species. London Quarterly Review, 126(252), 187–205.Google Scholar
Wang, Z., Hart, S. A., Kovas, Y. et al. (2014). Who is afraid of math? Two sources of genetic variance for mathematical anxiety. Journal of Child Psychology and Psychiatry, 55(9), 1056–1064. https://doi.org/10.1111/jcpp.12224CrossRefGoogle ScholarPubMed
Wang, S., Zhao, Y., Li, J. et al. (2019). Brain structure links trait conscientiousness to academic performance. Scientific Reports, 9(1), 12168. https://doi.org/10.1038/s41598-019-48704-1.CrossRefGoogle ScholarPubMed
Wang, Z., Rimfeld, K., Shakeshaft, N., Schofield, K., & Malanchini, M. (2020). The longitudinal role of mathematics anxiety in mathematics development: Issues of gender differences and domain-specificity. Journal of Adolescence, 80, 220–232. https://doi.org/10.1016/j.adolescence.2020.03.003.CrossRefGoogle ScholarPubMed
Wei, Y., de Lange, S. C., Scholtens, L. H. et al. (2019). Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nature Communications, 10(1), 4839. https://doi.org/10.1038/s41467-019-12764-8.CrossRefGoogle ScholarPubMed
Wellman, H. M., & Gelman, S. A. (1992). Cognitive development: Foundational theories of core domains. Annual Review of Psychology, 43, 337–375. https://doi.org/10.1146/annurev.ps.43.020192.002005.CrossRefGoogle ScholarPubMed
West, B. H., Griesbach, E. N., Taylor, J. D., & Taylor, L. T. (1982). The Prentice-Hall encyclopedia of mathematics. Prentice-Hall.Google Scholar
Whiten, A., & Van Schaik, C. P. (2007). The evolution of animal “cultures” and social intelligence. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1480), 603–620. https://doi.org/10.1098/rstb.2006.1998.CrossRefGoogle ScholarPubMed
Williams, G. C. (1957). Pleiotropy, natural selection and the evolution of senescence. Evolution, 11(4), 398–411. https://doi.org/10.1111/j.1558-5646.1957.tb02911.x.CrossRefGoogle Scholar
Winegard, B., Winegard, B., & Geary, D. C. (2018a). The status competition model of cultural production. Evolutionary Behavioral Science, 4(4), 351–371. https://doi.org/10.1007/s40806-018-0147-7.Google Scholar
Winegard, B., Winegard, B., & Geary, D. C. (2018b). The evolution of expertise. In Ericsson, K. A. (Ed.), Cambridge handbook of expertise and expert performance (2nd ed., pp. 40–48). Cambridge University Press.CrossRefGoogle Scholar
Witt, S. T., van Ettinger-Veenstra, H., Salo, T., Riedel, M. C., & Laird, A. R. (2021). What executive function network is that? An image-based meta-analysis of network labels. Brain Topography, 34(5), 598–607. https://doi.org/10.1007/s10548-021-00847-z.CrossRefGoogle ScholarPubMed
Wolfram, T. (2023). (Not just) Intelligence stratifies the occupational hierarchy: Ranking 360 professions by IQ and non-cognitive traits. Intelligence, 98, 101755. https://doi.org/10.1016/j.intell.2023.101755.CrossRefGoogle Scholar
Wu, H., Guo, Y., Yang, Y., Zhao, L., & Guo, C. (2021). A meta-analysis of the longitudinal relationship between academic self-concept and academic achievement. Educational Psychology Review, 33, 1749–1778. https://doi.org/10.1007/s10648-021-09600-1.CrossRefGoogle Scholar
Wynn, K. (1990). Children’s understanding of counting. Cognition, 36, 155–193. http://doi.org/10.1016/0010-0277(90)90003-3.CrossRefGoogle ScholarPubMed
Yarkoni, T., Speer, N. K., Balota, D. A., McAvoy, M. P., & Zacks, J. M. (2008). Pictures of a thousand words: Investigating the neural mechanisms of reading with extremely rapid event-related fMRI. NeuroImage, 42(2), 973–987. https://doi.org/10.1016/j.neuroimage.2008.04.258.CrossRefGoogle ScholarPubMed
Yeatman, J. D., Dougherty, R. F., Ben-Shachar, M., & Wandell, B. A. (2012). Development of white matter and reading skills. Proceedings of the National Academy of Sciences of the United States of America, 109(44), E3045–E3053. https://doi.org/10.1073/pnas.1206792109.Google ScholarPubMed
Yeo, B. T., Krienen, F. M., Sepulcre, J. et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(1), 1125–1165. https://doi.org/10.1152/jn.00338.2011.Google ScholarPubMed
Yeo, D. J., Pollack, C., Merkley, R., Ansari, D., & Price, G. R. (2020). The “inferior temporal numeral area” distinguishes numerals from other character categories during passive viewing: A representational similarity analysis. NeuroImage, 214, 116716. https://doi.org/10.1016/j.neuroimage.2020.116716.CrossRefGoogle Scholar
Yeshurun, Y., Nguyen, M., & Hasson, U. (2021). The default mode network: Where the idiosyncratic self meets the shared social world. Nature Reviews Neuroscience, 22(3), 181–192. https://doi.org/10.1038/s41583-020-00420-w.CrossRefGoogle ScholarPubMed
Yi, H., Xiao, M., Chen, X. et al. (2023). Resting-state functional network connectivity underlying conscientiousness in school-aged children. Child Neuropsychology, 1–17. https://doi.org/10.1080/09297049.2023.2221757.Google Scholar
Young, C. B., Wu, S. S., & Menon, V. (2012). The neurodevelopmental basis of math anxiety. Psychological Science, 23(5), 492–501. https://doi.org/10.1177/0956797611429134.CrossRefGoogle ScholarPubMed
Zatorre, R. J., Fields, R. D., & Johansen-Berg, H. (2012). Plasticity in gray and white: Neuroimaging changes in brain structure during learning. Nature Neuroscience, 15(4), 528–536. https://doi.org/10.1038/nn.3045.CrossRefGoogle ScholarPubMed
Zeithamova, D., Mack, M. L., Braunlich, K. et al. (2019). Brain mechanisms of concept learning. Journal of Neuroscience, 39(42), 8259–8266. https://doi.org/10.1523/JNEUROSCI.1166-19.2019.CrossRefGoogle ScholarPubMed
Zhang, L., Kirschner, P. A., Cobern, W. W., & Sweller, J. (2022). There is an evidence crisis in science educational policy. Educational Psychology Review, 34(2), 1157–1176. https://doi.org/10.1007/s10648-021-09646-1.CrossRefGoogle Scholar
Zippert, E. L., & Rittle-Johnson, B. (2020). The home math environment: More than numeracy. Early Childhood Research Quarterly, 50(3), 4–15. https://doi.org/10.1016/j.ecresq.2018.07.009.CrossRefGoogle Scholar
Zorzi, M., Priftis, K., & Umiltà, C. (2002). Neglect disrupts the mental number line. Nature, 417, 138–139. https://doi.org/10.1038/417138a.CrossRefGoogle ScholarPubMed
Accessibility standard: Unknown
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
Accessibility compliance for the HTML of this element is currently unknown
and may be updated in the future.
- 13
- Cited by