Neurobiology of cognitive competencies in primary school age: the latest foreign research

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Abstract

This paper presents the current researches revealing the peculiarities of neurocognitive status in primary school age and illustrating the achievements of the scientific school of foreign universities in such areas as: neuroscience compensatory-regulatory mechanisms of overcoming congenital learning difficulties; educational strategies that optimize the neurobiological status of the student; neurobiological tools for the development of the cognitive sphere of students; environmental and psychosomatic factors that affect the neurocognitive status of young students. The authors allocated memory resources, executive activity capabilities, inhibition reactions and self-control as the main elements in cognitive competencies’ formation. The materials provide evidence in favor of the importance of the quality of educational environments and attention to students with learning difficulties in their first school years.

General Information

Keywords: junior school age; cognitive flexibility, connectiveness; zones of cerebral cortex; executive activity; working memory, inhibition

Journal rubric: Cognitive Pedagogy

Article type: review article

DOI: https://doi.org/10.17759/jmfp.2020090201

For citation: Ermolova Т.V., Litvinov A.V., Balygina E.A., Savitskaya N.V., Litvinova A.V. Neurobiology of cognitive competencies in primary school age: the latest foreign research [Elektronnyi resurs]. Sovremennaia zarubezhnaia psikhologiia = Journal of Modern Foreign Psychology, 2020. Vol. 9, no. 2, pp. 8–20. DOI: 10.17759/jmfp.2020090201. (In Russ., аbstr. in Engl.)

References

  1. Drobakha V.E., Kulesh A.A., Shestakov V.V. Fraktsionnaya anizotropiya belogo i serogo veshchestva golovnogo mozga v ostrom periode ishemicheskogo insul'ta kak marker nevrologicheskogo, kognitivnogo i funktsional'nogo statusa [Fractional anisotropy of the white and gray matter of the brain in the acute period of ischemic stroke as a marker of neurological, cognitive and functional status] [Elektronnyi resurs]. Meditsinskaya vizualizatsiya = [Medical imaging], 2015. Vol. 6, pp. 8–15. URL: https://medvis.vidar.ru/jour/article/view/158 (Accessed 10.06.2020). (In Russ.).
  2. Ermolova T.V., Ponomareva V.V., Florova N.B. Diskal'kuliya detskogo vozrasta kak sistemnaya problema obucheniya [Childhood dyscalculia as a systemic learning problem]. Sovremennaya zarubezhnaya psikhologiya = Journal of Modern Foreign Psychology, 2016. Vol. 5, no. 3, pp. 7–27. DOI:10.17759/jmfp.2016050301 (In Russ.).
  3. Mikadze Yu.V. et al. Modeli i metody issledovaniya pererabotki informatsii v protsessakh nazyvaniya predmeta i sootneseniya nazvaniya s predmetom [Models and methods of researching information processing in the processes of naming an object and correlating a name with an object]. Eksperimental'naya psikhologiya = Experimental Psychology (Russia), 2019. Vol. 12, no. 1, pp. 153–166. DOI:10.17759/exppsy.2019120112 (In Russ.).
  4. Anderson P.J., Cheong J.L., Thompson D.K. The predictive validity of neonatal MRI for neurodevelopmental outcome in very preterm children Semin. Seminars in Perinatology, 2015. Vol. 39, no. 2, pp. 147–158. DOI:10.1053/j.semperi.2015.01.008
  5. Arfe B., Vardanega T., Ronconi L. The effects of coding on children’s planning and inhibition skills. Computers & Education, 2020. Vol. 148, 16 p. DOI:10.1016/j.compedu.2020.103807
  6. Jansen L. et al. Classroom-evaluated school performance at nine years of age after very preterm birth. Early Human Development, 2020. Vol. 140. 6 p. DOI:10.1016/j.earlhumdev.2019.104834
  7. Debanne D., Russier V. The contribution of ion channels in input-output plasticity. Neurobiology of Learning and Memory, 2019. Vol. 166. 8 p. DOI:10.1016/j.nlm.2019.107095
  8. Siman R. et al. Evidence that a Panel of Neurodegeneration Biomarkers Predicts Vasospasm, Infarction, and Outcome in Aneurysmal Subarachnoid Hemorrhage. PLoS One, 2011. Vol. 6, no. 12, 9 p. DOI:10.1371/journal.pone.0028938
  9. Barnes-Davis M.E. et al. Extremely preterm children exhibit increased interhemispheric connectivity for language: findings from fMRI-constrained MEG analysis. Developmental Science, 2018. Vol. 21, no. 6, 9 p. DOI:10.1111/desc.12669
  10. Farmer G.E., Thompson L.T. Learning-dependent plasticity of hippocampal CA1 pyramidal neuron postburst afterhyperpolarizations and increased excitability after inhibitory avoidance learning depend upon basolateral amygdala inputs. Hippocampus, 2012. Vol. 22, no. 8, pp. 1703–1719. DOI:10.1002/hipo.22005
  11. Bloodgood D.W. et al. Fear extinction requires infralimbic cortex projections to the basolateral amygdale. Translation Psychiatry, 2018. Vol. 8, article ID 60, 11 p. DOI:10.1038/s41398-018-0106-x
  12. Mazzoli E. et al. Feasibility of breaking up sitting time in mainstream and special schools with a cognitively challenging motor task. Journal of Sport and Health Science, 2019. Vol. 8, no. 2, pp. 137–148. DOI:10.1016/j.jshs.2019.01.002
  13. França T.F.A. Isolating the key factors defining the magnitude of hippocampal neurogenesis’ effects on anxiety, memory and pattern separation. Neurobiology of Learning and Memory, 2019. Vol. 166, 5 p. DOI:10.1016/j.nlm.2019.107102
  14. Grineski S.E., Collins T.W., Adkins D.E. Hazardous air pollutants are associated with worse performance in reading, math, and science among US primary schoolchildren. Environmental Research, 2020. Vol. 181, 10 p. DOI:10.1016/j.envres.2019.108925
  15. Guardabassi V., Tomasetto C. Weight status or weight stigma? Obesity stereotypes — Not excess weight — Reduce working memory in school-aged children. Journal of Experimental Child Psychology, 2020. Vol. 189, 9 p. DOI:10.1016/j.jecp.2019.104706
  16. Kyriakides L., Anthimou M., Panayiotou A. Searching for the impact of teacher behavior on promoting students’ cognitive and metacognitive skills. Studies in Educational Evaluation, 2020. Vol. 189, 14 p. DOI:10.1016/j.stueduc.2019.100810
  17. Aarnoudse-Moens C.S. et al. Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics, 2009. Vol. 124, no. 2, pp. 717–728. DOI:10.1542/peds.2008-2816
  18. Oh M.M., Disterhoft J.F. Learning and aging affect neuronal excitability and learning. Neurobiology of Learning and Memory, 2020. Vol. 167, 7 p. DOI:10.1016/j.nlm.2019.107133
  19. Polspoel B., Vandermosten M., De Smedt B. The association of grey matter volume and cortical complexity with individual differences in children’s arithmetic fluency. Neuropsychologia, 2020. Vol. 137, 10 p. DOI:10.1016/j.neuropsychologia.2019.107293
  20. Prensky M. Digital Natives, Digital Immigrants [Elektronnyi resurs]. On the Horizon, 2001. Vol. 9, no. 5, 6 p. URL: http://www.lablearning.eu/documents/doc_inspiration/prensky/digital_natives_digital_immigrants.pdf (Accessed 10.06.2020).
  21. Pulfrey J., Gasser U. Born Digital: How children gow up in a digital age. New York: Basic Books, 2016. 352 p.
  22. Xue H. et al. Resting-state EEG reveals global network deficiency in dyslexic children. Neuropsychologia, 2020. Vol. 138, 8 p. DOI:10.1016/j.neuropsychologia.2020.107343
  23. Barnes-Davis M.E. et al. Rewiring the extremely preterm brain: Altered structural connectivity relates to language function. NeuroImage: Clinical, 2020. Vol. 25, 12 p. DOI:10.1016/j.nicl.2020.102194
  24. Siddiqui N., Gorard S., See B.H. Can learning beyond the classroom impact on social responsibility and academic attainment? An evaluation of the Children’s University youth social action programme. Studies in Educational Evaluation, 2019. Vol. 61, pp. 74–82. DOI:10.1016/j.stueduc.2019.03.004
  25. Hachmann W.M. et al. The relationship of domain-general serial order memory and reading ability in school children with and without dyslexia. Journal of Experimental Child Psychology, 2020. Vol. 193, 39 p. DOI:10.1016/j.jecp.2019.104789
  26. Wang Q., Kushnir T. Cultural Pathways in Cognitive Development: Introduction to the Special Issue. Cognitive Development, 2019. Vol. 52, 4 p. DOI:10.1016/j.cogdev.2019.100816
  27. Jiang R. et al. Why students are biased by heuristics: Examining the role of inhibitory control, conflict detection, and working memory in the case of overusing proportionality. Cognitive Development, 2020. Vol. 53, 14 p. DOI:10.1016/j.cogdev.2020.100850

Information About the Authors

Тatiana V. Ermolova, PhD in Psychology, Head of the Chair of Foreign and Russian Philology, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0003-4260-9087, e-mail: yermolova@mail.ru

Alexander V. Litvinov, PhD in Education, professor of the chair of foreign and Russian philology, Moscow State University of Psychology and Education, associate professor at Foreign Languages Department at the Facultyof Economics (RUDN University), Moscow, Russia, ORCID: https://orcid.org/0000-0002-3306-0021, e-mail: alisal01@yandex.ru

Elena A. Balygina, PhD in Philology, Associate Professor of the Department of Foreign and Russian Philology, Moscow State University of Psychology & Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-5558-1389, e-mail: baliginaea@mgppu.ru

Natalia V. Savitskaya, PhD in Education, associate professor of the department of foreign and russian philology, Moscow State University of psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-1769-5553, e-mail: n.sawa@yandex.ru

Anna V. Litvinova, PhD in Psychology, Associate Professor, Department of Scientific Basis of Extreme Psychology, Moscow State University of Psychology & Education, Moscow, Russia, ORCID: https://orcid.org/0000-0001-6783-3144, e-mail: annaviktorovna@mail.ru

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