Нейрофизиологические механизмы эмоционального интеллекта
Ключевые слова:
эмоциональный интеллект, эмоции, регуляция эмоций, нейронные сети, функциональная связанность покоя
Аннотация
Обоснование: Эмоциональный интеллект (ЭИ) наряду с общим интеллектом взаимосвязан с академической успешностью, социальным и межличностным общением, что делает проблему изучения ЭИ актуальной в контексте междисциплинарных исследований с использованием методов нейровизуализации и проливает свет на психофизиологическую проблему. Цель: обзор и анализ исследований, посвященных проблеме конструкта эмоционального интеллекта и нейронных путей, обеспечивающих функционирование этого вида интеллекта. Теоретические основы: нарративный обзор опубликованных результатов оригинальных исследований и систематических научных обзорных статей с использованием поисковой системы Google Scholar. Результаты. Исследования показывают, что эмоциональный интеллект базируются на общих системах мозга, которые участвуют в реализации психических функций. Эмоциональный интеллект связан с активностью крупномасштабных нейронных сетей мозга, в которые вовлечены не только корковые регионы мозга, но и субкортикальные области, связанные с обработкой эмоционально значимых стимулов, формированием аффективного ответа и регуляцией эмоциональных процессов. Исследования полного конструкта эмоционального интеллекта (измеренного с использованием теста или методики самоотчета) также показывают вовлеченность нейронных сетей покоя в реализацию этого вида интеллекта. Траектория развития эмоционального интеллекта отличается от развития общего интеллекта, что, вероятно, связано как с накоплением индивидуального эмоционального опыта в течение жизни и с социокультурными особенностями, так и с изменениями функционирования структур мозга и нейронных сетей в течение жизни. Заключение. Проведенный анализ позволяет более полно представить взаимосвязь нейронных сетей покоя и эмоционального интеллекта. Однако очевидно, что исследований полного конструкта эмоционального интеллекта, измеренного с использованием тестов и самоотчетов, и его взаимосвязей с крупномасштабными сетями покоя, а также глобальными характеристиками функциональной связанности мозга, недостаточно, что, таким образом, делает это направление исследований перспективным для науки.
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Литература
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2. Tikhomirova T., Malykh A., Malykh S. Predicting Academic Achievement with Cognitive Abilities: Cross-Sectional Study across School Education. Behavioral Sciences. 2020;10(10):158. DOI: https://doi.org/10.3390/bs10100158
3. Adetula G.A. Emotional, Social, and Cognitive Intelligence as Predictors of Job Performance Among Law Enforcement Agency Personnel. Journal of Applied Security Research. 2016 Apr 2;11(2):149–65. DOI: https://doi.org/10.1080/19361610.2016.1137175
4. MacCann C., Jiang Y., Brown L.E.R. et al. Emotional intelligence predicts academic performance: A meta-analysis. Psychological Bulletin. 2020;146(2):150–186. DOI: https://doi.org/10.1037/bul0000219
5. Salovey P., Mayer J.D. Emotional Intelligence. Imagination, Cognition and Personality. 1990;9(3):185–211. DOI: https://doi.org/10.2190/DUGG-P24E-52WK-6CDG
6. Lindquist K.A., Wager T.D., Kober H. et al. The brain basis of emotion: A meta-analytic review. Behavioral and Brain Sciences. 2012;35(3):121–143. DOI: https://doi.org/10.1017/S0140525X11000446
7. Beckmann C.F., DeLuca M., Devlin J.T., Smith S.M. Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society B. 2005;360(1457):1001–1013. DOI: https://doi.org/10.1098/rstb.2005.1634
8. van de Ven V.G., Formisano E., Prvulovic D. et al. Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest. Human Brain Mapping. 2004;22(3):165–178. DOI: https://doi.org/10.1002/hbm.20022
9. Rubinov M., Sporns O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage. 2010;52(3):1059–1069. DOI: https://doi.org/10.1016/j.neuroimage.2009.10.003
10. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. 2009;10(3):186–198. DOI: https://doi.org/10.1038/nrn2575
11. Kostal L., Lansky P., McDonnell M.D. Metabolic cost of neuronal information in an empirical stimulus-response model. Biological Cybernetics. 2013;107(3):355–365. DOI: https://doi.org/10.1007/s00422-013-0554-6
12. Haier R.J., Jung R.E., Yeo R.A. et al. Structural brain variation and general intelligence. NeuroImage. 2004;23(1):425–433. DOI: https://doi.org/10.1016/j.neuroimage.2004.04.025
13. Ekman P., Cordaro D. What is Meant by Calling Emotions Basic. Emotion Review. 2011;3(4):364–370. DOI: https://doi.org/10.1177/1754073911410740
14. Barrett L.F., Adolphs R., Marsella S. et al. Emotional expressions reconsidered: challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest. 2019;20(1):1–68. DOI: https://doi.org/10.1177/1529100619889954
15. Kragel P.A., LaBar K.S. Decoding the Nature of Emotion in the Brain. Trends in Cognitive Sciences. 2016;20(6):444–455. DOI: https://doi.org/10.1016/j.tics.2016.03.011
16. Friston K. The free-energy principle: a unified brain theory? Nature Reviews Neuroscience. 2010;11(2):127–138. DOI: https://doi.org/10.1038/nrn2787
17. Nave K., Deane G., Miller M., Clark A. Wilding the predictive brain. Wiley Interdisciplinary Reviews: Cognitive Science 2020;11(6). DOI: https://doi.org/10.1002/wcs.1542
18. Smith R., Killgore W.D.S., Alkozei A., Lane R.D. A neuro-cognitive process model of emotional intelligence. Biological Psychology. 2018;139:131–51. DOI: https://doi.org/10.1016/j.biopsycho.2018.10.012
19. Hoemann K., Gendron M., Barrett L.F. Mixed emotions in the predictive brain. Current Opinion in Behavioral Sciences. 2017;15:51–57. DOI: https://doi.org/10.1016/j.cobeha.2017.05.013
20. Lindquist K.A., Barrett L.F. A functional architecture of the human brain: emerging insights from the science of emotion. Trends in Cognitive Sciences. 2012;16(11):533–540. DOI: https://doi.org/10.1016/j.tics.2012.09.005
21. Kaefer K., Stella F., McNaughton B.L., Battaglia F.P. Replay, the default mode network and the cascaded memory systems model. Nature Reviews Neuroscience. 2022;23(10):628–640. DOI: https://doi.org/10.1038/s41583-022-00620-6
22. Koelsch S., Andrews‐Hanna J.R., Skouras S. Tormenting thoughts: The posterior cingulate sulcus of the default mode network regulates valence of thoughts and activity in the brains pain network during music listening. Human Brain Mapping. 2022;43(2):773–786. DOI: https://doi.org/10.1002/hbm.25686
23. Killgore W.D.S., Smith R., Olson E.A. et al. Emotional intelligence is associated with connectivity within and between resting state networks. Social Cognitive and Affective Neuroscience. 2017;12(10):1624–1636. DOI: https://doi.org/10.1093/scan/nsx088
24. Aviezer H., Ensenberg N., Hassin R.R. The inherently contextualized nature of facial emotion perception. Current Opinion in Psychology. 2017;17:47–54. DOI: https://doi.org/10.1016/ j.copsyc.2017.06.006
25. Barrett L.F., Mesquita B., Gendron M. Context in Emotion Perception. Current Directions in Psychological Science. 2011;20(5):286–290. DOI: https://doi.org/10.1177/0963721411422522
26. Gündem D., Potočnik J., De Winter F.L. et al. The neurobiological basis of affect is consistent with psychological construction theory and shares a common neural basis across emotional categories. Communications Biology. 2022;5(1):1354. DOI: https://doi.org/10.1038/s42003-022-04324-6
27. Mesquita B., Boiger M. Emotions in Context: A Sociodynamic Model of Emotions. Emotion Review. 2014;6(4):298–302. DOI: https://doi.org/10.1177/1754073914534480
28. Greenaway K.H., Kalokerinos E.K., Williams L.A. Context is Everything (in Emotion Research). Social and Personality Psychology Compass. 2018;12(6):e12393. DOI: https://doi.org/10.1111/spc3.12393
29. Yan X., Andrews T.J., Young A.W. Cultural similarities and differences in perceiving and recognizing facial expressions of basic emotions. Journal of Experimental Psychology: Human Perception and Performance. 2016;42(3):423–440. DOI: https://doi.org/10.1037/xhp0000114
30. Seth A.K., Friston K.J. Active interoceptive inference and the emotional brain. Philosophical Transactions of the Royal Society B. 2016;371(1708):20160007. DOI: https://doi.org/10.1098/rstb.2016.0007
31. Hoemann K., Feldman B.L. Concepts dissolve artificial boundaries in the study of emotion and cognition, uniting body, brain, and mind. Cognition and Emotion. 2019;33(1):67–76. DOI: https://doi.org/10.1080/02699931.2018.1535428
32. Smith R., Alkozei A., Killgore W.D.S. Contributions of self-report and performance-based individual differences measures of social cognitive ability to large-scale neural network functioning. Brain Imaging and Behavior. 2017;11(3):685–697. DOI: https://doi.org/10.1007/s11682-016-9545-2
33. Barrett L.F., Simmons W.K. Interoceptive predictions in the brain. Nature Reviews Neuroscience. 2015;16(7):419–429. DOI: https://doi.org/10.1038/nrn3950
34. Ince S., Steward T., Harrison B.J. et al. Subcortical contributions to salience network functioning during negative emotional processing. NeuroImage. 2023;270:119964. DOI: https://doi.org/10.1016/j.neuroimage.2023.119964
35. Barrett L.F., Satpute A.B. Large-scale brain networks in affective and social neuroscience: towards an integrative functional architecture of the brain. Current Opinion in Neurobiology. 2013;23(3):361–372. DOI: https://doi.org/10.1016/j.conb.2012.12.012
36. Belyk M., Brown S. Perception of affective and linguistic prosody: an ALE meta-analysis of neuroimaging studies. Social Cognitive and Affective Neuroscience. 20141;9(9):1395–403. DOI: https://doi.org/10.1093/scan/nst124
37. Satpute A.B., Lindquist K.A. The Default Mode Networks Role in Discrete Emotion. Trends in Cognitive Sciences. 2019;23(10):851–864. DOI: https://doi.org/10.1016/j.tics.2019.07.003
38. Smith R., Lane R.D. The neural basis of ones own conscious and unconscious emotional states. Neuroscience and Biobehavioral Reviews. 2015;57:1–29. DOI: https://doi.org/10.1016/ j.neubiorev.2015.08.003
39. Scherer K.R., Moors A. The Emotion Process: Event Appraisal and Component Differentiation. Annual Review of Psychology. 2019;70(1):719–745. DOI: https://doi.org/10.1146/annurev-psych-122216-011854
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References
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Опубликован
2023-10-02
Как цитировать
Чипеева, Н. (2023). Нейрофизиологические механизмы эмоционального интеллекта. Психология. Психофизиология, 16(3), 65-74. https://doi.org/10.14529/jpps230306
Выпуск
Раздел
Общая психология, психология личности, история психологии
Copyright (c) 2023 Психология. Психофизиология
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