Neurophysiological foundations of emotional intelligence
Keywords:
emotional intelligence, emotions, emotional regulation, brain networks, resting-state functional connectivity
Abstract
Introduction. Emotional intelligence (EI), as well as cognitive intelligence, relates to academic success and social and interpersonal communication. The problem of studying EI is topical in the boundaries of neuroscience abilities research using neuroimaging methods and sheds light on the psychophysiological problem. Aims. This paper presents a review and analysis of the literature about emotional intelligence and the neural pathways responsible for it. Theoretical basis: a narrative overview of published original research and review articles indexed in Google Scholar. Results. EI is based on the brain systems that are engaged in mental functions and associated with the activity of large-scale networks and global characteristics of functional connectivity. These networks embrace the neuronal ensembles of cortical and subcortical regions associated with the perception and recognition of emotions, as well as the generation of affective responses and regulation of emotions. Neuroimaging studies of EI (measured by the test or the self-report method) also demonstrate the involvement of resting-state networks. EI development differs from that of cognitive intelligence. A possible reason for that can be associated with the accumulation of individual emotional experience and sociocultural context and the changes related to brain structures and neural networks during lifespan. Conclusion: This paper provides a better understanding of the relationship between resting-state neural networks and EI. However, comprehensive research on EI and its relationship to large-scale resting networks as well as the global characteristics of functional connectivity is insufficient. Thus, this area of research is promising for science.
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References
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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
40. Siemer M., Mauss I., Gross J.J. Same situation-Different emotions: How appraisals shape our emotions. Emotion. 2007;7(3):592–600. DOI: https://doi.org/10.1037/1528-3542.7.3.592
41. Smith R., Alkozei A., Bao J. et al. Resting state functional connectivity correlates of emotional awareness. NeuroImage. 2017;159:99–106. DOI: https://doi.org/10.1016/j.neuroimage.2017.07.044
42. Goulden N., Khusnulina A., Davis N.J. et al. The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM. NeuroImage. 2014;99:180–190. DOI: https://doi.org/10.1016/j.neuroimage.2014.05.052
43. 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
44. Dehaene S., Charles L., King J.R., Marti S. Toward a computational theory of conscious processing. Current Opinion in Neurobiology. 2014;25:76–84. DOI: https://doi.org/10.1016/j.conb.2013.12.005
45. Diano M., Celeghin A., Bagnis A., Tamietto M. Amygdala Response to Emotional Stimuli without Awareness: Facts and Interpretations. Frontiers in Psychology. 2017;7. DOI: https://doi.org/10.3389/fpsyg.2016.02029
46. Takeuchi H., Taki Y., Nouchi R. et al. Resting state functional connectivity associated with trait emotional intelligence. NeuroImage. 2013;83:318–328. DOI: https://doi.org/10.1016/ j.neuroimage.2013.06.044
47. Hampson M., Driesen N., Roth J.K. et al. Functional connectivity between task-positive and task-negative brain areas and its relation to working memory performance. Magnetic Resonance Imaging. 2010;28(8):1051–1057. DOI: https://doi.org/10.1016/j.mri.2010.03.021
48. Marchetti I., Koster E.H.W., Sonuga-Barke E.J., de Raedt R. The Default Mode Network and Recurrent Depression: A Neurobiological Model of Cognitive Risk Factors. Neuropsychology Review. 2012;22(3):229–251. DOI: https://doi.org/10.1007/s11065-012-9199-9
49. Pan W., Wang T., Wang X. et al. Identifying the Core Components of Emotional Intelligence: Evidence from Amplitude of Low-Frequency Fluctuations during Resting State. PLoS ONE. 2014;9(10):e111435. DOI: https://doi.org/10.1371/journal.pone.0111435
50. Lizeretti N.P., Extremera N. Emotional Intelligence and Clinical Symptoms in Outpatients with Generalized Anxiety Disorder (GAD). Psychiatric Quarterly. 2011;82(3):253–260. DOI: https://doi.org/10.1007/s11126-011-9167-1
51. Kilner J.M., Neal A., Weiskopf N. et al. Evidence of Mirror Neurons in Human Inferior Frontal Gyrus. Journal of Neuroscience. 2009;29(32):10153–10159. DOI: https://doi.org/10.1523/ JNEUROSCI.2668-09.2009
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Published
2023-10-02
How to Cite
Chipeeva, N. (2023). Neurophysiological foundations of emotional intelligence. Psychology. Psychophysiology, 16(3), 65-74. https://doi.org/10.14529/jpps230306
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Section
Methodological and theoretical issues of psychology
Copyright (c) 2023 Psychology. Psychophysiology
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
