Neurobiological underpinning of ability emotional intelligence

Research from our lab

At our lab in Maribor, the first research trying to relate performance on the MSCEIT (at that time still an experimental version) with brain activity determined with the electroencephalogram (EEG) was conducted (Jaušovec et al., 2001). The results showed that high emotional intelligent individuals displayed less desynchronization in the upper alpha band, as well as more left hemispheric theta desynchronization. A finding that is similar to the one observed for the verbal and performance components of general intelligence supporting the neural efficiency hypothesis. 

A follow up study confirmed these findings (Jaušovec & Jaušovec 2005a). The analysis of EEG in relation to the level of emotional intelligence revealed a clear cut difference in brain oscillations between the induced upper alpha and gamma band. This difference was only present for the emotional intelligence task of identifying emotions in pictures. The pattern of event related synchronization/ desynchronization (ERD/ERS) in the induced upper alpha band was in line with the neural efficiency theory—high EI performers (HEIQ) displayed a time-related decrease in ERD, whereas average performers (AEIQ) displayed increased ERD (see Figure 1). On the other hand, the pattern of ERD/ERS in the induced gamma band was contrary to what would be predicted by the neural efficiency theory—the HEIQ group displayed induced gamma band ERS, while the AEIQ group displayed induced gamma band ERD. The difference increased from stimulus onset till 4000 ms. A possible explanation could be that the HEIQ individuals solved the EI task by relying more on figural and less on semantic information provided by the displayed pictures. This would explain the increased ERS in the induced gamma band and the decreased ERD in the induced upper alpha band shown by the HEIQ group. A reverse strategy–more semantic and less figural orientation–could be hypothesized for the AEIQ group of individuals. 

Figure 1 Mean percentages of ERD/ERS of induced upper alpha band activity of AEIQ and HEIQ individuals while identifying emotions in pictures (IDEM).

The focus in our next studies was on sex differences in EI and their relation to brain activity. In the first one (Jaušovec & Jaušovec 2005b), we investigated gender differences in resting EEG (in three individually determined narrow alpha frequency bands) related to the level of general and emotional intelligence. The main finding of the study was that males and females differed in resting brain activity related to their level of general intelligence. Brain activity in males decreased with the level of intelligence, whereas an opposite pattern of brain activity was observed in females. A finding already discussed in our previous blogs on sex differences. The differences between males and females in resting EEG related to emotional intelligence were much less pronounced than for general intelligence. In males, the correlations between log-transformed alpha power and experiential EI had a reverse pattern than the correlations with IQ, whereas strategic EI correlated negatively with log-transformed alpha power similarly as fluid intelligence did.

The same pattern of correlations between coherence in the parieto-occipital areas and the Experiential EI area score could be also observed in the lower-1 alpha band (see Figure 2). For females significant correlations between Strategic EI and decoupling in frontal brain areas and between Experiential EI and parieto-occipital coupling of brain areas were obtained. The reverse tendency in correlations for the area scores Experiential and Strategic EI is expected, because experiential EI refers to more intuitive components of EI, whereas strategic EI involves more ‘‘logic’’, indicating the respondents' ability to understand and manage emotions.

Figure 2 The bar charts represent correlation coefficients (y-axes) between Z-coherence measures (collapsed with respect to distances and location into frontal, parieto-occipital, and long distance) in three alpha sub-bands and general and emotional intelligence. * = p < .05,  ** = p < .01.

In yet another study, gender and ability (performance and emotional intelligence)-related differences in brain activity, assessed with EEG methodology, were investigated while respondents solved spatial rotation tasks and a task in which they identified emotions in faces (IDEM) (Jaušovec & Jaušovec, 2008). The most robust gender-related difference in brain activity was observed in the lower-2 alpha band. As expected, males and females displayed an inverse IQ-activation relationship in the domain in which they usually perform better: females in the emotional intelligence domain, and males in the visuospatial ability domain. This difference was also present when the relationships between gender and the levels of PIQ and EIQ were investigated.  Because activity in the lower-2 alpha band is related to attentional processes it can be assumed that especially females increased their level of attention when solving the rotation task. Males on the other hand solved both problems with a similar level of activity, which was higher than that of females while solving the IDEM task. A similar pattern of brain activity was also observed for the male/female respondents with different levels of PIQ and EIQ. The observed brain activity in the lower and upper alpha bands suggests that high PIQ females solved the problems with an overall increased level of activity. High EI males solved the problems also with increased brain activity—mainly in the frontal brain areas. One could speculate that high ability representatives of both genders to some extent compensate for their inferior problem solving skills (males in emotional tasks and females in spatial rotation tasks) by increasing their level of attention. The more frontal brain activity in men could point to an intense attentional control of working memory, whereas the more diffuse activity in females (HPIQ) could point to a general increased level of attention.

Other research

In an event related (ERP) study by Raz et al. (2013), the neural efficiency hypothesis put forward in our research was further elucidated. It was shown that participants with high EI exhibited significantly greater amplitudes of the early P2 and later P3 ERP components in response to emotional pictures, which was evident at posterior-parietal as well as at frontal scalp locations. The results suggest that visual emotional stimuli elicit greater mobilization of attention resources and subsequently more elaborative emotional information processing in individuals with high EI compared with those with low EI.

The studies employing different MRI techniques were mainly interested in the topography of brain areas involved in components of EI. Timoshanko et al. (2014) using methodology for neurometabolite quantification reported a positive correlation between EI and Choline (Cho) levels in the left dorsolateral prefrontal cortex (DLPFC) and the left amygdala. Concentrations between Cho levels in the left DLPFC showed positive correlations with MSCEIT Managing Emotions together with a significant association between left amygdala Cho concentration and MSCEIT Understanding Emotions. Similar findings employing MRI methodology were reported by Killgore et al. (2013). The authors found that the strategic emotional intelligence subscale correlated positively with gray matter volume in the left ventromedial prefrontal and insular cortex. Moreover, the study by Krueger et al. (2009) revealed that  key competencies underlying EI depend on distinct neural substrates in the prefrontal cortex (PFC). First, ventromedial PFC damage diminishes strategic EI,  hindering the understanding and managing of emotional information. Second, dorsolateral PFC damage diminishes experiential EI, decreasing the efficient perception and integration of emotional information.

In yet another study employing diffusion tensor imaging, Pisner et al. (2016) demonstrated that white matter integrity was positively correlated with the strategic area branches of the MSCEIT (understanding emotions and managing emotions), but not the experiential branches (perceiving and facilitating emotions). Specifically, the Understanding emotions branch was associated with greater fractional anisotropy (FA) within somatosensory and sensory-motor fiber bundles, particularly those of the left superior longitudinal fasciculus and corticospinal tract.

The results obtained in our lab as well as other studies employing methodology based on cardiovascular principles demonstrated that brain activity observed in relation to EI performance is similar to the one observed in relation to fluid intelligence (e.g. neural efficiency, white matter integrity), although to some extent different  -  greater activity is often observed in the amygdala, which is assumed to be associated with emotional processing.

References

Jaušovec, N.,  Jaušovec, K. & Gerlič, I. (2001). Differences in event related and induced EEG patterns in the theta and alpha frequency bands related to human emotional intelligence. Neuroscience Letters, 311, 93-96.

Jaušovec, N. & Jaušovec, K. (2005a). Differences in induced gamma and upper alpha oscillations in the human brain related to verbal/performance and emotional intelligence. International Journal of Psychophysiology.

Jaušovec, N., & Jaušovec, K, (2005b). Sex differences in brain activity related to general and emotional intelligence. Brain and  Cognition 59, 277-286.

Jaušovec, N., & Jaušovec, K. (2008). Spatial-rotation and recognizing emotions: Gender related differences in brain activity, Intelligence, 36, 383-393.

Killgore, W. D. S., Weber, M., Schwab, Z. J., DelDonno, S. R., Kipman, M., Weiner, M. R., & Rauch, S. L. (2012). Gray matter correlates of Trait and Ability models of emotional intelligence: NeuroReport, 23(9), 551–555. https://doi.org/10.1097/WNR.0b013e32835446f7

Krueger, F., Barbey, A. K., McCabe, K., Strenziok, M., Zamboni, G., Solomon, J., … Grafman, J. (2009). The neural bases of key competencies of emotional intelligence. Proceedings of the National Academy of Sciences, 106(52), 22486–22491. https://doi.org/10.1073/pnas.0912568106

Pisner, D. A., Smith, R., Alkozei, A., Klimova, A., & Killgore, W. D. S. (2016). Highways of the emotional intellect: white matter microstructural correlates of an ability-based measure of emotional intelligence. Social Neuroscience, 1–15. https://doi.org/10.1080/17470919.2016.1176600

Raz, S., Dan, O., Arad, H., & Zysberg, L. (2013). Behavioral and neural correlates of emotional intelligence: An Event-Related Potentials (ERP) study. Brain Research, 1526, 44–53. https://doi.org/10.1016/j.brainres.2013.05.048

Timoshanko, A., Desmond, P., Camfield, D. A., Downey, L. A., & Stough, C. (2014). A magnetic resonance spectroscopy (1H MRS) investigation into brain metabolite correlates of ability emotional intelligence. Personality and Individual Differences, 65, 69–74. https://doi.org/10.1016/j.paid.2014.01.022