Jun 1, 2017

The Flynn effect: Are we getting smarter?

The Flynn effect refers to generational IQ test norm changes first systematically described in the 1980s (Flynn, 1987). IQ changes have been observed to be positive (on average 3 IQ points per decade), but differentiated according to the investigated country and the intelligence test domain. Paradoxically, larger gains for fluid than for crystallized intelligence have been observed. In a recent meta-analysis by Pietsching and Voracek (2015), the progress and strength of the Flynn effect since the introduction of psychometric intelligence testing in the early 20th  century till 2013 was analyzed along with the moderating influences of age, economic growth, sample health status, and sex.

The key findings of the study can be summarized as follows:
  • Strong evidence for continuous global generational IQ test score gains in the general population over the past century (2 SD or 0.28 IQ points annually) were observed.
  • Gains in fluid IQ were substantially stronger than those in crystallized IQ (4.1 vs. 2.1 IQ points per decade).
  • The IQ change trajectories showed robust evidence for decreasing gains in recent decades.
  • Stronger gains were observed for adults than for children, showing large effects for fluid and spatial IQ.
  • Gross domestic product growth was positively associated with full-scale, crystallized, and spatial IQ but it showed negligible effects for fluid IQ.
  • IQ gains were not observed for psychometric g. 
The authors also provided an overview of possible explanations and theories for the observed gains, dividing them into environmental, biological, and hybrid (i.e., interacting biological and environmental). The environmental factors include education, technology, decreasing family size (dysgenic fertility), and test-taking behavior. It is assumed that the availability of education and technology for individuals from different socioeconomic backgrounds had a beneficial influence on the level of intelligence. This also had some influence on becoming more familiar with tests, mainly multiple choice, and by that changing test-taking behavior – principally increasing guessing.

Among biological factors, hybrid vigor is often mentioned, which refers to the mating of individuals from genetically dissimilar subpopulations, thereby increasing allelic heterozygosity and reducing homozygosity.

Hybrid factors include decreased blood lead levels, genomic imprinting (epigenetic inheritance), nutrition, and reduced pathogen stress. Moreover, more complex factors such as reduced IQ variability, effects of social multipliers, and decreasing life history speed (fewer offsprings), have been proposed in the literature.

The end of the Flynn effect?

In more recent decades a stagnation or even a decrease of the Flynn effect, mainly in more developed Western countries, has been reported. It is possible that the beneficial effects have caused the IQ increase to reach a ceiling. Some explanations have also linked these decreases to a higher proportion of immigrants from less developed countries. The table below summarizes the findings.

Sundet et al. (2004)
Substantial gains in intelligence were observed from the mid-1950s (test years) to the end 1960s–early 1970s, followed by a decreasing gain rate and a complete stop from the mid-1990s. The gains seemed to be mainly caused by decreasing prevalence of low scorers.
Teasdale & Owen (2005; 2008)
Intelligence test results from over 500,000 young Danish men, tested between 1959 and 2004, showed that performance peaked in the late 1990s, and has since declined moderately to pre-1991 levels. A contributing factor in this recent fall could be a simultaneous decline in proportions of students entering 3-year advanced-level school programs for 16–18 year olds.
Woodley & Meisenberg (2013)
Sixty-three observations of secular IQ changes were collected from three demographically diverse studies of the Dutch population for the period 1975–2005 (representing the 1950–1990 birth cohorts). Declines due to the anti-Flynn effect were estimated at -4.52 points per decade, whereas gains due to the Flynn effect were estimated at 2.18 points per decade. The N-weighted net of these is a loss of  -1.35 points per decade, suggesting an overall tendency towards decreasing IQ in the Netherlands with respect to these cohorts.
Dutton & Lynn (2013)
The average IQs of approximately 25,000 18–20 year old male military conscripts in Finland per year were analyzed for the years 1988 to 2009. The results showed increases in the scores on tests of shapes, number and words over the years 1988 to 1997 averaging 4.0 IQ points a decade. From 1997 to 2009 there were declines in all three tests averaging 2.0 IQ points a decade.
Dutton & Lynn (2013)
The results of the French WAIS III (1999) and the French WAIS IV (2008–9) were compared based on a sample of 79 subjects aged between 30 years and 63 years who took both tests in 2008–2009. It is shown that between 1999 and 2008–9 the French full scale IQ declined by 3.8 points.
Pietsching & Gitter (2015)
Germany and Austria
A meta-analysis (k=96; N=13,172) showed an inverse u-shaped trajectory of IQ test performance changes (initial increases and  subsequent decrease of performance) over 38 years (1977–2014).

The paradox of the Flynn effect

The cognitive functions that predict psychometric intelligence are thought to be working memory and processing speed. The idea behind the relation between processing speed and g is rather straightforward. Processing speed is viewed as a form of cognitive or neural limitation of processing a simple stimulus.  The most often studied parameters are reaction time and reaction time variability. Similarly, working memory and intelligence are highly related constructs. In a recent confirmatory analysis it was shown that working memory shared 83.4% of variance with fluid intelligence (Chuderski, 2015). Hence, with respect to the Flynn effect, one would expect that reaction time would over the years decrease, while working memory capacity would show an increase. However, the findings do not follow these trajectories.

Silverman (2010) analyzed simple visual reaction time (RT) obtained in a study conducted by Francis Galton in the late 1800s with the RTs obtained in subsequent studies. In Galton’s study, the median RTs obtained by men and women between ages 18 and 30 were 183.0 and 187.0 ms, whereas the RTs obtained in more recent studies (3,836 men and 3,093 women) were for men 250.43 ms (SD = 46.53) and 277.71 ms (SD = 30.76) for women. Moreover, the RTs obtained in the comparison studies were all longer than the RTs obtained by Galton. Out of several possible causes for longer RTs, Silverman suggested that the most tenable were that RT has been increased by the buildup of neurotoxins in the environment and by the increasing numbers of people in less than robust health who have survived into adulthood.

Based on Silverman’s findings, Woodley et al. (2013) in their meta-analytic study of RT concluded that the Victorians were cleverer than modern populations. Woodley and colleagues used the data on the slowing of simple reaction time described in a meta-analysis of 14 age-matched studies from Western countries conducted between 1889 and 2004. Using psychometric meta-analysis, they computed the true correlation between simple reaction time and g, yielding a decline of −1.16 IQ points per decade or −13.35 IQ points since Victorian times. Further, the difference between the meta-regression trend-weighted present (2004) simple RT mean (270.18 ms) and the trend weighted 1889 mean (193.17 ms) was 77 ms. Woodley and colleagues  further concluded that the most likely reason for this finding, also put forward by Silverman, was that those with poorer health and slower RTs surviving into adulthood are more numerous in the modern era than in the past.

Miller (1956) suggested that the typical short-term memory capacity (STMC) of an adult is 7 plus or minus two objects. Cowan (2005) suggested that the typical working memory capacity (WMC) of an adult is 4, plus or minus one object. Were these numbers lower in the past? This would be expected based on the Flynn effect. To answer this question, Gignac (2015) analyzed digit span (forward and backward) across 85 years of data (Ns of 7,077 and 6,841). The mean adult verbal STMC was estimated at 6.56 (±2.39), and the mean adult verbal WMC was estimated at 4.88 (±2.58). No increasing trend in the STMC or WMC test scores was observed from 1923 to 2008. This finding may be considered surprising, since memory span is so intimately related to fluid intelligence. At the moment they still lack a plausible explanation.

We can conclude that (Gignac, 2015, p. 93) “it may be prudent to acknowledge that the magnitude, pervasiveness, and true nature of the Flynn effect remains a substantially open question”.


Chuderski, A. (2015). The broad factor of working memory is virtually isomorphic to fluid intelligence tested under time pressure. Personality and Individual Differences, 85, 98–104. http://doi.org/10.1016/j.paid.2015.04.046

Cowan, N. (2005). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioural and Brain Sciences, 24, 87–185.

Dutton, E., & Lynn, R. (2013). A negative Flynn effect in Finland, 1997–2009. Intelligence, 41(6), 817–820. https://doi.org/10.1016/j.intell.2013.05.008

Dutton, E., & Lynn, R. (2015). A negative Flynn Effect in France, 1999 to 2008–9. Intelligence, 51, 67–70. https://doi.org/10.1016/j.intell.2015.05.005

Flynn, J.R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101, 171–191.

Gignac, G. E. (2015). The magical numbers 7 and 4 are resistant to the Flynn effect: No evidence for increases in forward or backward recall across 85 years of data. Intelligence, 48, 85–95. https://doi.org/10.1016/j.intell.2014.11.001

Miller, G.A. (1956). Themagical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.

Pietschnig, J., & Gittler, G. (2015). A reversal of the Flynn effect for spatial perception in German-speaking countries: Evidence from a cross-temporal IRT-based meta-analysis (1977–2014). Intelligence, 53, 145–153. https://doi.org/10.1016/j.intell.2015.10.004

Pietschnig, J., & Voracek, M. (2015). One Century of Global IQ Gains: A Formal Meta-Analysis of the Flynn Effect (1909–2013). Perspectives on Psychological Science, 10(3), 282–306. https://doi.org/10.1177/1745691615577701

Silverman, I.W. (2010). Simple reaction time: It is not what it used to be. The American Journal of Psychology, 123(1), 39. https://doi.org/10.5406/amerjpsyc.123.1.0039

Sundet, J. M., Barlaug, D. G., & Torjussen, T. M. (2004). The end of the Flynn effect? A study of secular trends in mean intelligence test scores of Norwegian conscripts during half a century. Intelligence, 32, 349–362.

Teasdale, T. W., & Owen, D. R. (2005). A long-term rise and recent decline in intelligence test performance: The Flynn Effect in reverse. Personality and Individual Differences, 39(4), 837–843. https://doi.org/10.1016/j.paid.2005.01.029

Teasdale, T. W., & Owen, D. R. (2008). Secular declines in cognitive test scores: A reversal of the Flynn Effect. Intelligence, 36(2), 121–126. https://doi.org/10.1016/j.intell.2007.01.007

Woodley, M. A., & Meisenberg, G. (2013). In the Netherlands the anti-Flynn effect is a Jensen effect. Personality and Individual Differences, 54(8), 871–876. https://doi.org/10.1016/j.paid.2012.12.022

Woodley, M. A., te Nijenhuis, J., & Murphy, R. (2013). Were the Victorians cleverer than us? The decline in general intelligence estimated from a meta-analysis of the slowing of simple reaction time. Intelligence, 41(6), 843–850. https://doi.org/10.1016/j.intell.2013.04.006

May 2, 2017

The future belongs to the stupid


Dysgenics is the study of factors that contribute to changes to the genes of the present generation that will cause the next generation to have a lower IQ (Flynn, 2013). For instance, the decrease in genotypic intelligence is assumed to be the result of dysgenic fertility –  a negative correlation between intelligence and the number of children (Lynn and Harvey, 2008). The figure below shows national IQs (left side, the darker the higher the national IQ) compared with national fertility rates (right side, reddish/purple 6-7 children; blue/green 1-2 children).

Such claims and predictions have provoked a lot of discussion. For example, Herrnstein and Murray (1994) in their controversial book “The Bell Curve” showed that in the US, women with an average IQ of 111 had 1.6 children, while women with an average IQ of 81 had 2.6 children. They further argued that a loss of three IQ points means an increase in welfare dependency by 7%; illegitimacy by 8%; men interned in jail by 12%; and the number of permanent high school dropouts by nearly 15%.

Yet another interesting line of reasoning was put forward by Cattell (1987), proposing that Western societies are breeding for pacifism and pleasure rather than aggression and that only warlike societies will transform humankind into a higher species. This was disputed by Flynn (2013), providing the example of Pol Pot who between 1973 and 1976 killed millions of Cambodians. Although his criteria were just political they to some extent discriminated those with superior IQ (the main criterion was occupation/education – people  wearing spectacles and possessing a bicycle). If he would have used IQ tests the decrease in intelligence would be about 6.5 points, whereas the educational criteria used in a semirural society would mean a drop of only one IQ point. Sunic (2009) provides another instance from history accusing the former Yugoslav communists of killing Croatian intellectuals and by that lowering the national IQ of Croatia to 90 points. A provocative question: Did Hitler lose the war because he killed or expelled so many Jews?

Setting the holocausts of world history aside, there is concern that ordinary patterns of reproduction will lower genotypic IQ. The estimates are about half IQ point per familial generation (about 25 – 30 years) for the US and UK (Lynn and Vanhanen, 2012). Meisenberg (2010) analyzing the NLSY79 survey[1], concluded: “Assuming an indefinite continuation of current fertility patterns, an unchanging environment and a generation time of 28 years, the IQ will decline by about 2.9 points/century as a result of genetic selection. The proportion of highly gifted people with an IQ higher than 130 will decline by 11.5% in one generation and by 37.7% in one century.”

In contrast, actual measurements of intelligence (phenotypic intelligence) show that IQ is increasing which was dubbed the Flynn effect (Flynn, 2013).  We will discuss the Flynn effect in more detail in our next blog, at this point just a brief summary will be presented for better understanding. The effect, although still not well understood, is often explained as favorable environment enhancement of human intelligence, which enables people to create favorable environments. Hence, small rises in IQ caused by education were translated into even greater environmental improvements, which raised the intelligence of the next generation even higher. But as the pessimists fear, this compensation cannot be expected to continue indefinitely. On the contrary, the environmental improvements are likely to stop giving positive returns. When their impact is exhausted, and if dysgenic fertility continues, phenotypic intelligence will begin to decline and worsening environments will further reduce intelligence. Another point put forward by Lynn (2011) was that although the soil is continually enriched, this does not make insignificant the fact that the seeds have deteriorated.


The opposite of dysgenics is eugenics with the main goal of improving the genetic inheritance of the human race, for instance cognitive abilities (Flynn, 2013). Its literal meaning—good birth—suggests a suitable goal for all prospective families and  societies, however the relation to Germany's Nazi regime gives eugenics a strong negative valence (Goering, 2014).

The eugenic idea can be tracked back to Plato who recommended a state-run program of mating intended to strengthen the guardian class in his Republic. The modern version of eugenics had its start with the 19th  century British scientist Francis Galton. He wanted to “improve human stock” through scientific management of mating – to create better humans. The eugenicists considered a twofold strategy to deal with the problem, which they designated positive and negative eugenics. Positive eugenics consisted of policies designed to persuade the more intelligent to have greater numbers of children. Negative eugenics consisted of the dissemination of knowledge of birth control and the sterilization of the mentally retarded (in the language of that time, this included individuals who were poor, mentally insane, feeble-minded, idiots, drunken and more). Sterilization for eugenic purposes was first introduced in Indiana (US) in 1907 and subsequently in most of the American states and throughout most of Europe and became popular when the Nazis came to power in Germany. In the second half of the twentieth century, public opinion turned against eugenics and from the 1960s onwards eugenics became virtually universally condemned. However, involuntary eugenic sterilizations of “feeble-minded” women in a variety of US states didn't officially end until the 1970s, and may continue covertly in some state institutions (Goering, 2014).

There were also several contemporary attempts to renew eugenics. For instance, with the creation of a sperm bank collecting sperm from Nobel laureates, others deemed “geniuses” and Olympic level athletes (Repository for Germinal Choice). The sperm bank produced about 219 children. The most popular, whose life was exposed to the public, is Doron BlakeAs a newborn he could mark time to classical music with his hands. By age 2, he was using a computer. By kindergarten, he was reading Hamlet and learning algebra. At six his IQ was off the charts. He trained to become a teacher and is at the moment self employed. The Repository for Germinal Choice closed in 1999, two years after the death of its founder Robert Graham. There are now many others, such as the Fairfax Cryobankclaiming that it is easier to get into an Ivy League school than it is to get into their donor program.

Further, advances in genetic technology to test/manipulate for a large array of genes related to a wide variety of diseases and traits opens the possibility of “the new eugenics” of biotechnology. The most promising development would be embryo selection, whereas genetic modification is a less viable option. Most countries have not yet legislated genetic modification in human reproduction, but of those that have, all have banned it. On the other hand, embryo selection involves producing a large number of embryos and reading their genomes to find the one that most closely matches the parents’ desires. It is estimated that in 20-40 years the technology for safe, effective and cheap preimplantation genetic diagnosis will be available. Another prognosis is that this will lead to obsolescence of sex for procreation. The problem at the moment is that, although intelligence is on average 50% heritable (40% at the age of 4 years and 80% at the age of 65), there is no single gene that would regulate intelligence, which is probably polygenic (for more information, see Posthuma’s presentation).

Richard Dawkins (From the Afterword, The Herald, 20 Nov 2006argued that the broad public disapproval of designer babies is actually hypocritical. In his opinion there is no moral difference between breeding for musical ability and forcing a child to take music lessons, or between the training of fast runners and high jumpers and breeding them.

Regardless of our opinions and ethical concerns, “In technology, whatever can be done will be done” (Grove, 1998).


Cattell, R. B. (1987). Beyondism: Religion from science. New York, NY: Praeger.
Flynn, J. R. (2013). Intelligence and human progress: the story of what was hidden in our genes. Oxford, UK: Academic Press.

Goering, Sara, "Eugenics", The Stanford Encyclopedia of Philosophy (Fall 2014 Edition), Edward N. Zalta (ed.), https://plato.stanford.edu/archives/fall2014/entries/eugenics/.

Grove, A. S. (1998). Only the paranoid survive: how to exploit the crisis points that challenge every company and career. London: Profile Books.

Herrnstein, R., & Murray, C. (1994). The Bell Curve. New York: Random House.
Lynn, R. (2011). Dysgenics: Genetic deterioration in modern populations (2nd ed.). Belfast: Ulster Institute for Social Research.

Lynn, Richard; Vanhanen, Tatu (2012). Intelligence: A unifying construct for the social sciences. Ulster: Ulster Institute for Social Research.

Lynn, R., & Harvey, J. (2008). The decline of the world’s IQ. Intelligence, 36(2), 112–120. https://doi.org/10.1016/j.intell.2007.03.004

Meisenberg, G. (2010). The reproduction of intelligence. Intelligence, 38(2), 220–230. https://doi.org/10.1016/j.intell.2010.01.003

Sunic, T. (2009). Dysgenics of a Communist killing field: The Croatian Bleiburg. Brussels Belgium: European Action.

[1] Cognitive ability was measured when respondents were aged 15–23, while the number of children was obtained at ages 39–47.

Apr 15, 2017

Neurobiological underpinning of trait emotional intelligence

The neurobiological underpinning of ability EI was discussed in our previous blog.  Here we present research about the trait EI –  brain relationship. The research is grounded in theoretical assumptions on emotional processes, their influence on decision-making and brain areas involved in their normal and pathological functioning. Central to this are two assumptions: (i) the role of hemispheric lateralization in emotions and (ii) the somatic marker hypothesis.

The role of hemispheric lateralization in emotions has been the subject of interest for several decades (Harmon-Jones et al., 2010). The asymmetric involvement of prefrontal cortical regions in positive affect (or approach motivation) and negative affect (or withdrawal motivation) was suggested over 70 years ago based on lesion studies of individuals who had suffered damage to the right or left anterior cortex. The findings were later supported by research, which mainly employed the Wada test. The test involves injecting amytal into one of the internal carotid arteries and suppressing the activity of one hemisphere. Injections in the left side produced depressed affect, whereas injections in the right side produced euphoria. These findings have been also confirmed in non-human animals, ranging from great apes and reptiles to chicks, amphibians and even spiders. Summarizing the research on frontal lateralization, Harmon-Jones et al. (2010) concluded that there is robust evidence for the claim that greater left as compared to right frontal activity is associated with approach motivational processes, whereas the reverse hypothesis (withdrawal-right-frontal-region) in the motivational direction model was not as extensively investigated.

Support for a frontal asymmetry in relation to the level of trait emotional intelligence comes from the study by Mikolajczak et al. (2010). They showed that higher trait EI scores were associated with a greater relative left-sided frontal activation determined with EEG during a resting eyes open/closed condition. The effect size for the global score was 0.82, which is considered to be a large effect according to Cohen’s norms for social sciences. The strongest relationships were obtained with the factors ‘‘sociability” and ‘‘self-control”. Mikolajczak et al. (2010, p. 179) concluded: “[…] it seems that the construct of trait emotional intelligence might be particularly well-suited to capture the socio-emotional dispositions affected by frontal EEG asymmetries”.

The second theory that shaped research into the trait EI-brain relationship was the somatic marker hypothesis which suggests that several brain structures and operations are required for the normal function of decision-making (Damasio, 1999). Central in this process is the amygdala triggering somatic states activated by primary[1] and secondary[2] inducers. Representations of these states can be sub-conscious (brainstem) or perceived as a feeling when brought to the level of insular (SI, SII) and posterior cingulate cortices. All of these somatic states, which can be either positive or negative, are then summed into one overall somatic state providing a substrate for biasing our decision-making. When this process is carried out in the striatum, the person acts without a conscious decision to do so. In contrast, decision-making is under volitional control when this process takes place at (i) the level of the lateral orbitofrontal cortex - the person favors a plan of action and (ii) at the anterior cingulate - the person executes a plan of action. Thus, the conceptual nexus between the somatic marker hypothesis and emotional intelligence is mainly in the way the latter is defined – an array of interrelated emotional, personal and social competencies determining the ability to actively and effectively cope with daily demands.

Support for the somatic marker hypothesis comes from lesion studies. Bar-On et al. (2003) showed that only patients with lesions in the somatic marker circuitry revealed low emotional intelligence and poor judgment in decision-making as well as disturbances in social functioning, in spite of normal levels of cognitive intelligence  and the absence of psychopathology. Further support was provided by neuroimaging studies employing different methods such as EEG (Craig et al., 2009) and fMRI - showing also activity in support of the neural efficiency hypothesis (Killgore and Yurgelund-Todd, 2007). Voxel-based morphometry correlations between the level of the EI interpersonal factor and regional gray matter density were reported for an anatomical cluster that included the right anterior insula and the medial prefrontal cortex (Takeuchi et al, 2011). Similar brain areas in relation to trait EI were also observed in studies using resting-state functional connectivity (Takeuchi et al., 2013a) and diffusion tensor imaging (Takeuchi et al., 2013b).

In a recent review paper by Hogeveen et al. (2016), a miniature meta-analysis was conducted to determine whether any brain regions have been ‘reliably’ associated with EI. Regions-of-interest were manually constructed based on the relevant neuroimaging tables reported in the reviewed papers, and these regions-of-interest were placed on a glass brain for visualization. The resulting figure revealed a striking level of inconsistency in brain regions that have been associated with EI using traditional measures. To overcome these problems, the authors tried to map different emotional intelligence factors with findings obtained in lesion studies. The table below summarizes the obtained findings.

Recognizing Emotional States in the Self and Others
Emotional Awareness
Anterior Insula (AI)
Anterior Cingulate Cortex (ACC)
Ventromedial Prefrontal Cortex (vmPFC)
Emotion Recognition
Ventromedial Prefrontal Cortex (vmPFC)
Using Emotions to Facilitate Thought and Behavior
Empathy and Prosocial Behavior
Ventrolateral Prefrontal
Cortex (vlPFC)

Emotional Memory
Medial temporal lobes that encompass the amygdala, hippocampus, and perirhinal cortex
Understanding How Emotions Shape One's Own Behavior and the Behavior of Others
Ventromedial Prefrontal Cortex (vmPFC)
Emotion Regulation
Ventromedial Prefrontal Cortex (vmPFC)

These neurobiological findings have been used as evidence to distinguish EI abilities from cognitive intelligence. According to Hogeveen et al. (2016), such a separation seems untenable since the latest advances in psychology and neuroscience have reliably suggested that emotion and cognition are very much integrated in the brain and together they shape goal-directed behavior. In this direction points a recent study by Yao et al. (2017, published online) utilizing voxel-based morphometry  to investigate the neural structures underlying critical thinking disposition in relation to the level of emotional intelligence. The central finding was that: “Specifically, critical thinking disposition was associated with decreased GMV of the temporal pole for individuals who have relatively higher emotional intelligence rather than lower emotional intelligence. The results of the present study indicate that people who have higher emotional intelligence exhibit more effective and automatic processing of emotional information and tend to be strong critical thinkers.”


Bar-On, R. (2003). Exploring the neurological substrate of emotional and social intelligence. Brain, 126(8), 1790–1800. https://doi.org/10.1093/brain/awg177

Craig, A., Tran, Y., Hermens, G., Williams, L. M., Kemp, A., Morris, C., & Gordon, E. (2009). Psychological and neural correlates of emotional intelligence in a large sample of adult males and females. Personality and Individual Differences, 46(2), 111–115. https://doi.org/10.1016/j.paid.2008.09.011

Damasio, A.R. (1999) The feeling of what happens: body and emotion in the making of consciousness. New York: Harcourt Brace.

Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biological Psychology, 84(3), 451–462. https://doi.org/10.1016/j.biopsycho.2009.08.010

Hogeveen, J., Salvi, C., & Grafman, J. (2016). “Emotional Intelligence”: Lessons from Lesions. Trends in Neurosciences, 39(10), 694–705. https://doi.org/10.1016/j.tins.2016.08.007

Killgore, W. D., & Yurgelun-Todd, D. A. (2007). Neural correlates of emotional intelligence in adolescent children. Cognitive, Affective, & Behavioral Neuroscience, 7(2), 140–151.

Mikolajczak, M., Bodarwé, K., Laloyaux, O., Hansenne, M., & Nelis, D. (2010). Association between frontal EEG asymmetries and emotional intelligence among adults. Personality and Individual Differences, 48(2), 177–181. https://doi.org/10.1016/j.paid.2009.10.001

Takeuchi, H., Taki, Y., Nouchi, R., Sekiguchi, A., Hashizume, H., Sassa, Y., … Kawashima, R. (12/2013a). Resting state functional connectivity associated with trait emotional intelligence. NeuroImage, 83, 318–328. https://doi.org/10.1016/j.neuroimage.2013.06.044

Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Fukushima, A., & Kawashima, R. (2011). Regional gray matter density associated with emotional intelligence: Evidence from voxel-based morphometry. Human Brain Mapping, 32(9), 1497–1510. https://doi.org/10.1002/hbm.21122

Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Nagase, T., … Kawashima, R. 
(05/2013b). White matter structures associated with emotional intelligence: Evidence from diffusion tensor imaging. Human Brain Mapping, 34(5), 1025–1034. https://doi.org/10.1002/hbm.21492

Yao, X., Yuan, S., Yang, W., Chen, Q., Wei, D., Hou, Y., … Yang, D. (2017). Emotional intelligence moderates the relationship between regional gray matter volume in the bilateral temporal pole and critical thinking disposition. Brain Imaging and Behavior. https://doi.org/10.1007/s11682-017-9701-3

[1] Primary inducers are unconditioned stimuli that are innately set as pleasurable or aversive, or conditioned stimuli. When conditioned stimuli are present in the immediate environment, a somatic response is automatically generated.
[2] Secondary inducers are units generated by recall or by thought, eliciting a somatic response when brought to memory.

Apr 8, 2017

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.


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