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Thursday, September 5, 2024

Emotional intelligence

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Emotional_intelligence

Emotional intelligence
(EI) is defined as the ability to perceive, use, understand, manage, and handle emotions. People with high emotional intelligence can recognize their own emotions and those of others, use emotional information to guide thinking and behavior, discern between different feelings and label them appropriately, and adjust emotions to adapt to environments.

Although the term first appeared in 1964, it gained popularity in the 1995 bestselling book Emotional Intelligence by science journalist Daniel Goleman. Goleman defined EI as the array of skills and characteristics that drive leadership performance. Some researchers suggest that emotional intelligence can be learned and strengthened, while others claim that it is an innate characteristic.

Various models have been developed to measure EI over the years. In 1987, Keith Beasley used the term Emotional Quotient (EQ) in an article, named after the Intelligence Quotient (IQ). The trait model, developed by Konstantinos V. Petrides in 2001, focuses on self reporting of behavioral dispositions and perceived abilities. The ability model (Mayeret al., 2023), focuses on the individual's ability to process emotional information and use it to navigate the social environment. Goleman's original model may now be considered a mixed model that combines what has since been modelled separately as ability EI and trait EI.

Recent research has focused on emotion recognition, which refers to the attribution of emotional states based on observations of visual and auditory nonverbal cues. In addition, neurological studies have sought to characterize the neural mechanisms of emotional intelligence.

Studies show that there is a correlation between people with high EI and positive workplace performance, although no causal relationships have been shown. EI is typically associated with empathy because it involves a person relating their personal experiences with those of others. Since its popularization in recent decades and links to workplace performance, methods of developing EI have become sought by people seeking to become more effective leaders.

Criticisms have centered on whether EI is a real intelligence, and whether it has incremental validity over IQ and the Big Five personality traits. However, meta-analyses have found that certain measures of EI have validity even when controlling for IQ and personality.

History

The concept of Emotional Strength was introduced by Abraham Maslow in the 1950s. The term "emotional intelligence" seems first to have appeared in a 1964 paper by Michael Beldoch, and in the 1966 paper by B. Leuner titled Emotional Intelligence and Emancipation which appeared in the psychotherapeutic journal Practice of child psychology and child psychiatry.

In 1983, Howard Gardner's Frames of Mind: The Theory of Multiple Intelligences introduced the idea that traditional types of intelligence, such as IQ, fail to fully explain cognitive ability. He introduced the idea of multiple intelligences which included both interpersonal intelligence (the capacity to understand the intentions, motivations and desires of other people) and intrapersonal intelligence (the capacity to understand oneself, to appreciate one's feelings, fears and motivations).

The first published use of the term "EQ" (Emotional Quotient) is an article by Keith Beasley in 1987 in the British Mensa magazine.

In 1989, Stanley Greenspan put forward a model to describe EI, followed by another by Peter Salovey and John Mayer the following year.

However, the term became widely known with the publication of Goleman's book: Emotional Intelligence – Why it can matter more than IQ (1995). It is to this book's bestselling status that the term can attribute its popularity. Goleman followed up with several similar publications that reinforce use of the term.

Late in 1998, Goleman's Harvard Business Review article entitled "What Makes a Leader?" caught the attention of senior management at Johnson & Johnson's Consumer Companies (JJCC). The article spoke to the importance of Emotional Intelligence (EI) in leadership success, and cited several studies that demonstrated that EI is often the distinguishing factor between great leaders and average leaders. JJCC funded a study which concluded that there was a strong relationship between superior performing leaders and emotional competence, supporting theorists' suggestions that the social, emotional, and relational competency set referred to as Emotional Intelligence is a distinguishing factor in leadership performance.

Tests measuring EI have not replaced IQ tests as a standard metric of intelligence, and Emotional Intelligence has received criticism regarding its role in leadership and business success.

Definitions

Emotional intelligence has been defined by Peter Salovey and John Mayer as "the ability to monitor one's own and other people's emotions, to discriminate between different emotions and label them appropriately, and to use emotional information to guide thinking and behavior". This definition was later broken down and refined into four proposed, distinct abilities:

  1. Perceiving
  2. Using
  3. Understanding
  4. Managing emotions

Emotional intelligence also reflects an ability to use intelligence, empathy, and emotions to enhance understanding of interpersonal dynamics. However, substantial disagreement exists regarding the definition of EI, with respect to both terminology and operationalization. Currently, there are three main models of EI:

  1. Ability model
  2. Mixed model (usually subsumed under trait EI)
  3. Trait model

Different models of EI have led to the development of various instruments for the assessment of the construct. While some of these measures may overlap, most researchers agree that they tap different constructs.

Specific ability models address the ways in which emotions facilitate thought and understanding. For example, emotions may interact with thinking and allow people to be better decision makers. A person who is more emotionally responsive to crucial issues will attend to the more crucial aspects of their life. The emotional facilitation factor also involves knowing how to include or exclude emotions from thought, depending on the context and situation. This ability is related to emotional reasoning and understanding in response to the people, environment, and circumstances one encounters.

Ability model

Salovey and Mayer's strive to define EI within the confines of the standard criteria for a new intelligence. Following their continuing research, their initial definition of EI was revised to "The ability to perceive emotion, integrate emotion to facilitate thought, understand emotions, and to regulate emotions to promote personal growth." However, after pursuing further research, their definition of EI evolved into "the capacity to reason about emotions, and of emotions, to enhance thinking. It includes the abilities to accurately perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowledge, and to reflectively regulate emotions so as to promote emotional and intellectual growth."

The ability-based model views emotions as useful sources of information that help one to make sense of and navigate the social environment. The model proposes that individuals vary in their ability to process information of an emotional nature and in their ability to relate emotional processing to wider cognition. This ability manifests itself in certain adaptive behaviors. The model claims that EI includes four types of abilities:

Perceiving emotions
the ability to detect and decipher emotions in faces, pictures, voices, and cultural artifacts—including the ability to identify one's own emotions. Perceiving emotions is a basic aspect of emotional intelligence, as it makes all other processing of emotional information possible.
Using emotions
the ability to harness emotions to facilitate various cognitive activities, such as thinking and problem-solving. The emotionally intelligent person can capitalize fully upon his or her changing moods in order to best fit the task at hand.
Understanding emotions
the ability to comprehend emotion language and to appreciate complicated relationships among emotions. For example, understanding emotions encompasses the ability to be sensitive to slight variations between emotions, and the ability to recognize and describe how emotions evolve over time.
Managing emotions
the ability to regulate emotions in both ourselves and in others. The emotionally intelligent person can harness emotions, even negative ones, and manage them to achieve intended goals.

The ability EI model has been criticized for lacking face and predictive validity in the workplace. However, in terms of construct validity, ability EI tests have great advantage over self-report scales of EI because they compare individual maximal performance to standard performance scales and do not rely on individuals' endorsement of descriptive statements about themselves.

Measurement

The current measure of Mayer and Salovey's model of EI, the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT), is based on a series of emotion-based problem-solving items. Consistent with the model's claim of EI as a type of intelligence, the test is modeled on ability-based IQ tests. By testing a person's abilities on each of the four branches of emotional intelligence, it generates scores for each of the branches as well as a total score.

Central to the four-branch model is the idea that EI requires attunement to social norms. Therefore, the MSCEIT is scored in a consensus fashion, with higher scores indicating higher overlap between an individual's answers and those provided by a worldwide sample of respondents. The MSCEIT can also be expert-scored so that the amount of overlap is calculated between an individual's answers and those provided by a group of 21 emotion researchers.

Although promoted as an ability test, the MSCEIT test is unlike standard IQ tests in that its items do not have objectively correct responses. Among other challenges, the consensus scoring criterion means that it is impossible to create items (questions) that only a minority of respondents can solve, because, by definition, responses are deemed emotionally "intelligent" only if the majority of the sample has endorsed them. This and other similar problems have led some cognitive ability experts to question the definition of EI as a genuine intelligence.

In a study by Føllesdal, the MSCEIT test results of 111 business leaders were compared with how their employees described their leader. It was found that there were no correlations between a leader's test results and how he or she was rated by the employees, with regard to empathy, ability to motivate, and leader effectiveness. Føllesdal also criticized the Canadian company Multi-Health Systems, which administers the test. The test contains 141 questions but it was found after publishing the test that 19 of these did not give the expected answers. This has led Multi-Health Systems to remove answers to these 19 questions before scoring but without stating this officially.

Other measurements

Various other specific measures also assess ability in emotional intelligence. These include:

Diagnostic Analysis of Non-verbal Accuracy (DANVA)
The Adult Facial version includes 24 photographs of equal amount of happy, sad, angry, and fearful facial expressions of both high and low intensities which are balanced by gender. The tasks of the participants is to answer which of the four emotions is present in the given stimuli.
Japanese and Caucasian Brief Affect Recognition Test (JACBART)
Participants try to identify 56 faces of Caucasian and Japanese individuals expressing seven emotions such happiness, contempt, disgust, sadness, anger, surprise, and fear, which may also trail off for 0.2 seconds to a different emotion.
Situational Test of Emotional Understanding (STEU)
Test-takers complete 42 multiple-choice items assessing whether they understand which of five emotions a person would be experiencing in a given situation. There is also a brief version (STEU-B) consisting of 19 items.
Situational Test of Emotion Management (STEM)
Test-takers complete 44 multiple-choice items in which they select which of four possible responses is the most effective action to manage emotions in a specified situation. There is also a brief version (STEM-B) consisting of 18 items.

Mixed model

The model introduced by Daniel Goleman focuses on EI as a wide array of competencies and skills that drive leadership performance. Goleman's model outlines five main EI constructs:

  1. Self-awareness – the ability to know one's emotions, strengths, weaknesses, drives, values, and goals and recognize their impact on others while using gut feelings to guide decisions
  2. Self-regulation – involves controlling or redirecting one's disruptive emotions and impulses and adapting to changing circumstances
  3. Social skill – managing relationships to get along with others
  4. Empathy – considering other people's feelings especially when making decisions
  5. Motivation – being aware of what motivates them

Goleman includes a set of emotional competencies within each construct of EI. Emotional competencies are not innate talents, but rather learned capabilities that must be worked on and can be developed to achieve outstanding performance. Goleman posits that individuals are born with a general emotional intelligence that determines their potential for learning emotional competencies. Goleman's model of EI has been criticized in the research literature as mere "pop psychology".

Measurement

Two measurement tools are based on the Goleman model:

  1. The Emotional Competence Inventory (ECI), which was created in 1999, and the Emotional and Social Competence Inventory (ESCI), a newer edition of the ECI, which was developed in 2007. The Emotional and Social Competence – University Edition (ESCI-U) is also available. These tools developed by Goleman and Boyatzis provide a behavioral measure of the Emotional and Social Competencies.
  2. The Emotional Intelligence Appraisal, which was created in 2001 and which can be taken as a self-report or 360-degree assessment.

Trait model

Konstantinos V. Petrides proposed a conceptual distinction between the ability-based model and a trait-based model of EI and has been developing the latter over many years in numerous publications. Trait EI is "a constellation of emotional self-perceptions located at the lower levels of personality." In layman's terms, trait EI refers to an individual's self-perceptions of their emotional abilities. This definition of EI encompasses behavioral dispositions and self-perceived abilities and is measured by self report, as opposed to the ability-based model which refers to actual abilities, which have proven highly resistant to scientific measurement. Trait EI should be investigated within a personality framework. An alternative label for the same construct is trait emotional self-efficacy.

The trait EI model is general and subsumes the Goleman model discussed above. The conceptualization of EI as a personality trait leads to a construct that lies outside the taxonomy of human cognitive ability. This is an important distinction in that it bears directly on the operationalization of the construct and the theories and hypotheses that are formulated about it.

Measurement

There are many self-report measures of EI, including the EQ-i, the Swinburne University Emotional Intelligence Test (SUEIT), and the Schutte EI model. None of these assess intelligence, abilities, or skills (as their authors often claim), but rather, they are limited measures of trait emotional intelligence. The most widely used and widely researched measure of self-report or self-schema (as it is currently referred to) emotional intelligence is the EQ-i 2.0. Originally known as the BarOn EQ-i, it was the first self-report measure of emotional intelligence available, the only measure predating Goleman's bestselling book.

The Trait Emotional Intelligence Questionnaire (TEIQue) provides an operationalization for the model of Konstantinos V. Petrides and colleagues, that conceptualizes EI in terms of personality. The test encompasses 15 subscales organized under four factors: well-being, self-control, emotionality, and sociability. The psychometric properties of the TEIQue were investigated in a study on a French-speaking population, where it was reported that TEIQue scores were globally normally distributed and reliable.

The researchers found TEIQue scores were unrelated to nonverbal reasoning (Raven's matrices), which they interpreted as support for the personality trait view of EI (as opposed to the view that it is a form of intelligence). As expected, TEIQue scores were positively related to some of the Big Five personality traits (extraversion, agreeableness, openness, conscientiousness) as well as inversely related to others (alexithymia, neuroticism). A number of quantitative genetic studies have been carried out within the trait EI model, which have revealed significant genetic effects and heritabilities for all trait EI scores. Two studies (one a meta-analysis) involving direct comparisons of multiple EI tests yielded very favorable results for the TEIQue.

The Big Five Personality Traits theory offers a straightforward framework for understanding and enhancing relationships by uncovering the motivations behind people's behaviors. This theory is equally valuable for self-awareness and for improving interpersonal dynamics. Also referred to as the Five Factor Model, the Big Five Model is the most widely accepted personality theory. It suggests that personality can be distilled into five fundamental dimensions, often remembered as CANOE or OCEAN (conscientiousness, agreeableness, neuroticism, openness, extraversion). In contrast to other trait theories that sort individuals into binary categories (e.g. introvert or extrovert), the Big Five Model asserts that each personality trait exists on a spectrum. Consequently, individuals are positioned along a continuum between two contrasting poles.

General effects

A review published in the Annual Review of Psychology in 2008 found that higher emotional intelligence is positively correlated with:

  1. Better social relations for children – Among children and teens, emotional intelligence positively correlates with good social interactions and relationships, and negatively correlates with deviance from social norms and anti-social behavior measured both in and out of school as reported by children themselves, their own family members, and their teachers.
  2. Better social relations for adults – High emotional intelligence among adults is correlated with better self-perception of social ability and more successful interpersonal relationships, with less interpersonal aggression and problems.
  3. Highly emotionally intelligent people are perceived more positively by others – Other people perceive those with high EI to be more pleasant, socially skilled, and empathic.
  4. Better academic achievement – Emotional intelligence is correlated with greater achievement in academics as reported by teachers, but generally not higher grades once the factor of IQ is taken into account.
  5. Better social dynamics at work as well as better negotiating ability.
  6. Better well-being – Emotional intelligence is positively correlated with higher life satisfaction and self-esteem, and lower levels of insecurity or depression. It is also negatively correlated with poor health choices and behavior.

Emotionally intelligent people are more likely to have a better understanding of themselves and to make conscious decisions based on emotion and rationale combined. Overall, it leads a person to self-actualization.

The relevance and importance of emotional intelligence in contexts of business leadership, commercial negotiation, and dispute resolution has been recognized, and professional qualifications and continuous professional development have incorporated aspects of understanding emotions and developing greater insight into emotional interactions. Especially in the globalized world, the ability to be a world leader is becoming important. A high EQ allows business leaders to interact with various different cultures, and they must be comfortable in these diverse cultural environments, having diverse teams and organization. EQ has become an essential part of leading an organization.

Interactions with other phenomena

Bullying

Bullying is an abusive social interaction between peers that can include aggression, harassment, and violence. Bullying is typically repetitive and enacted by those who are in a position of power over the victim. A growing body of research illustrates a significant relationship between bullying and emotional intelligence. It also shows that emotional intelligence is a key factor in cybervictimization.

Emotional intelligence (EI) is a set of abilities related to the understanding, use, and management of emotion as it relates to one's self and others. Mayer et al. define EI as: "accurately perceiving emotion, using emotions to facilitate thought, understanding emotion, and managing emotion". The concept combines emotional and intellectual processes. Lower emotional intelligence appears to be related to involvement in bullying, as the bully and/or the victim of bullying. EI seems to play an important role in both bullying behavior and victimization in bullying; given that EI is illustrated to be malleable, EI education could improve bullying prevention and intervention initiatives.

Job performance

A meta-analysis of emotional intelligence and job performance showed correlations of r=.20 (for job performance & ability EI) and r=.29 (for job performance and mixed EI). Earlier research on EI and job performance had shown mixed results: a positive relation has been found in some of the studies, while in others there was no relation or an inconsistent one. This led researchers Cote and Miners to offer a compensatory model between EI and IQ, that posits that the association between EI and job performance becomes more positive as cognitive intelligence decreases, an idea first proposed in the context of academic performance. The results of the former study supported the compensatory model: employees with low IQ get higher task performance and organizational citizenship behavior directed at the organization, the higher their EI. It has also been observed that there is no significant link between emotional intelligence and work attitude-behavior.

Another study suggests that EI is not necessarily a universally positive trait. The study found a negative correlation between EI and managerial work demands; while under low levels of managerial work demands, they found a negative relationship between EI and teamwork effectiveness. An explanation for this may be gender differences in EI, as women tend to score higher levels than men. This furthers the idea that job context plays a role in the relationships between EI, teamwork effectiveness, and job performance.

Another study assessed a possible link between EI and entrepreneurial behaviors and success.

Although studies between emotional intelligence (EI) and job performance have shown mixed results of high and low correlations, EI is an undeniably better predictor than most of the hiring methods commonly used in companies, such as letters of reference or cover letters. By 2008, 147 companies and consulting firms in the U.S. had developed programmes that involved EI for training and hiring employees. Van Rooy and Viswesvaran showed that EI correlated significantly with different domains in performance, ranging from .24 for job performance to .10 for academic performance. Employees high on EI would be more aware of their own emotions and others', which in turn, could lead companies to better profits and less unnecessary expenses. This is especially important for expatriate managers, who have to deal with mixed emotions and feelings, while adapting to a new working culture. Employees high in EI show more confidence in their roles, which allow them to face demanding tasks positively.

According to a science book by the journalist Daniel Goleman, emotional intelligence accounts for more career success than IQ. Other studies argued that employees high on EI perform substantially better than employees low in EI. This is measured by self-reports and different work performance indicators, such as wages, promotions and salary increase. According to Lopes et al. EI contributes to developing strong and positive relationships with co-workers and to performing efficiently in work teams. This benefits performance of workers by providing emotional support and instrumental resources needed to succeed in their roles. Emotionally intelligent employees have better resources to cope with stressing situations and demanding tasks, which enable them to outperform in those situations. For instance, Law et al. found that EI was the best predictor of job performance beyond general cognitive ability among IT scientists in a computer company in China.

When examining the connection between job performance and emotional intelligence, it's essential to take into account the impact of "managing up," which signifies a positive rapport between an employee and their supervisor. Previous research found that the quality of this relationship could influence subjective assessments of job performance. Employees with strong emotional intelligence tend to dedicate more time to cultivating their rapport with supervisors. As a result, those with higher EI are more likely to achieve favorable outcomes in performance evaluations compared to those with lower EI.

Based on theoretical and methodological approaches, EI measures are categorized in three main streams: (1) ability-based measures (e.g. MSCEIT), (2) self-reports of abilities measures (e.g. SREIT, SUEIT and WLEIS), and (3) mixed-models (e.g. AES, ECI, EI questionnaire, EIS, EQ-I and GENOS), which include measures of EI and traditional social skills. O'Boyle Jr. et al. found that the three EI streams together had a positive correlation of 0.28 with job performance. Similarly, each of EI streams independently obtained a positive correlation of 0.24, 0.30, and 0.28, respectively. Streams 2 and 3 showed an incremental validity for predicting job performance over and above personality (Five Factor model) and general cognitive ability. Both streams 2 and 3 were the second most important predictor of job performance, below general cognitive ability. Stream 2 explained 13.6% of the total variance, whereas stream 3 explained 13.2%. In order to examine the reliability of these findings, a publication bias analysis was developed. Results indicated that studies on EI-job performance correlation prior to 2010 do not present substantial evidence to suggest the presence of publication bias. Noting that O'Boyle Jr. et al. had included self-rated performance and academic performance in their meta-analysis, Joseph, Jin, Newman, & O'Boyle collaborated to update the meta-analysis to focus specifically on job performance; using measures of job performance, these authors showed r=.20 (for job performance & ability EI) and r=.29 (for job performance and mixed EI).

The Consortium for Research on Emotional Intelligence in Organizations argues that there is a business case in favor of emotional intelligence but, despite the validity of previous findings, some researchers still question whether EI-job performance correlation makes a real impact on business strategies. Critics argue that the popularity of EI studies is due to media advertising, rather than objective scientific findings. Also, the relationship between job performance and EI is not as strong as suggested. This relationship requires the presence of other constructs to raise important outcomes. For instance, studies found that EI is positively associated with teamwork effectiveness under job contexts of high managerial work demands, which improves job performance. This is due to the activation of strong emotions during the performance on this job context. In this scenario, emotionally intelligent individuals show a better set of resources to succeed in their roles. However, individuals with high EI show a similar level of performance than non-emotionally intelligent employees under different job contexts. Moreover, Joseph and Newman suggest that emotional perception and emotional regulation components of EI highly contribute to job performance under job contexts of high emotional demands. Moon and Hur found that emotional exhaustion ("burn-out") significantly influences the job performance-EI relationship. Emotional exhaustion showed a negative association with two components of EI (optimism and social skills). This association impacted negatively to job performance, as well. Hence, the job performance-EI relationship is stronger under contexts of high emotional exhaustion or burn-out; in other words, employees with high levels of optimism and social skills possess better resources to outperform when facing high emotional exhaustion contexts.

Leadership

Several studies attempt to study the relationship between EI and leadership. Although EI plays a positive role in leadership effectiveness, what makes a leader effective is what he/she does with his/her role, rather than his/her interpersonal skills and abilities. Although in the past a good or effective leader gave orders and controlled the overall performance of the organization, almost everything is different nowadays: leaders are now expected to motivate and create a sense of belonging that makes employees feel comfortable, thus, making them work more effectively.

This does not mean that actions are more important than emotional intelligence. Leaders still need to grow emotionally in order to handle stress, life balance, and other things. A proper way to grow emotionally, for instance, is developing a sense of empathy since empathy is a key factor when it comes to emotional intelligence. In a study conducted to analyze the relationship between school counselors' EI and leadership skills, it was concluded that several participants were good leaders because their emotional intelligence was developed in counselor preparations, where empathy is taught.

Health

A 2007 meta-analysis of 44 effect sizes by Schutte et al. found that emotional intelligence was associated with better mental and physical health. Particularly, trait EI had the stronger association with mental and physical health. This was replicated in 2010 by researcher Alexandra Martins who found trait EI is a strong predictor for health after conducting a meta-analysis based on 105 effect sizes and 19,815 participants. This meta-analysis also indicated that this line of research reached enough sufficiency and stability to conclude EI is a positive predictor for health.

An earlier study by Mayer and Salovey argued that high EI can increase one's well-being because of its role in enhancing relationships.

Self-esteem and drug dependence

A 2012 study in India cross-examined emotional intelligence, self-esteem, and marijuana dependence. Out of a sample of 200, 100 of whom were dependent on cannabis and the other 100 emotionally healthy, the dependent group scored exceptionally low on EI when compared to the control group. They also found that the dependent group also scored low on self-esteem when compared to the control.

Another study in 2010 examined whether or not low levels of EI had a relationship with the degree of drug and alcohol addiction in Australia. In the assessment of 103 residents in a drug rehabilitation center, they examined their EI along with other psychosocial factors in a one-month interval of treatment. They found that participants' EI scores improved as their levels of addiction lessened as part of their treatment.

Academic performance

A 2020 meta-analysis showed that students with higher emotional intelligence show higher academic performance at school. This was a summary of over 1,246 effects from 158 different studies, with a sample size of 42,529. Students with higher emotional intelligence had better scores on standardized tests and achieved higher grades. The effect was significantly larger for humanities than for science/maths areas of study, and significantly larger for ability emotional intelligence (measured with objective tasks), than for rating scales of emotional intelligence. The association of emotional intelligence with higher academic achievement was still significant even after considering the effect of students' Big Five personality and intelligence.

There are three reasons why greater emotional intelligence might predict stronger academic performance. First, emotionally intelligent students are able to regulate their emotions at school—they are able to control their anxiety surrounding tests and assessment, and their boredom when the material is not intrinsically interesting. This means their emotions do not impede their test scores or their ability to learn. Second, emotionally intelligent students are able to build better social relationships with other students and with instructors. This means that they have sources of help when needed—other students and teachers are more willing to help them when they get stuck. Third, some of the abilities of emotional intelligence (understanding emotions, for example) overlap with academic content, particularly in the humanities. That is, analyzing universal themes in literature or the social forces underpinning historic events require a knowledge of human emotions.

Criticisms

EI, and Goleman's original 1995 analysis, have been criticized within the scientific community:

Predictive power

Landy distinguishes between the "commercial" and "academic" discussion of EI, basing this distinction on the alleged predictive power of EI as seen by each of the two. According to Landy, the former makes expansive claims on the applied value of EI, while the latter is trying to warn users against these claims. As an example, Goleman (1998) asserts that "the most effective leaders are alike in one crucial way: they all have a high degree of what has come to be known as emotional intelligence.... emotional intelligence is the sine qua non of leadership." In contrast, Mayer (1999) cautions that "the popular literature's implication—that highly emotionally intelligent people possess an unqualified advantage in life—appears overly enthusiastic at present and unsubstantiated by reasonable scientific standards." Landy further reinforces this argument by noting that the data upon which these claims are based are held in "proprietary databases", which means they are unavailable to independent researchers for reanalysis, replication, or verification.

It is difficult to create objective measures of emotional intelligence and demonstrate its influence on leadership as many scales are self-report measures.

In a 2009 academic exchange, Antonakis and Ashkanasy/Dasborough mostly agreed that researchers who test whether EI matters for leadership have not done so using robust research designs; therefore, currently there is no strong evidence showing that EI predicts leadership outcomes when accounting for personality and IQ. Antonakis argued that EI might not be needed for leadership effectiveness (he referred to this as the "curse of emotion" phenomenon, because leaders who are too sensitive to their and others' emotional states might have difficulty making decisions that would result in emotional labor for the leader or followers). A 2010 meta-analysis seems to support the Antonakis position: it found that, using data free from problems of common source and common methods, EI measures correlated only ρ=0.11 with measures of transformational leadership. Barling, Slater, and Kelloway also support this position on transformational leadership.

Ability-measures of EI fared worst (i.e., ρ=0.04); the WLEIS (Wong-Law measure) did a bit better (ρ=0.08), and the Bar-On measure slightly better (ρ=0.18). However, the validity of these estimates does not include the effects of IQ or the big five personality, which correlate both with EI measures and leadership. A study analyzing the impact of EI on both job performance and leadership found that the meta-analytic validity estimates for EI dropped to zero when Big Five traits and IQ were controlled for. A meta-analysis showed the same result for Ability EI.

Self-reported and Trait EI measures retain a fair amount of predictive validity for job performance after controlling Big Five traits and IQ. However the greater predictive validity of Trait EI measures can be attributed to their inclusion of content related to achievement motivation, self efficacy, and self-rated performance. Meta-analytic evidence confirms that self-reported emotional intelligence predicting job performance is due to mixed-EI and trait-EI measures tapping into self-efficacy and self-rated performance, in addition to the domains of Neuroticism, Extraversion, Conscientiousness, and IQ. As such, the predictive ability of mixed EI to job performance drops to nil when controlling for these factors.

A study of the predictive ability of EI for job performance concluded that higher EI was associated with higher leadership effectiveness regarding achievement of organizational goals. This study shows that EI may serve an identifying tool in understanding who is (or is not) likely to deal effectively with colleagues. Furthermore, one can develop and enhance one's leadership qualities by advancing one's emotional intelligence. EI can be deliberately developed, specifically the facets of "facilitating thinking with emotions" and "monitoring and regulation of emotions" in the workplace.

Correlations with personality

Researchers raised concerns about the extent to which self-report EI measures correlate with established personality dimensions. Self-report EI measures and personality measures converge because they both purport to measure personality traits. Two dimensions of the Big Five stand out as most related to self-report EI: neuroticism and extraversion. Neuroticism relates to negative emotionality and anxiety. People who score high on neuroticism are likely to score low on self-report EI measures.

Studies examined the multivariate effects of personality and intelligence on EI and attempted to correct estimates for measurement error. For example, one study showed that general intelligence (measured with the Wonderlic Personnel Test), agreeableness (measured by the NEO-PI), as well as gender could reliably predict the measure of EI ability. They gave a multiple correlation (R) of .81 with the MSCEIT (perfect prediction would be 1). This result was replicated. The replication found a multiple R of .76 using Cattell's "Culture Fair" intelligence test and the Big Five Inventory (BFI); significant covariates were intelligence (standardized beta = .39), agreeableness (standardized beta = .54), and openness (standardized beta = .46).

A study of the Ability Emotional Intelligence Measure found similar results (Multiple R = .69), with significant predictors being intelligence, standardized beta = .69 (using the Swaps Test and a Wechsler scales subtest, the 40-item General Knowledge Task) and empathy, standardized beta = .26 (using the Questionnaire Measure of Empathic Tendency). Antonakis and Dietz (2011b) also show how including or excluding important controls variables can fundamentally change results.

Interpretations of the correlations between EI questionnaires and personality have been varied, but a prominent view is the Trait EI view, which re-interprets EI as a collection of personality traits.

A 2011 meta-analysis classified EI studies into three streams: "(1) ability‐based models that use objective test items; (2) self‐report or peer‐report measures based on the four‐branch model of EI; and (3) 'mixed models' of emotional competencies." It found that these "three streams have corrected correlations ranging from 0.24 to 0.30 with job performance. The three streams correlated differently with cognitive ability and with neuroticism, extraversion, openness, agreeableness, and conscientiousness. Streams 2 and 3 have the largest incremental validity beyond cognitive ability and the Five Factor Model (FFM)." The meta-analysis concluded that "all three streams of EI exhibited substantial relative importance in the presence of FFM and intelligence when predicting job performance." A follow-up meta-analysis in 2015 further substantiated these findings, and addressed concerns about "the questionable construct validity of mixed EI measures" by arguing that "mixed EI instruments assess a combination of ability EI and self-perceptions, in addition to personality and cognitive ability."

A 2017 meta-analysis of 142 data sources found a very large overlap between the general factor of personality and trait EI. The overlap was so large they concluded that "The findings suggest that the general factor of personality is very similar, perhaps even synonymous, to trait EI." However, the overlap between the general factor of personality and ability EI was more moderate, with a correlation of about 0.28.

In 2021, two review papers examined the relationship between emotional intelligence and the dark triad of personality traits (narcissism, Machiavellianism, and psychopathy). This research found that emotional intelligence showed negative associations with all three dark triad domains of personality. Of the four ability branches of emotional intelligence, the largest effects were for emotion management (versus emotion perception, use, or understanding) and for psychopathy (versus narcissism or Machiavellianism). The two different facets of narcissism showed different relationships with emotional intelligence. Vulnerable narcissism (characterized by anxiety and fragile self-esteem) was associated with lower emotional intelligence. However, grandiose narcissism (characterized by self-confidence, dominance, and an inflated sense of ego) related to higher levels of emotional intelligence. This indicates that not all "dark" personalities lack emotional intelligence.

A 2021 meta-analysis showed that emotional intelligence was positively associated with secure attachment in adults, but negatively associated with insecure attachment styles such as anxious attachment and avoidant attachment. The associations with anxious attachment and avoidant attachment were significant for both ability EI and for rating scales of EI. However, only rating scales of EI showed a significantly positive association with secure attachment. The authors suggest that the early development of attachment styles may facilitate (or hinder) the development of emotional abilities and traits involved in EI.

Socially desirable responding

Socially desirable responding (SDR), or "faking good", is a response pattern in which test-takers systematically represent themselves with an excessive positive bias. This bias has long been known to contaminate responses on personality inventories, acting as a mediator of the relationships between self-report measures.

It has been suggested that responding in a desirable way is a "response set"—a situational and temporary response pattern. This is contrasted with a "response style", which is a more long-term trait-like quality. Considering the contexts in which some self-report EI inventories are used (e.g., employment settings), the problems of response sets in high-stakes scenarios are clear.

There is evidence that people can ‘‘fake good’’ on emotional intelligence tests, resulting in inaccurate measurement with several studies showing people can distort their responses on both self-rated and informant-rated emotional intelligence measures when instructed to.

There are a few methods to prevent socially desirable responding on behavior inventories. Some researchers believe it is necessary to warn test-takers not to fake good before taking a personality test. Some inventories use validity scales in order to determine the likelihood or consistency of the responses across all items.

EI as behavior rather than intelligence

Goleman's early work has been criticized for assuming that EI is a type of intelligence or cognitive ability. Eysenck writes that Goleman's description of EI contains unsubstantiated assumptions about intelligence in general and that it even runs contrary to what researchers have come to expect when studying types of intelligence:

"[Goleman] exemplifies more clearly than most the fundamental absurdity of the tendency to class almost any type of behavior as an 'intelligence'... If these five 'abilities' define 'emotional intelligence', we would expect some evidence that they are highly correlated; Goleman admits that they might be quite uncorrelated, and in any case, if we cannot measure them, how do we know they are related? So the whole theory is built on quicksand: there is no sound scientific basis."

Similarly, Locke claims that the concept of EI is a misinterpretation of the intelligence construct, and he offers an alternative interpretation: it is not another form or type of intelligence, but intelligence—the ability to grasp abstractions—applied to a particular life domain: emotions. He suggests the concept should be re-labeled and referred to as a skill.

The essence of these criticisms is that scientific inquiry depends on valid and consistent construct utilization and that before the introduction of the term EI, psychologists had established theoretical distinctions between factors such as abilities and achievements, skills and habits, attitudes and values, and personality traits and emotional states. Some scholars believe that the term EI merges and conflates such accepted concepts and definitions.

EI as skill rather than moral quality

Adam Grant warned of the common but mistaken perception of EI as a desirable moral quality rather than a skill. Grant asserted that a well-developed EI is not only an instrumental tool for accomplishing goals, but can function as a weapon for manipulating others by robbing them of their capacity to reason.

EI as a measure of conformity

Tom Reed describes four stages of emotional intelligence: self-awareness, social consciousness, self-care and relationship management, as part of NAVAIR's "Mentoring at the Speed of Life" event

One criticism of the works of Mayer and Salovey comes from a study that suggests that the EI, as measured by the MSCEIT, may only be measuring conformity. This argument is rooted in the MSCEIT's use of consensus-based assessment, and in the fact that scores on the MSCEIT are negatively distributed (meaning that its scores differentiate between people with low EI better than people with high EI).

EI as a form of knowledge

Another criticism says that in contrast with tests of cognitive ability, the MSCEIT "tests knowledge of emotions but not necessarily the ability to perform tasks that are related to the knowledge that is assessed". If someone knows how they should behave in an emotionally laden situation, it does not necessarily follow that they could actually carry out the reported behavior.

NICHD pushes for consensus

The National Institute of Child Health and Human Development recognized that because there are divisions about the topic of EI, the mental health community needs to agree on some guidelines to describe good mental health and positive mental living conditions. In their section, "Positive Psychology and the Concept of Health", they explain: "Currently there are six competing models of positive health, which are based on concepts such as being above normal, character strengths and core virtues, developmental maturity, social-emotional intelligence, subjective well-being, and resilience. But these concepts define health in philosophical rather than empirical terms. Dr. [Lawrence] Becker suggested the need for a consensus on the concept of positive psychological health...".

Wednesday, September 4, 2024

Neuroscience and intelligence

Neuroscience and intelligence refers to the various neurological factors that are partly responsible for the variation of intelligence within species or between different species. A large amount of research in this area has been focused on the neural basis of human intelligence. Historic approaches to studying the neuroscience of intelligence consisted of correlating external head parameters, for example head circumference, to intelligence. Post-mortem measures of brain weight and brain volume have also been used. More recent methodologies focus on examining correlates of intelligence within the living brain using techniques such as magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), positron emission tomography and other non-invasive measures of brain structure and activity.

Researchers have been able to identify correlates of intelligence within the brain and its functioning. These include overall brain volume, grey matter volume, white matter volume, white matter integrity, cortical thickness and neural efficiency.

Analyses of the parameters of intellectual systems, patterns of their emergence and evolution, distinctive features, and the constants and limits of their structures and functions made it possible to measure and compare the capacity of communications (~100 m/s), to quantify the number of components in intellectual systems (~1011 neurons), and to calculate the number of successful links responsible for cooperation (~1014 synapses).

Although the evidence base for our understanding of the neural basis of human intelligence has increased greatly over the past 30 years, even more research is needed to fully understand it.

The neural basis of intelligence has also been examined in animals such as primates, cetaceans, and rodents.

Humans

Brain volume

One of the main methods used to establish a relationship between intelligence and the brain is to use measures of brain volume. The earliest attempts at estimating brain volume were done using measures of external head parameters, such as head circumference as a proxy for brain size. More recent methodologies employed to study this relationship include post-mortem measures of brain weight and volume. These have their own limitations and strengths. The advent of MRI as a non-invasive highly-accurate measure of living brain structure and function (using fMRI) made this the pre-dominant and preferred method for measuring brain volume.

Overall, larger brain size and volume is associated with better cognitive functioning and higher intelligence. The specific regions that show the most robust correlation between volume and intelligence are the frontal, temporal and parietal lobes of the brain. A large number of studies have been conducted with uniformly positive correlations, leading to the generally safe conclusion that larger brains predict greater intelligence. In healthy adults, the correlation of total brain volume and IQ is approximately 0.4 when high-quality tests are used. A large scale study (n = 29k) using the UK Biobank found a correlation of .275. The strength of this relationship did not depend on sex, contradicting some earlier studies. A study using a sibling-design in two medium sized samples found evidence of causality with an effect size of 0.19. This study design rules out confounders that vary between families, but not those that vary within families.

Less is known about variation on scales less than total brain volume. A meta-analytic review by McDaniel found that the correlation between intelligence and in vivo brain size was larger for females (0.40) than for males (0.25). The same study also found that the correlation between brain size and Intelligence increased with age, with children showing smaller correlations. It has been suggested that the link between larger brain volumes and higher intelligence is related to variation in specific brain regions: a whole-brain measure would under-estimate these links. For functions more specific than general intelligence, regional effects may be more important. For instance evidence suggests that in adolescents learning new words, vocabulary growth is associated with gray matter density in bilateral posterior supramarginal gyri. Small studies have shown transient changes in gray-matter associated with developing a new physical skill (juggling) occipito-temporal cortex 

Brain volume is not a perfect account of intelligence: the relationship explains a modest amount of variance in intelligence – 12% to 36% of the variance. The amount of variance explained by brain volume may also depend on the type of intelligence measured. Up to 36% of variance in verbal intelligence can be explained by brain volume, while only approximately 10% of variance in visuospatial intelligence can be explained by brain volume. A 2015 study by researcher Stuart J. Ritchie found that brain size explained 12% of the variance in intelligence among individuals. These caveats imply that there are other major factors influencing how intelligent an individual is apart from brain size. In a large meta-analysis consisting of 88 studies Pietschnig et al. (2015) estimated the correlation between brain volume and intelligence to be about correlation coefficient of 0.24 which equates to 6% variance. Taking into account measurement quality, and sample type and IQ-range, the meta-analytic association of brain volume in appears to be ~ .4 in normal adults. Researcher Jakob Pietschnig argued that the strength of the positive association of brain volume and IQ remains robust, but has been overestimated in the literature. He has stated that "It is tempting to interpret this association in the context of human cognitive evolution and species differences in brain size and cognitive ability, we show that it is not warranted to interpret brain size as an isomorphic proxy of human intelligence differences".

Grey matter

Grey matter has been examined as a potential biological foundation for differences in intelligence. Similarly to brain volume, global grey matter volume is positively associated with intelligence. More specifically, higher intelligence has been associated with larger cortical grey matter in the prefrontal and posterior temporal cortex in adults. Furthermore, both verbal and nonverbal intelligence have been shown to be positively correlated with grey matter volume across the parietal, temporal and occipital lobes in young healthy adults, implying that intelligence is associated with a wide variety of structures within the brain.

There appear to be sex differences between the relationship of grey matter to intelligence between men and women. Men appear to show more intelligence to grey matter correlations in the frontal and parietal lobes, while the strongest correlations between intelligence and grey matter in women can be found in the frontal lobes and Broca's area. However, these differences do not seem to impact overall Intelligence, implying that the same cognitive ability levels can be attained in different ways.

One specific methodology used to study grey matter correlates of intelligence in areas of the brain is known as voxel-based morphometry (VBM). VBM allows researchers to specify areas of interest with great spatial resolution, allowing the examination of grey matter areas correlated with intelligence with greater special resolution. VBM has been used to correlate grey matter positively with intelligence in the frontal, temporal, parietal, and occipital lobes in healthy adults. VBM has also been used to show that grey matter volume in the medial region of the prefrontal cortex and the dorsomedial prefrontal cortex correlate positively with intelligence in a group of 55 healthy adults. VBM has also been successfully used to establish a positive correlation between grey matter volumes in the anterior cingulate and intelligence in children aged 5 to 18 years old.

Grey matter has also been shown to positively correlate with intelligence in children. Reis and colleagues have found that grey matter in the prefrontal cortex contributes most robustly to variance in Intelligence in children between 5 and 17, while subcortical grey matter is related to intelligence to a lesser extent. Frangou and colleagues examined the relationship between grey matter and intelligence in children and young adults aged between 12 and 21, and found that grey matter in the orbitofrontal cortex, cingulate gyrus, cerebellum and thalamus was positively correlated to intelligence, while grey matter in the caudate nucleus is negatively correlated with intelligence. However, the relationship between grey matter volume and intelligence only develops over time, as no significant positive relationship can be found between grey matter volume and intelligence in children under 11.

An underlying caveat to research into the relationship of grey matter volume and intelligence is demonstrated by the hypothesis of neural efficiency. The findings that more intelligent individuals are more efficient at using their neurons might indicate that the correlation of grey matter to intelligence reflects selective elimination of unused synapses, and thus a better brain circuitry.

White matter

Similar to grey matter, white matter has been shown to correlate positively with intelligence in humans. White matter consists mainly of myelinated neuronal axons, responsible for delivering signals between neurons. The pinkish-white color of white matter is actually a result of these myelin sheaths that electrically insulate neurons that are transmitting signals to other neurons. White matter connects different regions of grey matter in the cerebrum together. These interconnections make transport more seamless and allow us to perform tasks easier. Significant correlations between intelligence and the corpus callosum have been found, as larger callosal areas have been positively correlated with cognitive performance. However, there appear to be differences in importance for white matter between verbal and nonverbal intelligence, as although both verbal and nonverbal measures of intelligence correlate positively with the size of the corpus callosum, the correlation for intelligence and corpus callosum size was larger (.47) for nonverbal measures than that for verbal measures (.18). Anatomical mesh-based geometrical modelling has also shown positive correlations between the thickness of the corpus callosum and Intelligence in healthy adults.

White matter integrity has also been found to be related to intelligence. White matter tract integrity is important for information processing speed, and therefore reduced white matter integrity is related to lower intelligence. The effect of white matter integrity is mediate entirely through information processing speed. These findings indicate that the brain is structurally interconnected and that axonal fibres are integrally important for fast information process, and thus general intelligence.

Contradicting the findings described above, VBM failed to find a relationship between the corpus callosum and intelligence in healthy adults. This contradiction can be viewed to signify that the relationship between white matter volume and intelligence is not as robust as that of grey matter and intelligence.

Cortical thickness

Cortical thickness has also been found to correlate positively with intelligence in humans. However, the rate of growth of cortical thickness is also related to intelligence. In early childhood, cortical thickness displays a negative correlation with intelligence, while by late childhood this correlation has shifted to a positive one. More intelligent children were found to develop cortical thickness more steadily and over longer periods of time than less bright children. Studies have found cortical thickness to explain 5% in the variance of intelligence among individuals. In a study conducted to find associations between cortical thickness and general intelligence between different groups of people, sex did not play a role in intelligence. Although it is hard to pin intelligence on age based on cortical thickness due to different socioeconomic circumstances and education levels, older subjects (17 - 24) tended to have less variances in terms of intelligence than when compared to younger subjects (19 - 17).

Cortical convolution

Cortical convolution has increased the folding of the brain’s surface over the course of human evolution. It has been hypothesized that the high degree of cortical convolution may be a neurological substrate that supports some of the human brain's most distinctive cognitive abilities. Consequently, individual intelligence within the human species might be modulated by the degree of cortical convolution.

An analysis published in 2019 found the contours of 677 children and adolescent (mean age 12.72 years) brains had a genetic correlation of almost 1 between IQ and surface area of the supramarginal gyrus on the left side of the brain.

Neural efficiency

The neural efficiency hypothesis postulates that more intelligent individuals display less activation in the brain during cognitive tasks, as measured by Glucose metabolism. A small sample of participants (N=8) displayed negative correlations between intelligence and absolute regional metabolic rates ranging from -0.48 to -0.84, as measured by PET scans, indicating that brighter individuals were more effective processors of information, as they use less energy. According to an extensive review by Neubauer & Fink a large number of studies (N=27) have confirmed this finding using methods such as PET scans, EEG and fMRI.

fMRI and EEG studies have revealed that task difficulty is an important factor affecting neural efficiency. More intelligent individuals display neural efficiency only when faced with tasks of subjectively easy to moderate difficulty, while no neural efficiency can be found during difficult tasks. In fact, more able individuals appear to invest more cortical resources in tasks of high difficulty. This appears to be especially true for the Prefrontal Cortex, as individuals with higher intelligence displayed increased activation of this area during difficult tasks compared to individuals with lower intelligence. It has been proposed that the main reason for the neural efficiency phenomenon could be that individuals with high intelligence are better at blocking out interfering information than individuals with low intelligence.

Further research

Some scientists prefer to look at more qualitative variables to relate to the size of measurable regions of known function, for example relating the size of the primary visual cortex to its corresponding functions, that of visual performance.

In a study of the head growth of 633 term-born children from the Avon Longitudinal Study of Parents and Children cohort, it was shown that prenatal growth and growth during infancy were associated with subsequent IQ. The study’s conclusion was that the brain volume a child achieves by the age of 1 year helps determine later intelligence. Growth in brain volume after infancy may not compensate for poorer earlier growth.

There is an association between IQ and myopia. One suggested explanation is that one or several pleiotropic gene(s) affect the size of the neocortex part of the brain and eyes simultaneously.

Parieto-frontal integration theory

In 2007, Behavioral and Brain Sciences published a target article that put forth a biological model of intelligence based on 37 peer-reviewed neuroimaging studies (Jung & Haier, 2007). Their review of a wealth of data from functional imaging (functional magnetic resonance imaging and positron emission tomography) and structural imaging (diffusion MRI, voxel-based morphometry, in vivo magnetic resonance spectroscopy) argues that human intelligence arises from a distributed and integrated neural network comprising brain regions in the frontal and parietal lobes.

A recent lesion mapping study conducted by Barbey and colleagues provides evidence to support the P-FIT theory of intelligence.

Brain injuries at an early age isolated to one side of the brain typically results in relatively spared intellectual function and with IQ in the normal range.

Primates

Brain size

Another theory of brain size in vertebrates is that it may relate to social rather than mechanical skills. Cortical size relates directly to pair-bonding lifestyle and among primates, cerebral cortex size varies directly with the demands of living in a large complex social network. Compared to other mammals, primates have significantly larger brain sizes. Additionally, most primates are found to be polygynandrous, having many social relationships with others. Although inconclusive, some studies have shown that this polygynandrous statue correlates to brain size.

Intelligence in chimpanzees has been found to be related to brain size, grey matter volume, and cortical thickness, as in humans.

Health

Several environmental factors related to health can lead to significant cognitive impairment, particularly if they occur during pregnancy and childhood when the brain is growing and the blood–brain barrier is less effective. Developed nations have implemented several health policies regarding nutrients and toxins known to influence cognitive function. These include laws requiring fortification of certain food products and laws establishing safe levels of pollutants (e.g. lead, mercury, and organochlorides). Comprehensive policy recommendations targeting reduction of cognitive impairment in children have been proposed.

Heritability of IQ

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Heritability_of_IQ

Research on the heritability of IQ inquires into the degree of variation in IQ within a population that is due to genetic variation between individuals in that population. There has been significant controversy in the academic community about the heritability of IQ since research on the issue began in the late nineteenth century. Intelligence in the normal range is a polygenic trait, meaning that it is influenced by more than one gene, and in the case of intelligence at least 500 genes. Further, explaining the similarity in IQ of closely related persons requires careful study because environmental factors may be correlated with genetic factors.

Early twin studies of adult individuals have found a heritability of IQ between 57% and 73%, with some recent studies showing heritability for IQ as high as 80%. IQ goes from being weakly correlated with genetics for children, to being strongly correlated with genetics for late teens and adults. The heritability of IQ increases with the child's age and reaches a plateau at 14–16 years old, continuing at that level well into adulthood. However, poor prenatal environment, malnutrition and disease are known to have lifelong deleterious effects.

Although IQ differences between individuals have been shown to have a large hereditary component, it does not follow that disparities in IQ between groups have a genetic basis. The scientific consensus is that genetics does not explain average differences in IQ test performance between racial groups.

Heritability and caveats

Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of variation in a phenotypic trait in a population that is due to genetic variation between individuals in that population. The concept of heritability can be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is not explained by the environment or random chance?"

Estimates of heritability take values ranging from 0 to 1; a heritability estimate of 1 indicates that all variation in the trait in question is genetic in origin and a heritability estimate of 0 indicates that none of the variation is genetic. The determination of many traits can be considered primarily genetic under similar environmental backgrounds. For example, a 2006 study found that adult height has a heritability estimated at 0.80 when looking only at the height variation within families where the environment should be very similar. Other traits have lower heritability estimates, which indicate a relatively larger environmental influence. For example, a twin study on the heritability of depression in men estimated it as 0.29, while it was 0.42 for women in the same study.

Caveats

There are a number of points to consider when interpreting heritability:

  • Heritability measures the proportion of variation in a trait that can be attributed to genes, and not the proportion of a trait caused by genes. Thus, if the environment relevant to a given trait changes in a way that affects all members of the population equally, the mean value of the trait will change without any change in its heritability (because the variation or differences among individuals in the population will stay the same). This has evidently happened for height: the heritability of stature is high, but average heights continue to increase. Thus, even in developed nations, a high heritability of a trait does not necessarily mean that average group differences are due to genes. Some have gone further, and used height as an example in order to argue that "even highly heritable traits can be strongly manipulated by the environment, so heritability has little if anything to do with controllability."
  • A common error is to assume that a heritability figure is necessarily unchangeable. The value of heritability can change if the impact of environment (or of genes) in the population is substantially altered. If the environmental variation encountered by different individuals increases, then the heritability figure would decrease. On the other hand, if everyone had the same environment, then heritability would be 100%. The population in developing nations often has more diverse environments than in developed nations. This would mean that heritability figures would be lower in developing nations. Another example is phenylketonuria which previously caused mental retardation for everyone who had this genetic disorder and thus had a heritability of 100%. Today, this can be prevented by following a modified diet, resulting in a lowered heritability.
  • A high heritability of a trait does not mean that environmental effects such as learning are not involved. Vocabulary size, for example, is very substantially heritable (and highly correlated with general intelligence) although every word in an individual's vocabulary is learned. In a society in which plenty of words are available in everyone's environment, especially for individuals who are motivated to seek them out, the number of words that individuals actually learn depends to a considerable extent on their genetic predispositions and thus heritability is high.
  • Since heritability increases during childhood and adolescence, and even increases greatly between 16 and 20 years of age and adulthood, one should be cautious drawing conclusions regarding the role of genetics and environment from studies where the participants are not followed until they are adults. Furthermore, there may be differences regarding the effects on the g-factor and on non-g factors, with g possibly being harder to affect and environmental interventions disproportionately affecting non-g factors.
  • Polygenic traits often appear less heritable at the extremes. A heritable trait is definitionally more likely to appear in the offspring of two parents high in that trait than in the offspring of two randomly selected parents. However, the more extreme the expression of the trait in the parents, the less likely the child is to display the same extreme as the parents. At the same time, the more extreme the expression of the trait in the parents, the more likely the child is to express the trait at all. For example, the child of two extremely tall parents is likely to be taller than the average person (displaying the trait), but unlikely to be taller than the two parents (displaying the trait at the same extreme). See also regression toward the mean.

Estimates

Various studies have estimated the heritability of IQ to be between 0.7 and 0.8 in adults and 0.45 in childhood in the United States. It has been found that estimates of heritability increase as individuals age. Heritability estimates in infancy are as low as 0.2, around 0.4 in middle childhood, and as high as 0.8 in adulthood. The brain undergoes morphological changes in development which suggests that age-related physical changes could contribute to this effect.

A 1994 article in Behavior Genetics based on a study of Swedish monozygotic and dizygotic twins found the heritability of the sample to be as high as 0.80 in general cognitive ability; however, it also varies by trait, with 0.60 for verbal tests, 0.50 for spatial and speed-of-processing tests, and 0.40 for memory tests. In contrast, studies of other populations estimate an average heritability of 0.50 for general cognitive ability.

In 2006, David Kirp, writing in The New York Times Magazine, summarized a century's worth of research as follows, "about three-quarters of I.Q. differences between individuals are attributable to heredity."

Shared family environment

There are some family effects on the IQ of children, accounting for up to a quarter of the variance. However, adoption studies show that by adulthood adoptive siblings aren't more similar in IQ than strangers, while adult full siblings show an IQ correlation of 0.24. However, some studies of twins reared apart (e.g. Bouchard, 1990) find a significant shared environmental influence, of at least 10% going into late adulthood. Judith Rich Harris suggests that this might be due to biasing assumptions in the methodology of the classical twin and adoption studies.

There are aspects of environments that family members have in common (for example, characteristics of the home). This shared family environment accounts for 0.25-0.35 of the variation in IQ in childhood. By late adolescence it is quite low (zero in some studies). There is a similar effect for several other psychological traits. These studies have not looked into the effects of extreme environments such as in abusive families.

The American Psychological Association's report "Intelligence: Knowns and Unknowns" (1996) asserts the necessity of a certain minimum level of responsible care for normal child development. Environments that are severely deprived, neglectful, or abusive negatively affect various developmental aspects, including intellectual growth. Beyond this minimum threshold, the influence of family experience on child development is contentious. Variables such as home resources and parents' use of language are correlated with children's IQ scores; however, these correlations may be influenced by genetic as well as environmental factors. The extent to which variance in IQ results from differences between families, compared to the varying experiences of different children within the same family, is a subject of debate. Recent twin and adoption studies indicate that the effect of the shared family environment is significant in early childhood but diminishes substantially by late adolescence. These findings suggest that differences in family lifestyles, while potentially important for many aspects of children's lives, have little long-term impact on the skills measured by intelligence tests.

Non-shared family environment and environment outside the family

Although parents treat their children differently, such differential treatment explains only a small amount of non-shared environmental influence. One suggestion is that children react differently to the same environment due to different genes. More likely influences may be the impact of peers and other experiences outside the family. For example, siblings grown up in the same household may have different friends and teachers and even contract different illnesses. This factor may be one of the reasons why IQ score correlations between siblings decreases as they get older.

Malnutrition and diseases

Certain single-gene metabolic disorders can severely affect intelligence. Phenylketonuria is an example, with publications documenting the capacity of treated phenylketonuria to produce a reduction of 10 IQ points on average. Meta-analyses have found that environmental factors, such as iodine deficiency, can result in large reductions in average IQ; iodine deficiency has been shown to produce a reduction of 12.5 IQ points on average.

Heritability and socioeconomic status

The APA report "Intelligence: Knowns and Unknowns" (1996) also stated that:

"We should note, however, that low-income and non-white families are poorly represented in existing adoption studies as well as in most twin samples. Thus it is not yet clear whether these studies apply to the population as a whole. It remains possible that, across the full range of income and ethnicity, between-family differences have more lasting consequences for psychometric intelligence."

A study (1999) by Capron and Duyme of French children adopted between the ages of four and six examined the influence of socioeconomic status (SES). The children's IQs initially averaged 77, putting them near retardation. Most were abused or neglected as infants, then shunted from one foster home or institution to the next. Nine years later after adoption, when they were on average 14 years old, they retook the IQ tests, and all of them did better. The amount they improved was directly related to the adopting family's socioeconomic status. "Children adopted by farmers and laborers had average IQ scores of 85.5; those placed with middle-class families had average scores of 92. The average IQ scores of youngsters placed in well-to-do homes climbed more than 20 points, to 98."

Stoolmiller (1999) argued that the range of environments in previous adoption studies was restricted. Adopting families tend to be more similar on, for example, socio-economic status than the general population, which suggests a possible underestimation of the role of the shared family environment in previous studies. Corrections for range restriction to adoption studies indicated that socio-economic status could account for as much as 50% of the variance in IQ.

On the other hand, the effect of this was examined by Matt McGue and colleagues (2007), who wrote that "restriction in range in parent disinhibitory psychopathology and family socio-economic status had no effect on adoptive-sibling correlations [in] IQ"

Turkheimer and colleagues (2003) argued that the proportions of IQ variance attributable to genes and environment vary with socioeconomic status. They found that in a study on seven-year-old twins, in impoverished families, 60% of the variance in early childhood IQ was accounted for by the shared family environment, and the contribution of genes is close to zero; in affluent families, the result is almost exactly the reverse.

In contrast to Turkheimer (2003), a study by Nagoshi and Johnson (2005) concluded that the heritability of IQ did not vary as a function of parental socioeconomic status in the 949 families of Caucasian and 400 families of Japanese ancestry who took part in the Hawaii Family Study of Cognition.

Asbury and colleagues (2005) studied the effect of environmental risk factors on verbal and non-verbal ability in a nationally representative sample of 4-year-old British twins. There was not any statistically significant interaction for non-verbal ability, but the heritability of verbal ability was found to be higher in low-SES and high-risk environments.

Harden, Turkheimer, and Loehlin (2007) investigated adolescents, most 17 years old, and found that, among higher income families, genetic influences accounted for approximately 55% of the variance in cognitive aptitude and shared environmental influences about 35%. Among lower income families, the proportions were in the reverse direction, 39% genetic and 45% shared environment."

In the course of a substantial review, Rushton and Jensen (2010) criticized the study of Capron and Duyme, arguing their choice of IQ test and selection of child and adolescent subjects were a poor choice because this gives a relatively less hereditable measure. The argument here rests on a strong form of Spearman's hypothesis, that the hereditability of different kinds of IQ test can vary according to how closely they correlate to the general intelligence factor (g); both the empirical data and statistical methodology bearing on this question are matters of active controversy.

A 2011 study by Tucker-Drob and colleagues reported that at age 2, genes accounted for approximately 50% of the variation in mental ability for children being raised in high socioeconomic status families, but genes accounted for negligible variation in mental ability for children being raised in low socioeconomic status families. This gene–environment interaction was not apparent at age 10 months, suggesting that the effect emerges over the course of early development.

A 2012 study based on a representative sample of twins from the United Kingdom, with longitudinal data on IQ from age two to age fourteen, did not find evidence for lower heritability in low-SES families. However, the study indicated that the effects of shared family environment on IQ were generally greater in low-SES families than in high-SES families, resulting in greater variance in IQ in low-SES families. The authors noted that previous research had produced inconsistent results on whether or not SES moderates the heritability of IQ. They suggested three explanations for the inconsistency. First, some studies may have lacked statistical power to detect interactions. Second, the age range investigated has varied between studies. Third, the effect of SES may vary in different demographics and different countries.

A 2017 King's College London study suggests that genes account for nearly 50 per cent of the differences between whether children are socially mobile or not.

Maternal (fetal) environment

A meta-analysis by Devlin and colleagues (1997) of 212 previous studies evaluated an alternative model for environmental influence and found that it fits the data better than the 'family-environments' model commonly used. The shared maternal (fetal) environment effects, often assumed to be negligible, account for 20% of covariance between twins and 5% between siblings, and the effects of genes are correspondingly reduced, with two measures of heritability being less than 50%. They argue that the shared maternal environment may explain the striking correlation between the IQs of twins, especially those of adult twins that were reared apart. IQ heritability increases during early childhood, but whether it stabilizes thereafter remains unclear. These results have two implications: a new model may be required regarding the influence of genes and environment on cognitive function; and interventions aimed at improving the prenatal environment could lead to a significant boost in the population's IQ.

Bouchard and McGue reviewed the literature in 2003, arguing that Devlin's conclusions about the magnitude of heritability is not substantially different from previous reports and that their conclusions regarding prenatal effects stands in contradiction to many previous reports. They write that:

Chipuer et al. and Loehlin conclude that the postnatal rather than the prenatal environment is most important. The Devlin et al. (1997a) conclusion that the prenatal environment contributes to twin IQ similarity is especially remarkable given the existence of an extensive empirical literature on prenatal effects. Price (1950), in a comprehensive review published over 50 years ago, argued that almost all MZ twin prenatal effects produced differences rather than similarities. As of 1950 the literature on the topic was so large that the entire bibliography was not published. It was finally published in 1978 with an additional 260 references. At that time Price reiterated his earlier conclusion (Price, 1978). Research subsequent to the 1978 review largely reinforces Price's hypothesis (Bryan, 1993; Macdonald et al., 1993; Hall and Lopez-Rangel, 1996; see also Martin et al., 1997, box 2; Machin, 1996).

Dickens and Flynn model

Dickens and Flynn (2001) argued that the "heritability" figure includes both a direct effect of the genotype on IQ and also indirect effects where the genotype changes the environment, in turn affecting IQ. That is, those with a higher IQ tend to seek out stimulating environments that further increase IQ. The direct effect can initially have been very small but feedback loops can create large differences in IQ. In their model an environmental stimulus can have a very large effect on IQ, even in adults, but this effect also decays over time unless the stimulus continues. This model could be adapted to include possible factors, like nutrition in early childhood, that may cause permanent effects.

The Flynn effect is the increase in average intelligence test scores by about 0.3% annually, resulting in the average person today scoring 15 points higher in IQ compared to the generation 50 years ago. This effect can be explained by a generally more stimulating environment for all people. The authors suggest that programs aiming to increase IQ would be most likely to produce long-term IQ gains if they taught children how to replicate outside the program the kinds of cognitively demanding experiences that produce IQ gains while they are in the program and motivate them to persist in that replication long after they have left the program.[57][59] Most of the improvements have allowed for better abstract reasoning, spatial relations, and comprehension. Some scientists have suggested that such enhancements are due to better nutrition, better parenting and schooling, as well as exclusion of the least intelligent people from reproduction. However, Flynn and a group of other scientists share the viewpoint that modern life implies solving many abstract problems which leads to a rise in their IQ scores.

Influence of genes on IQ stability

Recent research has illuminated genetic factors underlying IQ stability and change. Genome-wide association studies have demonstrated that the genes involved in intelligence remain fairly stable over time. Specifically, in terms of IQ stability, "genetic factors mediated phenotypic stability throughout this entire period [age 0 to 16], whereas most age-to-age instability appeared to be due to non-shared environmental influences". These findings have been replicated extensively and observed in the United Kingdom, the United States, and the Netherlands. Additionally, researchers have shown that naturalistic changes in IQ occur in individuals at variable times.

Influence of parents genes that are not inherited

Kong reports that, "Nurture has a genetic component, i.e. alleles in the parents affect the parents' phenotypes and through that influence the outcomes of the child." These results were obtained through a meta-analysis of educational attainment and polygenic scores of non-transmitted alleles. Although the study deals with educational attainment and not IQ, these two are strongly linked.

Spatial ability component of IQ

Spatial ability has been shown to be unifactorial (a single score accounts well for all spatial abilities), and is 69% heritable in a sample of 1,367 pairs of twins from the ages 19 through 21. Further only 8% of spatial ability can be accounted for by shared environmental factors like school and family. Of the genetically determined portion of spatial ability, 24% is shared with verbal ability (general intelligence) and 43% was specific to spatial ability alone.

Molecular genetic investigations

A 2009 review article identified over 50 genetic polymorphisms that have been reported to be associated with cognitive ability in various studies, but noted that the discovery of small effect sizes and lack of replication have characterized this research so far. Another study attempted to replicate 12 reported associations between specific genetic variants and general cognitive ability in three large datasets, but found that only one of the genotypes was significantly associated with general intelligence in one of the samples, a result expected by chance alone. The authors concluded that most reported genetic associations with general intelligence are probably false positives brought about by inadequate sample sizes. Arguing that common genetic variants explain much of the variation in general intelligence, they suggested that the effects of individual variants are so small that very large samples are required to reliably detect them. Genetic diversity within individuals is heavily correlated with IQ.

A novel molecular genetic method for estimating heritability calculates the overall genetic similarity (as indexed by the cumulative effects of all genotyped single nucleotide polymorphisms) between all pairs of individuals in a sample of unrelated individuals and then correlates this genetic similarity with phenotypic similarity across all the pairs. A study using this method estimated that the lower bounds for the narrow-sense heritability of crystallized and fluid intelligence are 40% and 51%, respectively. A replication study in an independent sample confirmed these results, reporting a heritability estimate of 47%. These findings are compatible with the view that a large number of genes, each with only a small effect, contribute to differences in intelligence.

Correlations between IQ and degree of genetic relatedness

The relative influence of genetics and environment for a trait can be calculated by measuring how strongly traits covary in people of a given genetic (unrelated, siblings, fraternal twins, or identical twins) and environmental (reared in the same family or not) relationship. One method is to consider identical twins reared apart, with any similarities that exist between such twin pairs attributed to genotype. In terms of correlation statistics, this means that theoretically the correlation of tests scores between monozygotic twins would be 1.00 if genetics alone accounted for variation in IQ scores; likewise, siblings and dizygotic twins share on average half alleles and the correlation of their scores would be 0.50 if IQ were affected by genes alone (or greater if there is a positive correlation between the IQs of spouses in the parental generation). Practically, however, the upper bound of these correlations are given by the reliability of the test, which is 0.90 to 0.95 for typical IQ tests.

If there is biological inheritance of IQ, then the relatives of a person with a high IQ should exhibit a comparably high IQ with a much higher probability than the general population. In 1982, Bouchard and McGue reviewed such correlations reported in 111 original studies in the United States. The mean correlation of IQ scores between monozygotic twins was 0.86, between siblings 0.47, between half-siblings 0.31, and between cousins 0.15.

The 2006 edition of Assessing adolescent and adult intelligence by Alan S. Kaufman and Elizabeth O. Lichtenberger reports correlations of 0.86 for identical twins raised together compared to 0.76 for those raised apart and 0.47 for siblings. These numbers are not necessarily static. When comparing pre-1963 to late 1970s data, researchers DeFries and Plomin found that the IQ correlation between parent and child living together fell significantly, from 0.50 to 0.35. The opposite occurred for fraternal twins.

Every one of these studies presented next contains estimates of only two of the three factors which are relevant. The three factors are G, E, and GxE. Since there is no possibility of studying equal environments in a manner comparable to using identical twins for equal genetics, the GxE factor can not be isolated. Thus the estimates are actually of G+GxE and E. Although this may seem like nonsense, it is justified by the unstated assumption that GxE=0. It is also the case that the values shown below are r correlations and not r(squared), proportions of variance. Numbers less than one are smaller when squared. The next to last number in the list below refers to less than 5% shared variance between a parent and child living apart.

Another summary:

  • Same person (tested twice over time) .85 or above
  • Identical twins—Reared together .86
  • Identical twins—Reared apart .76
  • Fraternal twins—Reared together .55
  • Fraternal twins—Reared apart .35
  • Biological siblings—Reared together .47
  • Biological siblings—Reared apart .24
  • Biological siblings—Reared together—Adults .24
  • Unrelated children—Reared together—Children .28
  • Unrelated children—Reared together—Adults .04
  • Cousins .15
  • Parent-child—Living together .42
  • Parent-child—Living apart .22
  • Adoptive parent–child—Living together .19

Between-group heritability

In the US, individuals identifying themselves as Asian generally tend to score higher on IQ tests than Caucasians, who tend to score higher than Hispanics, who tend to score higher than African Americans. Yet, although IQ differences between individuals have been shown to have a large hereditary component, it does not follow that between-group differences in average IQ have a genetic basis. In fact, greater variation in IQ scores exists within each ethnic group than between them. The scientific consensus is that genetics does not explain average differences in IQ test performance between racial groups. Growing evidence indicates that environmental factors, not genetic ones, explain the racial IQ gap.

Arguments in support of a genetic explanation of racial differences in average IQ are sometimes fallacious. For instance, some hereditarians have cited as evidence the failure of known environmental factors to account for such differences, or the high heritability of intelligence within races. Jensen and Rushton, in their formulation of Spearman's Hypothesis, argued that cognitive tasks that have the highest g-load are the tasks in which the gap between black and white test takers is greatest, and that this supports their view that racial IQ gaps are in large part genetic. However, in separate reviews, Mackintosh, Nisbett et al. and Flynn have all concluded that the slight correlation between g-loading and the test score gap offers no clue to the cause of the gap. Further reviews of both adoption studies and racial admixture studies have also found no evidence for a genetic component behind group-level IQ differences. Hereditarian arguments for racial differences in IQ have been criticized from a theoretical point of view as well. For example, the geneticist and neuroscientist Kevin Mitchell has argued that "systematic genetic differences in intelligence between large, ancient populations" are "inherently and deeply implausible" because the "constant churn of genetic variation works against any long-term rise or fall in intelligence." As he argues, "To end up with systematic genetic differences in intelligence between large, ancient populations, the selective forces driving those differences would need to have been enormous. What's more, those forces would have to have acted across entire continents, with wildly different environments, and have been persistent over tens of thousands of years of tremendous cultural change."

In favor of an environmental explanation, on the other hand, numerous studies and reviews have shown promising results. Among these, some focus on the gradual closing of the black–white IQ gap over the last decades of the 20th century, as black test-takers increased their average scores relative to white test-takers. For instance, Vincent reported in 1991 that the black–white IQ gap was decreasing among children, but that it was remaining constant among adults. Similarly, a 2006 study by Dickens and Flynn estimated that the difference between mean scores of black people and white people closed by about 5 or 6 IQ points between 1972 and 2002, a reduction of about one-third. In the same period, the educational achievement disparity also diminished. Reviews by Flynn and Dickens, Mackintosh, and Nisbett et al. all accept the gradual closing of the gap as a fact. Other recent studies have focused on disparities in nutrition and prenatal care, as well as other health-related environmental disparities, and have found that these disparities may account for significant IQ gaps between population groups. Still other studies have focused on educational disparities, and have found that intensive early childhood education and test preparation can diminish or eliminate the black–white IQ test gap. In light of these and similar findings, a consensus has formed that genetics does not explain differences in average IQ test performance between racial groups.

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