PSYCHOLOGY TEACHERS UPDATE
(Incorporating Psychology Information for Students)
New Series No.16 - May 2018
Intelligence in the 21st century
KEVIN BREWER
ISSN: 1478-4548
Orsett Psychological Services PO Box 179 Grays Essex RM16 3EW UK [email protected]
PSYCHOLOGY TEACHERS UPDATE
Psychology Teachers Update is designed to give a brief overview of the main developments in the different areas of psychology. There is a proliferation of journals and research, and it is very difficult to keep abreast of the latest trends, particularly in the many and varied areas of psychology.
Each issue of Psychology Teachers Update will cover a particular areas, and summarise some of the research and findings in the last ten to fifteen years approximately. The aim is to give teachers the feel of what is happening in that area of psychology.
Psychology Teachers Update will appear twice a year in May, and November.
AUTHOR
Kevin Brewer
Kevin is an experienced teacher of A level psychology since the 1980s. He has taught and examined with many of the different exam boards. He is a social psychology tutor with the Open University.
Author of three books published by Heinemann: "Psychology and Crime" (2000) and "Clinical Psychology" (2001) as sole author, and "Heinemann Psychology AS for AQA A" (2003) by David Moxon, Kevin Brewer, and Peter Emmerson. He is also one of the authors of the Nelson Thorne book, "AQA A2 Psychology B" (Billingham et al 2008). Kevin has published other material himself.
A complete list and writings are available at http://psychologywritings.synthasite.com/ . More freely available material can be found at http://www.archive.org/ .
Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 2
CONTENTS
Page Numbers
1. Introduction 4 1.1. Brain size 1.2. Evolutionary perspective
2. Conceptualisations of intelligence 9 2.1. Biological bases 2.2. Creativity
3. Intelligence testing 15 3.1. History and issues 3.2. Flynn effect 3.3. Improving intelligence
4. Why do individuals differ in intelligence? 22 4.1. Environmental enrichment
5. Group differences in intelligence 28 5.1. Gender differences 5.2. Ethnic differences 5.3. Regional/Socio-economic differences
6. Appendices 33 A - Alternatives to intelligence B - Criticisms of psychometrics and individual differences C - Cognitive enhancement D - Race science
7. References 40
Past Issues 46
Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 3
1. INTRODUCTION
Reviewing the state of knowledge for the American Psychological Association in 1994, Neisser et al (1996) began: "Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: A given person's intellectual performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of 'intelligence' are attempts to clarify and organise this complex set of phenomena. Although considerable clarity has been achieved in some areas, no such conceptualisation has yet answered all the important questions and none commands universal assent" (p77) 1. Over twenty years have passed since this statement, but much of its sentiment still holds. There is debate still about the conceptualisation of "intelligence", and related issues, which Neisser et al (1996) covered under the headings:
Concepts of intelligence Intelligence tests Genes and environment (ie: why do individuals differ in intelligence?) Group differences (eg: gender).
More recently, Wang et al (2014) summarised the future of intelligence research as "delineating what intelligence is and what is not, carefully measuring related cognitive constructs, considering gene- environment interactions, taking advantage of big data, and integrating across different levels of analysis from neural (and even molecular) up to social and environmental" (pp16-17).
"Big data" is attractive to researchers generally as well as specifically to intelligence studies as it can include whole countries, but Wang et al (2014) raised concerns about the quality of the data (eg: measures of intelligence used; confounding factors). With so much data the value placed on statistics like "percent of variance explained" can produce "number absolutism" (Pan et al 2012 quoted in Wang et al 2014).
1 Hunt and Jaeggi (2013) commented that "[A]ny discussion of the big issues in the study of intelligence has to face the fact that the study of intelligence, like the study of psychology itself, is simultaneously a biological and a social science" (p37). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 4
At the same time, neuroscience data (eg: from brain imaging) can be "over-valued". "It is important that intelligence researchers who use neuroscience data communicate with care and, in particular, avoid overselling claims especially in the media. Unfortunately, 'neuromyths' based on misinterpretations of neuroscience data may influence the public's decisions. Individuals who believe, for example, that they are right-brained and therefore cannot learn left- brained tasks, or who believe that because there is a neural basis to ADHD or a learning disability and therefore no possibility of improvement, may not be motivated to achieve academically or put forth the effort needed to learn certain cognitive skills. On the other hand, individuals may positively and inappropriately respond to under supported marketing claims for energy drinks or brain-training interventions developed by 'neuroscientists'" (Wang et al 2014 p18).
In this "era of rapid advances in technological capacity", Johnson (2013) drew this analogy: "we may be too much like a six-year-old eager to drive a car: unable to see over the dashboard or reach the brakes, limited in use of peripheral vision, and lacking the prefrontal cortex development to maintain the necessary focus of attention and impulse control. In other words, we lack the necessary conceptual foundations to make use of all our new technological tools wisely" (p26). She continued: "Intelligence is a construct. That is, it is an idea we hold in our minds about a driver of observable behaviour rather than a tangible, fungible property we can observe directly. It is also a complex idea, about a property of our minds that contributes not only to our behaviour on a day-to-day basis, but also, somehow, to our potential for future behaviour" (Johnson 2013 p26). The upshot is the difficulty in establishing an agreed definition, and the problems with its predictive validity. Both of these issues are also linked to whether intelligence is a fixed capacity (Johnson 2013).
Dweck (eg: 2009) viewed this differently, and she argued that it is the belief in intelligence as "fixed" or "incremental" that matters. People who believe in the latter, that intelligence "improves with effort and application, tend to achieve more, persist longer in learning new skills, and be more willing to take on new learning challenges than do people who believe their intelligence is fixed" (Johnson 2013 p30) 2.
2 A number of characteristics have been proposed as more important than IQ for educational success (appendix A). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 5
De Boeck (2013) summarised the responses of twelve experts 3 to two questions:
1. "What are the most important scientific issues in the domain of human intelligence?"
2. "What are the most promising new ideas and approaches in the study of human intelligence?"
The responses were categorised as:
Agreement - Understanding the biological basis of intelligence (question 1), using neuroimaging and related technology (question 2), and improving the measurement of intelligence (question 1).
No divergence - Responses made by one or two individuals on top of the above points, including cognitive enhancement, use of big data, and artificial intelligence (AI).
Divergence - Defining intelligence (as well as whether to include emotional intelligence, say), how to study differences between individuals (eg: longitudinal or cross-sectional method), and the genetic basis to intelligence.
1.1. BRAIN SIZE
Humans are viewed as more intelligence than other species, but what is the brain basis to this? Here are three possibilities:
1. Absolute and relative brain size.
Bigger animals have bigger brains because of their body size. For example, the brain of some whales weighs 10 kilograms, compared to 1300 grams in humans (Koch 2016). This is absolute brain size, but it is better to use relative brain size (ie: brain size relative to overall body size). The brain-to-body size of humans is about 2% compared to less than 0.1% for whales, but for the shrew it is 10% (Koch 2016). Among humans themselves, there is variability in brain volume. The average for men is 1274 cm³ and 1131 cm³ for women, but this can vary between 1053 to 1499 cm³ and 975 to 1398 cm³ respectively (Koch 2016). Brain volume, however, weakly correlates with intelligence (IQ
3 Editorial Board of the "Journal of Intelligence". Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 6
test scores) at 0.3 - 0.4 (ie: brain size accounts for 9- 16% of variability in general intelligence) (Koch 2016).
2. Encephalisation quotient (EQ).
The ratio of the mass of the brain of one species relative to its taxonomic group. Humans have an EQ of 7.5 for mammals (ie: the brain is 7.5 times bigger than the brain of a mammal weighing the same amount), compared to less than 5 for apes and monkeys (Koch 2016).
3. Neocortex neurons.
The EQ makes sense if it represents the cellular constituents of the brain, and particularly more neurons in the neocortex (irrelevant of the size of the brain). However, a study of ten long-finned pilot whales found twice as many neocortical neurons as humans (Mortensen et al 2014).
Gerschwind and Levitsky (1968) were among the first to look for unique cortical features of the human brain "that might explain the cognitive success of the human species" (Leroy et al 2015). Hemispheric asymmetry and language processing are unique brain aspects. Based on neuroimaging scans of 177 human and 73 chimpanzee brains, more recently, Leroy et al (2015) distinguished a human-specific asymmetry in the superior temporal sulcus (STS) (divides part of the temporal lobe; figure A), which they called the "superior temporal asymmetrical pit" (STAP). "Because STS asymmetry is barely visible in chimpanzees and likely is absent in macaques, the presence of the STAP is interpreted as a recent evolutionary change. Furthermore, that this asymmetry is present in infants and even foetuses suggests an early genetically driven mechanism and stimulates the search for genes of recent evolution expressed differently in the superior temporal region during mid-gestation. Although observed in all human groups, the magnitude of the STAP asymmetry is modulated by sex, perhaps because male brains are larger than female brains" (Leroy et al 2015 p1212).
Koch (2016) summed up: "People forever ask for the single thing that distinguishes humans from all other animals, on the supposition that this one magical property would explain our evolutionary success [...] it's not brain size, relative brain size or absolute number of neurons that distinguishes us. Perhaps our wiring has become more streamlined, our metabolism more efficient, our synapses more sophisticated" (p25).
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(Source: Henry Gray (1918) "Anatomy of the Human Body"; in public domain)
Figure A - Location of STS (red line).
There may be limitations to how intelligent humans could become because of physical restraints. For example, increasing brain size has a physical limit within the skull, as well as becoming more energy-demanding, while better "wiring" of neurons "would hit thermodynamic limitations similar to those that affect transistors in computer chips" (Fox 2015 p106). Table A summarises possible improvements to the brain and their limitations (Fox 2015).
IMPROVEMENT LIMITATION Increase brain size with more Increased energy demands of more neurons neurons; longer "wiring" leading to slower processing Increase interconnectedness of Energy costs existing neurons Increase signalling speed between Energy cost and more space needed neurons with thicker axons (ie: brain becomes bigger) Shrink neurons in order to pack Smaller neurons have noisier more into existing brain size signalling (eg: risk of random firing)
Table A - 4 possible improvements to the brain and intelligence.
1.2. EVOLUTIONARY PERSPECTIVE
From an evolutionary perspective, intelligence is not a universal trait, and, in fact, most species survive without it and the related abilities, like advanced learning skills and innovativeness. Part of the reason is
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the cost of large brains (Reader 2004).
In a breeding experiment with fruit flies, it was shown that higher intelligence is not always better. Mery and Kawecki (2003) selectively bred twenty generations with better learning abilities, but the larvae of these flies were poorer at competing for limited food than controls (Reader 2004). Intelligence and learning are most beneficial in a changing environment that requires adaptability (eg: "ecological intelligence" theories - bigger brains help to remember where and when patchily available food can be found) (Reader 2004). Reader (2004), working with guppy fish, has found that the most innovative are hungry, small and uncompetitive individuals. So, Reader (2004) concluded that "the story of the evolution of human creative intelligence is perhaps not one of successful individuals innovating to do still better, but rather one of losers innovating to do less badly" (p37).
2. CONCEPTUALISATIONS OF INTELLIGENCE
Defining intelligence is not easy, and there are many conceptualisations/theories about its nature 4. The dominant view is the psychometric approach 5, which concentrates on intelligence as measured by "IQ" (intelligence quotient) tests. For convenience, intelligence is often defined as a general cognitive ability (or "g"; Spearman 1927), which can be ascertained by factor analysis of scores on a range of tests (eg: logic, vocabulary, picture completion) (Toga and Thompson 2005). However, "g" as a single, unitary measure of intelligence has been and is hotly debated (eg: classic texts - Jensen (1969) (for) vs Gould (1981) (against)).
Certain theories have rejected this approach, including Gardner's (1983) "multiple intelligences" 6, and
4 Sternberg et al (2005) pointed out that two major symposia - one in the 1920s and one in the mid- 1980s - produced over twenty definitions of intelligence each time that were mostly different. "There were some common threads, such as the importance of adaptation to the environment and of the ability to learn, but these constructs themselves are not well specified" (Sternberg et al 2005 p46). 5 This approach is not without its critics both in relation to intelligence and personality (appendix B). 6 Gardner widens the definition of "intelligence" to include reasoning ability, musical ability, and agility of movement, for example. Hunt and Jaeggi (2013) commented: "By using the term intelligence in the way that he did, Gardner called educators’ attention to the fact that there are many traits outside of the cognitive realm that are important in a person’s adjustment to society, and therefore these traits ought to be developed by the educational system. We simultaneously applaud Gardner’s attempt to broaden the scope of education, and at the same time argue that the profligate combination of desirable Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 9
Sternberg's (1985) "triarchic theory". Such theories are interested in intelligence as more than or different to that measured by psychometric tests. Other distinctions include between academic and practical intelligence (eg: practical mathematics of Brazilian street traders with no schooling), or cultural variations in the understanding of intelligence (Neisser et al 1996).
Biological theories have grown in popularity, and these look for physical brain differences to describe variations in intelligence. Correlations have been proposed between intelligence and measures of total brain volume (eg: McDaniel 2005), and different aspects of intelligence and measures of volume, surface area and density in certain brain areas (eg: Colom et al 2006), for instance (Hunt and Jaeggi 2013).
From another point of view, Cattell (1971) distinguished between crystallised intelligence "related to learned or culturally determined skills and knowledge", and fluid intelligence involving "'here-and- now' thinking" (Shayer et al 2007).
2.1. BIOLOGICAL BASES
The debate on "g" versus multiple abilities/intelligencies has been addressed by neuroimaging in recent years. "For example, if one homogeneous system supports all intelligence processes, then a common network of brain regions should be recruited whenever difficulty increases across all cognitive tasks, regardless of the exact stimulus, response, or cognitive process that is manipulated. Conversely, if intelligence is supported by multiple specialised systems, anatomically distinct brain networks should be recruited when tasks that load on distinct intelligence factors are undertaken" (Hampshire et al 2012 p1225). Hampshire et al (2012) got sixteen healthy young participants to perform twelve different cognitive tasks
traits into the term intelligence will not advance the scientific study of individual differences in cognition" (p41). Overall, Hunt and Jaeggi (2013) argued for "a new definition of intelligence" that "ought to expand the concept beyond the traits traditionally included on tests of cognition, but retain the restriction that the traits involved are cognitive ones" (p41). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 10
(covering planning, reasoning, attention, and working memory) in a MRI (magnetic resonance imaging) scanner. The activity in different parts of the cortex in response to the cognitive tasks led the researchers to the view that "human intelligence is not unitary but, rather, is formed from multiple cognitive components... [and] that human intelligence is most parsimoniously conceived of as an emergent property of multiple specialised brain systems, each of which has its own capacity" (Hampshire et al 2012 p1233).
Jung and Haier (2007) proposed the parieto-frontal integration theory (P-FIT) to explain the fourteen brain areas they found active in intelligence tests. "Both the amount of grey matter in certain P-FIT areas and the rate of information flow among these areas are likely to play key roles in intelligence" (Haier 2009 p31) 7.
Finn et al (2015) reported that "functional connectivity profiles" could be used as "an identifying fingerprint". The connectivity profile is the pattern of brain connections (ie: 268 nodes) established by using structural and functional magnetic resonance imaging (MRI). One hundred and twenty-six volunteers from the Human Connectome Project were each scanned six times. It was found that "a functional connectivity profile obtained from one session can be used to uniquely identify a given individual from the set of profiles obtained from the second session" (Finn et al 2015 p1664) (ie: 93% success). Furthermore, the researchers said: "although changes in brain state may modulate connectivity patterns to some degree, an individual's underlying intrinsic functional architecture is reliable enough across sessions and distinct enough from that of other individuals to identify him or her from the group regardless of how the brain is engaged during imaging" (Finn et al 2015 p1664). Finn et al (2015) also reported that differences in fronto-parietal networks in the brain during MRI predicted differences in fluid intelligence 8. "Nodes in these networks tend to act as flexible hubs, switching connectivity patterns according to task demands. Additionally, broadly distributed across-network connectivity has been reported in these regions, suggesting a role in large-scale coordination of brain activity" (Finn et al 2015 pp1669-1670).
7 Higher IQ scores have been found to correlate with different brain areas in men (posterior areas) and women (frontal areas) (Haier 2009). 8 Finn et al (2015) defined fluid intelligence as "the capacity for on-the-spot reasoning to discern patterns and solve problems independently of acquired knowledge" (p1668). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 11
Structural brain mapping attempts to produce an anatomical map of the brain showing the function of each area, and the genes involved. Eventually, it should be possible to answer the following questions: " What is the degree of individual variability in anatomy, and how do these differences link with cognitive measures? What are the sources of these variations, and to what degree are they influenced by genes and environment?" (Toga and Thompson 2005 p6). Thompson et al (2001), for example, compared the brains of ten identical-twin pairs and ten non-identical- twin pairs from Finland, and found a high correlation between twins for volume of certain brain areas (suggesting high heritability). On the other hand, Sullivan et al's (2001) study of elderly male twins found little similarity in hippocampus volume, suggesting that "environmental differences, perhaps interacting with genetic differences, may exert especially strong or prolonged influences on hippocampal size, consistent with its lifelong plasticity and fundamental role in learning and memory" (Toga and Thompson 2005 p10). The next step is to find "the specific genes whose variations are linked with brain structure and function" (Toga and Thompson 2005 p11). An early study listed seventy candidate genes (Morley and Montgomery 2001).
Toga and Thompson (2005) finished their review of studies of the genetics of brain structure and intelligence thus: "Nature is not democratic. Individuals' IQs vary, but the data presented in this review and elsewhere do not lead us to conclude that our intelligence is dictated solely by genes. Instead genetic interactions with the environment suggest that enriched environments will help everyone achieve their potential, but not to equality. Our potential seems largely predetermined" (p17).
2.2. CREATIVITY
Creativity is a concept that is not easy to define, nor its relationship to intelligence. Pankhurst's (1999) definition is commonly used: "the ability or quality displayed when solving hitherto unsolved problems, when developing novel solutions to problems others have solved differently, or when developing original and novel (at least to the originator) products" (quoted in Batey and Furnham 2006). Batey and Furnham (2006), however, noted problems with what constitutes a product, and "whether new must mean unique or pre-eminent" (or new for the creator). Creativity has several components, including
Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 12
cognitive ability, personality factors (eg: "creative personality"), cognitive style, motivation, knowledge, and the environment as a stimulus (Batey and Furnham 2006).
The relationship between creativity and intelligence varies depending on how the two concepts are defined and measured. For example, creativity seen as divergent thinking (DT) and measured, say, by the number of different users for an object like a brick, has a small correlation with IQ test scores (Batey and Furnham 2006). Guilford (1981) described a non-linear relationship with a high correlation between an IQ score of less than 120 and creativity, and a low correlation between a higher IQ score than 120 and creativity. "Guilford explained this discrepancy on the basis of the levels of convergent and divergent thinking that may be used by problem solvers. Problem solvers with IQs less than 120 may make great use of their DT skills to reach an acceptable answer, whereas problem solvers with IQs greater than 120 may rely on their strong convergent thinking skills to reach an acceptable answer" (Batey and Furnham 2006 p371). Using Cattell's (1971) distinction of fluid and crystallised intelligence, where the latter relates to acquired knowledge and the former to reasoning ability, say, Batey and Furnham (2006) summarised the studies thus. Both types of intelligence correlate highly with "scientific creativity" but low with "everyday creativity", while crystallised intelligence is more important with "artistic creativity".
In terms of physiology, Beaty et al (2018) found a pattern of brain connections in the frontal and parietal cortices related to high-creative thinking ability using functional magnetic resonance imaging (fMRI). Three brain systems were involved:
Default mode network - resting brain system. Executive control network - areas involved in decision- making. Salience network - detects relevant stimuli.
One hundred and sixty-three US participants engaged in a creative thinking task (eg: think of novel use for familiar object) while in the scanner. It was found that "individual differences in creative thinking ability can be reliably predicted in novel participants based on the strength of functional connectivity within task-related brain networks" (Beaty et al 2018 p1089). Subsequently, 93 Austrian participants and 405 from China were tested, and it was possible to predict levels of individual creativity from the strength of brain activity.
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Beaty et al (2018) concluded: "Creativity remains a complex construct that will require considerable further research to uncover its many manifestations in the brain. Unlike some aspects of cognition that have been reliably localised to specific brain regions, complex constructs such as creativity are likely a product of similarly complex neural mechanisms that engage the whole brain... Network-based approaches are particularly well-suited to address such questions because they can accommodate the complex interplay of multiple neurocognitive processes (eg: memory retrieval, mental simulation, and cognitive control)" (pp1090-1091).
Focusing on particular biochemical activity in the brain, Jung et al (2009) found differences in one neurotransmitter in creativity tests between high and average intelligence individuals. Average intelligent individuals showed lower levels of N-acetyl-aspartate in the anterior cingulate cortex, while individuals with IQs above 120 showed high levels (Geddes 2009).
The personality trait of openness to experience is "absolutely essential to creativity for Kaufman and Gregoire (2016), who distinguished three major elements of openness:
Intellectual engagement - eg: enjoying problem-solving, search for truth. Affective engagement - eg: a preference for gut feelings over logic in decision-making. Aesthetic engagement - eg: a fascination with fantasy.
Kaufman (2013) argued that openness is more important in creativity in the arts and sciences than high IQ. The engagements associated with openness are motivated by dopamine, which is the "neuromodulator of exploration" (DeYoung 2013). Zabelina et al (2015) added "leaky sensory filtering" (or sensory hypersensitivity) (ie: the brain "does not efficiently filter out irrelevant information from the environment"; Kaufman and Gregoire 2016). This means that creative individuals are more easily distracted by, say, a ticking clock. Kaufman and Gregoire (2016) explained: "Sensory hypersensitivity most likely contributes to creativity by widening the brain's scope of attention and allowing individuals to take note of more subtleties in their environment. Taking in a greater volume of information increases your chance of making new and unusual connections between distantly related pieces of information" (p66).
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Rosenkrantz et al (2017) recently showed that a supposedly creativity-enhancing placebo could actually increase creativity in an experiment with ninety volunteers in Israel. All participants sniffed a particular odour before performing creativity tasks, but half of the participants were told that "the odorant increases creativity and reduces inhibitions". One of three measures of creativity was the commonly used alternate uses test (AUT), where participants had to list as many possible uses in each case for ten minutes for five common items (eg: pin, sheet, button). The placebo group produced more ideas, and more original ones (ie: not chosen by others) ("out-of-the- boxness"). The researchers tried to explain the findings with two possibilities:
i) The suggestion about the placebo enhancing creativity increased the participants' intrinsic motivation, which is closely linked to creativity.
ii) The suggestion about the placebo reducing inhibitions was able to "weaken inhibitory mechanisms that normally impair performance" (Rosenkrantz et al 2017).
3. INTELLIGENCE TESTING
A lot of time has and does go into establishing the psychometric characteristics of tests (eg: reliability, validity), particularly with the aim of such tests to be predictors of school and job performance 9. This is important for the confirmation of meritocracy. Put simply, intelligent individuals become the best paid etc in a society. However, the socio- economic status (SES) of one's parents is as strong a predictor of the SES of the individual as IQ test score. Neisser et al (1996) summarised the view in 1994: "intelligence test scores predict a wide range of social outcomes with varying degrees of success. Correlations are highest for school achievement, where they account for about a quarter of the variance. They are somewhat lower for job performance, and very low for negatively valued outcomes such as criminality. In general, intelligence tests measure only some of the many personal characteristics that are relevant to life in contemporary America. Those characteristics are never the
9 Johnson (2013) argued that "however hard we try to measure.. intelligence, we always only measure task performance" (p30). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 15
only influence on outcomes, though in the case of school performance they may well be the strongest" (p83). This, like much related to intelligence, is contested, however.
Recent research has concentrated on correlating IQ test scores with cognitive capabilities (eg: reaction time) or neurological measures (eg: visual evoked potentials).
3.1. HISTORY AND ISSUES
The traditional intelligence tests today are the Stanford-Binet and Wechsler scales. The Stanford-Binet scale has its origins in France with Alfred Binet at the turn of the twentieth century. It was based on the assumption of a "mental age" (ie: normal level) to which children of different ages could be compared. This basis was taken up at Stanford University, USA, in 1916, to become the Stanford-Binet scale with the concept of intelligence quotient (IQ) (ratio between mental age and chronological age). It has subsequently been developed with different norms (Anastasi 1988). The Wechsler scales have been developed for adults as well as children. The first scale appeared in 1939. Modern versions have sub-scales which measure specific abilities (eg: vocabulary, digit span, arithmetic) as well as general intelligence (Anastasi 1988).
Anastasi (1988) discussed the issues related to intelligence and testing:
i) Stability of intelligence - This is assessed by correlating scores at different ages in childhood or adulthood. Whether intelligence is stable or not has implications in, for example, predicting educational success from early IQ scores, or the benefits of intervention to improve IQ. There is also a debate about whether IQ declines in later life.
ii) Population changes in IQ over time - Comparisons are often made of populations across time - for instance, an eleven year-olds taking the same test today as compared to eleven year-olds in 1950.
iii) Cultural and ethnic comparisons - Giving the same IQ test to different ethnic groups within a society or to individuals in different cultures. This has proved controversial because "no single test can be universally applicable or equally 'fair' to all cultures" (Anastasi 1988 p357), among other reasons.
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iv) How an IQ score relates to intelligence.
Hunt and Jaeggi (2013) referred to the "criterion problem" - ie: "the problem of defining the situations in which intelligence is partially causal to behaviour" (p42). This is seen most often in the correlation of IQ score with a future behaviour. For example, Deary et al (2007) correlated general intelligence (g) at age eleven years with national examination performance at age sixteen years in Britain as 0.81 overall. But the correlation did vary for g and individual academic subjects between 0.43 and 0.77 (Hunt and Jaeggi 2013) 10 . Hunt and Jaeggi (2013) commented that the "academic field is a particularly easy one in which to conduct studies such as this because bundles of skills are reasonably closely associated with academic areas. The problem is more challenging, but the payoff may be greater in studies of the workforce" (p43).
Whitaker (2012) questioned the appropriateness of the Wechsler tests for measuring low IQ (ie: below 70; sometimes called "learning disability" or "intellectual retardation") including:
Some instructions too complex for low IQ individuals;
The scoring method artificially elevates low IQ (ie: "floor effect");
Very low IQ is not normally distributed, whereas the tests assume a normal distribution of intelligence;
A very small sample of individuals with very low IQ used by test designers for standardisation.
Stanovich (2009) used the term "dysrationalia" to describe "the inability to think and behave rationally despite having adequate intelligence", and to draw attention to cognition that intelligence tests fail to assess. The characteristics of "dysrationalia" include (Stanovich 2009):
Lack of fully disjunctive reasoning (ie: not considering all possibilities).
10 Haworth et al's (2010) study of MZ twins calculated that genes explained differences between individuals more as the individual ages. Concentrating on "g", genes accounted for about 41% of variation in childhood, but 66% by young adulthood (Coghlan 2009). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 17
"My side" bias (ie: "the tendency to reason from an egocentric bias"). For example, Stanovich and West (2008) gave US participants the same logical problem about car crashes, but varied the make of car as German or American, and found difference responses depending on the car's place of manufacture.
Difficulty with probabilistic thinking and thinking scientifically.
3.2. FLYNN EFFECT
Flynn (1984) found that IQ data over the 20th century showed an average increase of about three points per decade (or 0.3 points per year) 11 . This became known as the "Flynn effect". Explanations for it tend to focus on improvements in performance on specific parts of IQ tests. It is not vocabulary, arithmetic and knowledge, but the abstract reasoning parts. For example, finding abstract similarities between two words, or between two abstract patterns (Folger 2012). The "Flynn effect" does not reflect "an increase in our raw brainpower", but it shows "how modern our minds have become" (Folger 2012 p32). Flynn argued that the industrial revolution and mass education began the changes along with improved childhood nutrition, smaller families, and, more recently, technological developments (Folger 2012).
But looking at data from the 19th century onwards, Flynn (1998) became sceptical that intelligence was inevitably improving generation by generation. "He wondered whether the concept of 'intelligence' was still viable. Certainly, people's test-taking ability has improved; but after discounting as minor factors such as nutrition, urbanisation and TV, and pointing out that children's gains on arithmetic reasoning, vocabulary, creativity and speed of learning were far less, he was tempted to suggest that school and environmental stimulus in some undefined way were improving children's decontextualized problem-solving skills" (Shayer et al 2007 p28). There is evidence that gains in test scores have levelled off since the 1980s (eg: in the UK and Scandinavian countries) (Shayer et al 2007). The problem is finding standardised tests that have been used over long periods of time. Shayer et al (2007)
11 Flynn (1987) observed gains of nine points per generation for tests of crystallised intelligence and fifteen points for fluid intelligence since the 1940s. Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 18
referred to the "Science Reasoning Test II Volume and Heaviness" used in 1975-6 with eleven year-olds in the UK. Based on the ideas of Jean Piaget, it measures understanding of concepts like displacement volume and weight. Comparing 1976 and 2003 data, there was a drop in scores (Shayer et al 2007).
In reference to the Flynn effect and ethnic differences (particularly in the USA), Dickens and Flynn (2006) noted that: "It is often asserted that blacks have made no IQ gains on whites, despite relative environmental gains, and that this adds credibility to the case that the black/white IQ gap has genetic origins" (p913). These researchers showed that this was not the case with evidence from four major IQ tests that had a gain of 5-6 IQ points for Blacks compared to Whites between 1972 and 2002 in the USA. Dickens and Flynn (2006) were able to do this research as the major IQ tests (Wechsler and Stanford-Binet) had included Blacks in standardisation samples for their tests from the 1970s, but not necessarily prior to that. Dickens and Flynn (2006) ended their article: "The constancy of the black/white IQ gap is a myth. Blacks have gained 5 or 6 IQ points on whites over the last 30 years. Neither changes in the ancestry of those classified as black nor changes in those who identify as black can explain more than a small fraction of this gain. Therefore, environment has been responsible. The last two decades have seen both positive and negative developments: gains in occupational status and school funding have been accompanied by more black pre-schoolers in single-parent homes and lower income in those homes... We believe that further black environmental progress would engender further black IQ gains" (p917). Rushton and Jensen (2005) had stated that the IQ difference between Black and White Americans had not changed in one hundred years. Dickens and Flynn (2006) were critical of this assertion: "No one really knows the history of the black/white gap. Estimates for 1917 and 1943 are based on military data subject to a host of biases. Estimates since 1945 are based almost entirely on averaging studies, none of which compare nationally representative samples taking the same test administered at two different times" (p913).
3.3. IMPROVING INTELLIGENCE
Jensen (1969) was adamant that "when all is said and done, there is not much one can do to raise people's IQs" (Sternberg 2008 p6791). This has proved not to be completely true.
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The "Flynn effect" suggests that intelligence improves over time, but it is not quite as inevitable as that. Deary (2012) reported a high stability in IQ scores among individuals tested in their teens and in their retirement years. Hunt and Jaeggi (2013) stated that "such correlations show that there is substantial stability within a population cohort but that there is also a large amount of intra-individual variability. The glass is half full and half empty" (p45). So, in relation to the concept of "improving intelligence", they said: "we are not talking about whether or not cognitive abilities are malleable, because we know that they are. The issue is about what sorts of interventions have the effect of increasing intelligence, over and above those we would expect from the normal processes of ageing, better provisions of artefacts to support cognition over one's lifetime, and the accumulation of experience. There is an informative analogy between procedures intended to improve physical prowess and those intended to produce cognitive prowess. Both are sought after avidly. Some segments of contemporary societies seem to be on a near frantic quest for flat abs and sharp minds. The questions we should ask about both these quests are 'what classes of interventions are considered?', 'how general are the results obtained?' 'for whom do those interventions work best' and 'how long do these results last?'" (Hunt and Jaeggi 2013 p45).
The most common techniques to improve intelligence are education, mental exercises that improve cognitive abilities, and drugs and nutritional supplements. The latter developed from the findings that long term nutritional deficiencies in childhood leads to lower IQ scores. But the causal relationship may not be turned around - ie: better nutrition will improve intelligence. Speaking about it generally, Hunt and Jaeggi (2013) pointed out that it is "a difficult field to study, in no small part because of the conflicts between advocates who believe passionately that the benefits of many of the nutritional and pharmaceutical agents have already been 'proven' and a more conservative view that treats with extreme scepticism any claim that an agent increases human intelligence (or any other trait) without evidence for a mechanism producing the effect. In addition, there are normal scientific issues concerning the reliability of findings and identification of appropriate target populations, and the distinction between short term (acute) and long term (chronic) effects. It is not inconceivable that an agent could have beneficial short term effects, eg: on attention or memory consolidation, and long term deleterious effects through interference with neurotransmitter reception mechanisms. There are
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also substantial social issues involving differential access to cognitive enhancers (appendix C) or destroyers (eg: alcohol, cocaine) by different segments of the population" (p46).
The idea of mental exercises to improve cognition is linked to the "disuse hypothesis", which explains the decline in cognitive abilities in later life to limited use of these abilities (Hunt and Jaeggi 2013) 12 .
Specifically, Jaeggi et al (2008) showed that fluid intelligence is improvable with training. Seventy healthy young adults at a Swiss university took part in four experiments. The training task was the dual n-back task, which involves squares of letters presented swiftly at different locations on a computer screen and a target letter is spoken into headphones. The difficulty can be varied by the timing of the spoken target letter - ie: when the squares are presented, or one screen after and so on. Working memory is being tested. Training led to significant improvements over nineteen days in the n-back task, and to improvements in a similar task (ie: transfer of learning). Sternberg (2008) made some evaluation points about these experiments:
a) One type of working memory task, "so it is unclear to what extent the results can be generalised to other working-memory tasks. It would be important to show that the results are really about working memory rather than some peculiarity of the particular training task" (Sternberg 2008 p6791).
b) One type of transfer task used, so generalisability is relevant again.
c) The "study does not address whether the training is durable over extended periods of time. Too often increases of intelligence obtained through training programs have proved to be fleeting" (Sternberg 2008
12 Using data on over 1500 individuals born in England between 1920 and 1943, Martyn et al (1996) found no association between impaired foetal growth and cognitive decline in later life. The researchers concluded that "foetal growth is less important than genetic factors and environmental influences in post-natal life in determining adult cognitive performances. Adaptations made by the foetus in response to conditions that retard its growth seem to be largely successful in maintaining its brain development" (Martyn et al 1996 p1395). Foetal exposure to the anti-epileptic drug, valproate, however, significantly negatively correlated with IQ at three years old (r = -0.38), but not with the drugs, carbamazepine, lamotrigine, and phenytoin. The mean IQ at 3 years old after exposure to the former was 92 compared to 98-101 for the other drugs (Meador et al 2009). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 21
p6792).
d) The control group had no training, which could produce a placebo effect in the training group. It would be better to have a control group who received training on an irrelevant task.
e) The sample. The "effects need to be examined on a much wider range of ability levels and, in general, of types of participants than were tested in this study. The subjects were all recruited from the University of Bern community, which is likely to be a rather selective sample of individuals not typical of the population either of Switzerland or, more generally, of developed countries or certainly the world. It would be particularly important to test elderly people, who are at risk for loss of fluid ability. The sample was also relatively small" (Sternberg 2008 p6792).
Despite these points, Sternberg (2008) ended: "None of these criticisms detracts from the central importance of the results of Jaeggi et al's study" (p6792).
4. WHY DO INDIVIDUALS DIFFER IN INTELLIGENCE?
The simple either/or debate of nature/nurture or genes/environment is not seen as relevant today. It is accepted that both are involved, though that has not stopped arguments about how much each is involved. Twin and family studies are still viewed as important in establishing "heritability". Neisser et al (1996) pointed out importantly that a "common error is to assume that because something is heritable it is necessarily unchangeable. This is wrong. Heritability does not imply immutability" (p86).
Evidence that intelligence can be changed comes from different types of studies, including (Howe 1998):
Compensatory early education that stimulates children from deprived backgrounds.
Adoption studies (eg: children from low IQ biological families raised by high IQ adoptive families).
Situations where children's schooling has been interrupted by war, say.
Researchers focused on the environmental effects on intelligence look at social and cultural variables (eg:
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social class), schooling, and family environment, for example, as well as "biological" factors like nutrition, environmental toxins, and the womb environment 13 (eg: maternal alcoholism). For example, Hart and Risley's (1992) observations of families of different levels of socio-economic status (SES) found different use of words by the parents with their children with the upshot that children from higher SES homes develop larger vocabularies, which manifests as greater intelligence on standard IQ tests (Johnson 2013). But, at the same time, parents of higher SES are often of higher intelligence, and so provide more intellectually stimulating environments. This is called the "gene-environment correlation passive" "because the children do not take any initiative of their own to experience this environment" (Johnson 2013 p32), as opposed to brighter individuals actively seeking intellectually stimulating environments.
The effect of the environment on intelligence can be seen through childhood poverty's consequences for normal brain development. A number of studies have shown a correlation between the size of the hippocampus, and the size and shape of the cerebral cortex, and SES (taken as the measure of family income/poverty) (Noble 2017) 14 . Noble et al (2015), for instance, studied the brain structure of 1100 children and adolescents from various SES groups in the USA. A difference of 6% in cortical surface area was found between the richest and poorest groups. This difference manifest itself in language processing, and self-regulation of behaviour. But the "relation between family income and surface area was strongest at the lowest end of the income spectrum and
13 Iodine deficiency during pregnancy, for example, can lead to poor cognitive development. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, which covers children born in 1991-2 in that part of south-west England, Bath et al (2013) found that lower iodine in early pregnancy was associated with lower verbal IQ and reading ability at eight years old. Iodine levels were measured from urine samples of 959 pregnant women. This was the first study to show an association between mild-to-moderate maternal iodine deficiency and poorer cognitive development in their children in the UK. 14 In terms of actual brain differences, Mackay et al (2015) reported a thinner cortex in 12-13 year-olds from lower income families as compared to from higher income ones. Thickness of cortex has been associated with scores on tests of reading and mathematics (Gabrielli and Bunge 2017). Mackay et al (2015) was a correlational study, as with similar studies, like Hair et al (2015), who found less critical grey matter among 389 4-22 year-olds from poorer backgrounds. Gabrielli and Bunge (2017) observed: "On the one hand, a reduced cortex may simply reflect the deleterious consequence of impoverished environments. On the other hand, it could reflect a protective adaptation to such environments. Accelerated thinning could perhaps diminish the influence of negative experiences on the developing brain. Preventing the brain from being shaped by harsh influences over the course of many years could be an evolutionarily adaptive response, helping a child to better cope in adverse conditions - but premature thinning could also reduce education's influence on the developing brain" (p59). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 23
tended to level off at higher-income brackets. That is, dollar for dollar, differences in family income were associated with proportionately greater differences in brain structure among the most disadvantaged families" (Noble 2017 p40). Other research has suggested that between 15 and 44% of the gap in educational achievement between an adolescent from a low- and a high-income household is due to cortical volume or thickness (Noble 2017).
Noble (2017), however, offered this warning: "Although dozens of studies have supplied evidence of the relationship between family income and healthy brain development, this type of research needs to be placed on a surer footing. The oft-cited adage 'correlation is not causation' helps to explain the lingering uncertainty: Does growing up in a disadvantaged home cause differences in the brain, or does a distinct developmental course lead a child to flounder in school or at work?" (p41).
In terms of "educational attainment" (number of years of schooling completed), around 40% of the variation between individuals is seen as genetic-based (Rietveld et al 2013) 15 . Okbay et al (2016) identified seventy-four candidate genes that varied with educational attainment among 111 000 UK individuals 16 . The genes tended to be involved in neural development, especially in the womb. The researchers made the following warnings about interpreting their findings:
Misinterpretation of the variants as "genes for education". "Such characterisation is not correct for many reasons: educational attainment is primarily determined by environmental factors, the explanatory power of the individual SNPs [single nucleotide polymorphisms] is small, the candidate genes may not be causal, and the genetic associations with educational attainment are mediated by multiple intermediate phenotypes" [eg: "openness to experience" personality trait] (Okbay et al 2016 p541).
"It would also be a mistake to infer from our findings that the genetic effects operate independently of environmental factors" (Okbay et al 2016 p541). For
15 Rietveld et al (2013) reported three particular genetic variations involved in educational attainment (ie: independent single-nucleotide polymorphisms). However, each variant predicted only 0.02% of difference in educational attainment between individuals. Generally, genes identified only account for small differences between individuals, and Maher (2008) has referred to "the missing heritability problem". 16 This was an expansion of Rietveld et al (2013). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 24
example, Branigan et al's (2013) meta-analysis of twin studies found that genetic influences on educational attainment varied between countries and between cohorts studied.
Making use of developments in genomic research and building on Okbay et al (2016) (Martin 2018), Smith- Woolley et al (2018) compared 4814 18 year-olds in the UK on exam performance based on school type (non-selective State, selective State (grammar schools), and private (often selective)) 17 . There is an underlying argument about the benefits of schools as pupils at private schools often gain better grades. But this is not necessarily a result of the "value added" by the school, it "may simply be the consequence of selection - either active, as in the case of ability or achievement, or passive, as in the case of family SES [socio-economic status]" (Smith-Woolley et al 2018 p1). Genome-wide association studies use statistical techniques to calculate specific areas of the genome (single-nucleotide polymorphisms; SNPs) that differ between individuals with variations on particular behaviours 18 . The upshot is a "genome-wide polygenic score" (GPS), as in years of education (EduYears) used in this study (ie: educational achievement scores at age 16 based on GCSE grades as a function of GPS). Non-selective State school students had significantly lower EduYears GPS than the other two school types. But controlling for pupil selection removed the difference in exam scores explained by school type. The findings suggested that "genetic and exam differences between school types are primarily due to the heritable characteristics involved in pupil selection" (Smith- Woolley et al 2018 p1) 19 . "Put another way, students with higher polygenic score for years of education have, on average, higher cognitive ability, better grades and come from families with higher SES, and these students are subsequently more likely to be accepted into selective schools. This results in a system in which children are intentionally phenotypically selected, but unintentionally genetically
17 Data came from unrelated individuals in the Twins Early Development Study (TEDS), which covers a representative sample of 16 000 twin pairs born in England and Wales in 1994-6. 18 The genome is organised into haplotype blocks, which means that genes close to each other on a chromosome are inherited together. "You can get most of the information about a person's genome (up to 95%) by genotyping for few SNPs that are associated with specific regions of the genome called LD [linkage disequilibrium] blocks" (Martin 2018 p1). 19 Fifty years ago, an influential report in the USA (Colman et al 1966) found that "schools themselves did little to affect a student’s academic outcomes over and above what the students themselves brought to them to school—‘the inequalities imposed on children by their home, neighbourhood and peer environment are carried along to become the inequalities with which they confront adult life at the end of school’" (Thomson 2018 p1). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 25
selected" (Smith-Woolley et al 2018 p4).
The authors continued: "finding an association between genotype and school type suggests that genetic factors are contributing to variation in educational environments, a concept known as gene-environment correlation (rGE). This occurs when individuals select, modify and 'inherit' their environment, in part based on their genotype. Putting our research within the context of rGE, we suggest that in addition to students being selected into schools based on their genetically 20 influenced traits (evocative rGE), children themselves also actively select educational environments that correlate with their genotype (active rGE). In the case of high achieving students, these environments might be challenging or competitive academic institutions, which grammar and private schools are often reputed to be. Finally, because we know that the factors used in school selection are substantially heritable, it is likely that academically gifted children will come from academically gifted parents. These parents not only provide the genes but also the environments to help them progress academically" (Smith-Woolley et al 2018 p4) .
Key limitations with this study include (Smith- Woolley et al 2018):
Ignoring variations in schools within three categories.
The different types of schools are not evenly distributed nationwide.
A GCSE (General Certificate of Secondary Education) composite score (based on English, Science and Mathematics) was calculated.
Family SES was measured as a mean of five measures, including maternal and paternal education, and occupation. Different measures of SES find different relationships to EA (Sirin 2005).
Deary et al (2012) compared the genome of 1940 unrelated individuals in Scotland 21 with their intelligence test scores at eleven years old and around half a century later. An individual's IQ score correlated highly over their lifetime (r = +0.62), and "nearly a quarter of the variation in the change in cognitive
20 In a Dutch study, Van Dongen et al (2018) showed that EA was influenced by epigenetics (ie: genes were altered by the environment - eg: cigarette smoke during pregnancy and the early years of life). 21 Community-dwelling, surviving members of the Scottish Mental Surveys of 1932 (born in 1921) and 1947 (born in 1936). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 26
scores that occurs throughout life could be explained by different genes being associated with this trait in childhood and later life" (Plomin 2012 p166).
A simple relationship between genes, environment, and IQ is also challenged by findings that socio-economic status (SES) (or social class) plays a role. For example, the degree of heritability of intelligence between twins was lower in families from lower SES groups than from middle or higher groups (eg: Scarr-Salapatek 1971 - Philadelphia sample; Fishbein 1980 - Sweden). More recently, Turkheimer et al (2003) found that in impoverished families, the environment had a much greater effect than genes on differences in intelligence, but this was the opposite for affluent families. This research used data from the National Collaborative Perinatal Project in the USA, which involved over 48 000 pregnant women in twelve urban hospitals, and followed their children until age seven years old. Turkheimer et al (2003) concentrated on 319 pairs of twins in the sample (114 monozygotic and 205 dizygotic). SES was categorised based on education, occupation, and income of head of the household, and IQ was measured at eight and sixteen months, and 7 years old. Turkheimer et al (2003) concluded that "a model in which variability in intelligence among children is partitioned into independent components attributable to genes and environments is too simple for the dynamic interaction of genes and real-world environments during development. The relative importance of environmental differences in causing differences in observed intelligence appears to vary with the SES of the homes in which children were raised. SES is a complex variable, however, and the substantive interpretation to be placed on our results depends on an interpretation of what SES actually measures" (p627).
4.1. ENVIRONMENTAL ENRICHMENT
Howe (1995) outlined four issues about the accelerated development of intelligence in young children (sometimes called "hothousing"):
Is it possible? What are the long-term benefits? Are there negative effects? What is the best way to do it?
One particular line of laboratory mice (known as Ts65Dn) are used as a model of Down syndrome, which is primarily learning disabilities due to chromosome 21 problems in humans (Begenisic et al 2015).
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Environmental enrichment (EE) during the early years as a means to "cure" (or at least reverse) the cognitive problems has been tried successfully with Ts65Dn mice (eg: Begenisic et al 2011). While Begenisic et al (2015) reported that "exposure to EE resulted in a robust increase in maternal care levels displayed by Ts65Dn mothers and led to a normalisation of declarative memory abilities and hippocampal plasticity in... [Down syndrome-like] offspring" (p409). EE was created with different objects and materials to interact with, and novel objects every week. A Ts65Dn female and mating male were kept in the enriched or standard environment, and after birth the mother's stimulation of and care for the pup were measured. Those in the EE condition provided more care and stimulation 22 . The pups were then raised in an enriched or standard environment. Ts65Dn mice have severe memory deficits, which were tested with an object recognition test, for example. This involves the individual becoming familiar with an object, and then ten minutes later being presented with a familiar and a novel object. Poor memory will be seen in behaving towards the familiar object as if it was a novel one, whereas mice with a good memory are more interested in the novel object. Ts65Dn mice in the early EE condition showed "a full rescue of memory abilities" (Begenisic et al 2015). The authors admitted that "our experimental study did not allow us to distinguish the relative contribution to brain development given by maternal stimulation or by the autonomous interaction with the enriching environment in developing pups, our results underscore the importance of appropriate maternal care as a fundamental source of stimulation in this vulnerable population" (Begenisic et al 2015 p418).
5. GROUP DIFFERENCES IN INTELLIGENCE
Neisser et al (1996) observed: "Group means have no direct implications for individuals. What matters for the next person you meet (to the extent that test scores matter at all) is that person's own particular score, not the mean of some reference group to which he or she happens to belong. The commitment to evaluate people on their own individual merit is central to a democratic society. It also makes quantitative sense. The distributions of different groups inevitably overlap,
22 Pupura et al (2014) have reported the benefits on cognitive development of infant body massage (stimulation) for children with Down syndrome. Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 28
with the range of scores within any one group always wider than the mean differences between any two groups. In the case of intelligence test scores, the variance attributable to individual differences far exceeds the variance related to group membership" (p90). However, this has not stopped "intense interest and debate" (Neisser et al 1996) in group differences, particularly in terms of gender, and most controversially in terms of ethnicity.
5.1. GENDER DIFFERENCES
Gender differences in general intelligence, and/or in specific abilities are hotly debated. The classic names in intelligence research, like Spearman and Cattell, saw no gender differences in general intelligence in their work. "Thus there has evolved a widely held consensus that there is no sex difference in general intelligence, whether this is defined as the IQ from an omnibus intelligence test, as reasoning ability, or as Spearman’s g" (Irwing and Lynn 2005 p506). Dissent from this consensus is based on the finding that males have larger average brain tissue volume than females (even allowing for larger male body size). So, logically males must have higher intelligence, argued Lynn (eg: 1999), for example. Anderson (2004) called this idea "idle speculation" for a number of reasons: "(a) Neanderthals had a bigger brain than current humans but nobody wants to make a claim that they were more intelligent than modern people; (b) the relationship between brain size and IQ within species is very small; (c) the causal direction is ambiguous (IQ and its environmental consequence may affect brain size rather than the other way round); (d) the brain does far more than generate IQ differences and it may be those other functions that account for any male/female differences in size" (quoted in Irwing and Lynn 2005).
Concentrating on specific tests, the non-verbal reasoning task called Raven's Progressive Matrices is commonly used. Hans Eysenck, for instance, saw no gender differences here, and this is the consensus view. Irwing and Lynn (2005) dissented here with their meta-analysis of twenty-two studies of gender differences in university students using the Matrices task. The researchers accepted that the differences may have an environmental explanation.
5.2. ETHNIC DIFFERENCES
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In the USA, in particular, such ideas are controversial because "claims about ethnic differences have often been used to rationalise racial discrimination in the past... [so] all such claims must be subjected to very careful scrutiny" (Neisser et al 1996 p90) 23 . Hernstein and Murray's (1994) "The Bell Curve" is best known for arguing that the difference in intelligence between White (or Euro-Americans) and African-Americans (Blacks) is "real" (even has a genetic basis) (table B) 24 .
True - There are differences between "races"/ethnic groups in intelligence because such groups have different genes, and these genes are the basis to intelligence.
True - There are differences in intelligence but the cause is social/environmental (eg: poverty, social disadvantage).
Artificial - Any differences in intelligence are a facet of biased IQ tests.
Artificial - Any differences are a product of "racial discrimination".
Artificial - There are no such things as "race" and "intelligence".
Table B - Possible relationships between "race" and intelligence.
For example, Flynn (1980) reported a study of German children after World War II who were fathered by US servicemen. There were no differences in IQ based on whether the father was Black or White (Howe 1998).
Leaving aside the "racial discrimination" concerns, there are many reason why establishing differences between ethnic groups is difficult. These include:
i) The definition of a particular group - For example, "Asian Americans" covers individuals from China and Japan, as well as India and Pakistan, who are very diverse and do not share a common language.
ii) The focus on certain groups and not others - In the USA, most of the focus is upon those ethnic groups who are supposedly lower in intelligence, and ignored Chinese and Japanese Americans, for instance, who do
23 This has been described as "race science" (appendix D). 24 Kingstone (2016) criticised "The Bell Curve" for recycling the "pseudo-science" of the 19th century that "published extensive 'evidence' to prove the fixedness of racial categories". Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 30
better than White Americans.
iii) Problems with IQ tests - It has been argued that IQ tests favour one group (usually Whites) over others in terms of language used and familiarity with concepts.
iv) The mechanisms of difference - If there are genetic differences in intelligence based on ethnicity, then it is necessary to explain the mechanisms of those differences. Genetic evolution of populations occurs in different ways (Sternberg et al 2005):
Random mutation in genes.
Random genetic drift - changes in genes from generation to generation such that two populations will vary over time (eg: migrant population and home population).
Gene flow or exchange - interbreeding between individuals from two different populations.
Natural selection - adaptive gene patterns to an environment become more prevalent over time.
None of these mechanisms would explain differences in intelligence based on "race"/ethnicity as it is described today, particularly as "the global distribution of genetic variation in humans is not easily sorted according to so-called races" (Sternberg et al 2005 p55) 25 .
Sternberg et al's (2005) position was made clear in their abstract: "They suggest that because theorists of intelligence disagree as to what it is, any consideration of its relationships to other constructs must be tentative at best. They further argue that race is a social construction with no scientific definition. Thus, studies of the relationship between race and other constructs may serve social ends but cannot serve scientific ends" (p46).
At the other end of an article, finishing their review of intelligence research, Hunt and Jaeggi (2013) stated: "We have said nothing about racial differences in intelligence. Our belief is that this is simply not an important scientific issue. Racial and socio-economic
25 Using the example of protein markers (eg: blood groups; serum proteins), Lewontin (eg: 1982) estimated that about 85% of genetic variance occurs between two individuals of the same "race", about 9% between two populations of the same "race", and the remainder (6%) occurs between two "races" (Sternberg et al 2005). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 31
groupings are inherently fuzzy classes, rather than sharply defined categories. Due to migration and inter- marriage, the identity of different racial groups can change in a very few years. Furthermore, in the developed countries racial differences are substantially confounded with differences in socio-economic status. For all these reasons, it is unlikely that the study of racial differences will reveal very much about the nature of intelligence. We do acknowledge the fact that (and note how carefully this is worded) indices of cognitive competence, including intelligence test scores, are differentially distributed across demographic groups. There are circumstances under which this fact may be relevant in discussions of social policy" (p50).
5.3. REGIONAL/SOCIO-ECONOMIC DIFFERENCES
Average IQ in a country correlates with the socio- economic development of that country across the world, and also within a country (ie: average IQ of a region) (eg: UK, France, Italy, USA) (Carl 2016). Using data from the 1940s and 1950s, Lynn (1979) calculated the average IQ in twelve regions of the UK, and in the Republic of Ireland, and found correlations with first class degrees per capita, and income per capita, for example, in a region, while average IQ was negatively correlated with unemployment and infant mortality. Carl (2016) updated this study using data from the UK Household Longitudinal Survey (with the latest wave in 2011-13). The IQ score was based on six measures of cognitive ability (table C). Average IQ was calculated for the twelve UK regions, and it varied between 102.6 in the south-east to 98.2 in Wales 26 . Fourteen measures of socio-economic development were used (eg: weekly earnings; life expectancy).
The correlation between the average regional IQ of Lynn's (1979) and Carl's (2016) data was weak. Carl (2016) explained: "Assuming the two sets of estimates are accurate and comparable, this suggests that the relative IQs of different UK regions have changed since the 1950s, most likely due to differentials in the magnitude of the Flynn effect, the selectivity of external migration, the selectivity of internal migration or the strength of the relationship between IQ and fertility" (p413) 27 . Immediate word recall - recall ten words immediately after hearing the list.
26 This compares to a range of ten IQ points between regions in the USA (McDaniel 2006). 27 Note that the regional areas were not the same in both studies (Carl 2016). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 32
Delayed word recall - recall ten words after short delay since list read out. Serial subtraction - subtract 7 from 100 and so on. Number series - identity the next number in a sequence. Verbal fluency - name as many animals as possible in one minute. Numeracy - solve short mathematical problems.
Table C - Six measures of cognitive ability used for the IQ score.
The regional IQ correlated with income, longevity, and technological accomplishment (eg: patent applications per capita), and negatively with poverty, deprivation, and unemployment. Carl (2016) was cautious about interpreting the findings because "given the cross-sectional nature of the data and lack of statistical controls, support for the hypothesis that average IQ has a causal impact on socio- economic development should be considered only preliminary at best. Indeed, a plausible alternative hypothesis is that higher-IQ individuals migrate to regions that happen to have greater socio-economic development" (p414).
6. APPENDICES
APPENDIX A - ALTERNATIVES TO INTELLIGENCE
Different characteristics have been proposed as more important than intelligence/IQ for predicting educational/academic success. These include:
i) Self-discipline - Duckworth and Seligman (2005) performed two studies with 12-13 year-olds in the USA. Their self-discipline (eg: not impulsive; delay gratification) was reported by parents and teachers, and this correlated with exam scores seven months later. In fact, self-discipline accounted for twice as much variation in later academic performance as IQ.
ii) Intrinsic and identified self-motivation - Intrinsic motivation is performing an activity out of personal interest, and identified self-motivation is performing a behaviour because it is beneficial, while extrinsic motivation is dong the behaviour for an external reward, like money. Burton et al (2006) showed that intrinsic and identified self-motivation predicted academic performance at a later date. In one study, 241 8-13 year-olds in Canada completed a questionnaire about motivation one week before a termly report card. Identified self-
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motivation was significantly associated with grades. In a second study, sixty Canadian undergraduates completed measures about motivation two weeks prior to exams. Intrinsic motivation items included, "I find the course material interesting", and an identified self- motivation item was, "I value being able to learn from the course material". "The more that students had an identified self-regulation, the higher were their grades on the final examination" (Burton et al 2006 p758). The researchers concluded: "In tandem, intrinsic and identified regulations should help people to achieve their goals, and, happily, to feel good in the process" (Burton et al 2006 p761). Note that measures of IQ were not taken in this research.
APPENDIX B - CRITICISMS OF PSYCHOMETRICS AND INDIVIDUAL DIFFERENCES
Talking about personality, Trofimova et al (2018) began: "We all know how family upbringing, culture, personal history and socio-economic status (SES) can contribute to individual differences in values, attitudes, manners, skills and habits that manifest as consistent patterns in human behaviour. Yet, at some point all of us start wondering why children in the same family appear to be different from a very early age on, although they have the same parents, teachers, SES and culture (ie: the same social environment). We often observe that there is something in people that remains rather stably over time, no matter how much training or education is applied. This 'something' must come from biological factors" (p1). The "something" is called temperament or personality, and over forty models have been proposed to explain it in the last century (Trofimova et al 2018). Many of these models were based on psychometrics, and the statistical association of traits (as established with the use of factor analysis). Trofimova et al (2018) noted five "faulty trends" in the use of psychometrics:
i) "Evidence of psychometric properties of tests is mistakenly considered as evidence of the structure of the actual phenomena under study" (Trofimova et al 2018 p5).
ii) "Statistical linear models are used as evidence of a structure underlying non-linear, contingent and feedback processes" (Trofimova et al 2018 p5).
iii) "In the description of factor analysis-evidence for specific structures, no attention is given to biases in the data" (Trofimova et al 2018 p6).
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iv) Observation of cross-cultural similarities is taken as evidence for biologically-based traits.
v) Statistical evidence is valued above concepts.
Much of the psychometric approach assumes that traits are dimensional (eg: sociable-unsociable), whereas Trofimova et al (2018) argued for "interdependence between components contributing to biologically-based traits". They emphasised that "despite intense efforts over several millennia, the task of classifying biologically- based traits and their deviations as mental illnesses continues to be incomplete and challenging. The more knowledge that human-kind has gained, the more challenging this task appears to be, and during the twentieth century, the lists of neuroanatomical and neurochemical systems contributing to biologically-based behavioural traits grew into the hundreds. We are still very far from declaring 'mission accomplished' in the classification of these traits..." (Trofimova et al 2018 p9). The answer is a combination of models, methods, approaches, and disciplines for Trofimova et al (2018). These comments relate to personality, but the criticisms of the psychometric approach are as relevant to how it conceptualises intelligence.
APPENDIX C - COGNITIVE ENHANCEMENT
Grayson (2016) began with this observation: "From superfoods to brain training, the Internet is full of advice on how to improve cognitive health and boost brain power. Yet anyone curious enough to dip into the scientific literature will find a complicated picture behind the claims" (pS1). Hildt (2013) defined cognitive enhancement as "the use of drugs, biotechnological strategies or other means by healthy individuals aiming at the improvement of cognitive functions such as vigilance, concentration or memory without any medical need" (quoted in Singh et al 2014).
In terms of pharmacological cognitive enhancement (PCE), around 3% of British and Irish students admitted to using prescription medication as cognitive enhancers (Singh et al 2014) (table D). This compares to between 5- 35% (depending on the study) by US students, and 0.8% to 16% in EU studies (Singh et al 2014). Battleday and Brem (2015) reviewed twenty-four papers on modafinil (prescribed for narcoplepsy, say) and cognitive performance among health users. There was some
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enhancement in areas like decision-making, planning, and fluid intelligence. But not all the studies in the review were uniformly positive (Dance 2016).
Singh et al (2014) recruited online a convenience sample 28 of 877 students (75% undergraduates) at universities in the UK and Ireland between February and September 2012. Respondents were presented with 27 substances and asked which they had used to "improve cognitive functioning and performance". A fake drug called "Relevin" was included to check for lying, and two respondents claimed to have taken it.
Caffeine pills and energy drinks were most popular as cognitive enhancers.
Concentrating on three prescription drugs used as PCE: Methylphenidate (eg: Ritalin): 1-2.5% of respondents (regular - occasional use). Modafinil: 0.7% (regular) to 2.1% (occasional use). Adderall (mixed amphetamine salts): 0.3-1.4%.
Table D - Details of Singh et al (2014).
From the opposite side, intelligence can influence diet. Gale et al (2007), for instance, examined the relationship between IQ in childhood and vegetarianism in adulthood using data from the 1970 British Cohort Study. This involves 17 198 live births in Britain between the 5th and 11th of April 1970. IQ was measured at ten years old, and vegetarianism at 30 years old. Complete data were available for 8170 individuals. In total, 4.5% of respondents were vegetarian, and these individuals had a higher childhood IQ score than non-vegetarians (mean: 106.1 vs 100.6 (males); 104.0 vs 99.0 (females)). The results suggested that "children who are more intelligent may be more likely to become vegetarian as adolescents or as young adults" (Gale et al 2007), though details were not collected of how long individuals had been vegetarian. The researchers argued that "coupled with the evidence on the potential benefits to cardiovascular health of a vegetarian diet", the findings "help to explain why higher IQ in childhood or adolescence is linked with a reduced risk of coronary heart disease in adult life" (Gale et al 2007). Also vegetarianism may be one of a number of healthy lifestyle choices made by individuals with higher IQ. APPENDIX D - RACE SCIENCE
28 "Results of convenience sample surveys may be biased, due to participant self-selection and other factors" (Singh et al 2014 p10). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 36
Kingstone (2016) noted: "Race or the idea that there are biologically different human 'species' that we can label by physical type and also connect to superior and inferior traits such as intelligence, morality, physical robustness and work ethic, is an idea that just won't die". It is grounded in the "race scientists" of the 19th century who provided taxonomies of humans (as well as animals and flowers) in order to highlight the differences. "Borders needed to be drawn and the law needed to have clear criteria for placing someone in or ousting someone from a racial category. Most believed race was something you could see whether it was in the moons of the fingernails, marks along the spine or simply darker pigmentation on the skin. Because it was believed that different species were not supposed to interbreed or it would reverse evolution and degrade mankind into a mongrel race..." (Kingstone 2016) 29 .
The nineteenth century presupposition that there must be biological differences between Whites and Blacks led to, for instance, the belief that "the higher mortality from tuberculosis among Black slaves to be due to a biological intolerance of cold weather - not the crowded, unsanitary living conditions they endured" (Fofana 2013 p137). Other ideas included "drapetomania" - "a madness that led slaves to run away, in contradiction to their innate (biological) tendency for servility" (Fofana 2013 p137).
Omi and Winant (1994) showed how "the fiction of race was constructed and sustained as a powerful creation myth of the United States that enabled the enslavement and exploitation of a designated group now called black" (Kingstone 2016). In 1950, UNESCO stated the current view that "race" is a myth, and the American Anthropological Association admitted in 1998 that " present-day inequalities between so-called 'racial' groups are not consequences of their biological inheritance but products of historical and contemporary social, economic, educational, and political circumstances" (quoted in Kingstone 2016). "Intellectually, race is now understood as a 'lived experience of a people' or 'a series of networks and a set of relationships, but 'not anything organic to humans biologically (Hobbs 2014). This was further emphasised when the human genome project's data that all humans come originally from Africa and are 99.9 percent
29 "'Race' can be understood to mean many things. In common parlance, it often refers to a combination of characteristic physical traits - most notably skin colour - geographic origin(s), and socio-cultural affiliation(s), mediated by content-specific historical and political factors (eg: colonisation, immigration)" (Fofana 2013 p137). Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 37
the same in their DNA... has made it hard for anyone to claim there is such a thing as biological race" (Kingstone 2016).
Evans (2015) noted that "race science has arrived in four related forms" - books, academic journals generally, evolutionary psychology own journals, and articles "which seem immune to counter-argument". The "key flaw comes from its misplaced faith in IQ tests" (Evans 2015 p42).
The use of categories of "race" are necessary for some people in order to eliminate racial disparities (ie: "more race-conscious"), while others "argued that the continued use of racial categories only serves to reify the biological concept of race and reinforce existing preconceptions..." (Fofana 2013 p139).
Examples of Controversial Studies
Lynn (2002)
One of the controversial ideas is relating skin colour and intelligence, but specifically lighter skin colour of African Americans (suggesting more White ancestry) and higher intelligence. Lynn (2002) is an example of this type of research. He used secondary data in the form of the National Opinion Research Centre's (NORC) opinion poll survey in 1982, which interviewed a nationally representative sample of adults in the USA. Intelligence was measured by the correct definition of ten words given, while skin colour was self-rated. Participants were asked if they describe themselves as White, Black or Other, and then, if they said Black, as describing their skin as very dark, dark brown, medium brown, light brown or very light. The respondents were divided into five groups of skin colour, and an average vocabulary (IQ) score was calculated per group. The average score was higher with increasing lightness of skin. Lynn (2002) took the findings as going "a considerable way to establishing that there is a significant association between light skin colour and intelligence among African Americans" (p171).
This study has poor methodology, and to draw any great conclusions from it is wishful thinking. The key methodology issues include:
i) Using the ability to give the meaning of ten words as the measure of intelligence. Vocabulary is not a "pure" measure of intelligence, but it is influenced by
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books read, for example, so it is closer to a measure of general knowledge.
ii) The grading of skin colour were self-categorised as well as being vague (eg: no definition given of what "very light" means).
iii) The sample size of the skin colour sub-groups varied - eg: fourteen "very light", 42 "very dark", and the highest, 204, "medium".
Hill (2002) reanalysed Lynn's (2002) data, and found that the association between skin colour and intelligence "disappears once childhood environmental factors are considered" (p209). This means Lynn (2002) "perilously disregards the body of research showing that environmental factors such as family background and educational opportunities influence cognitive development... As a legacy of slavery and racial subordination, light-skinned African Americans are more likely to be born into higher status families than their darker counterparts... As such, they enjoy childhood environments and educational backgrounds more conducive to the development of cognitive skill" (Hill 2002 p211).
Rushton (1994)
Rushton (1994) analysed data on cranial capacity (as an indicator of brain size) from around the world to go with the idea that larger brains mean greater intelligence. After adjusting for body size, the average cranial capacity of East Asian and European samples was greater than African samples.
This type of research can be criticised in two main ways:
i) The methodology, including calculating cranial size, and adjusting for body size. Gould (1981), for instance, has reviewed these issues.
ii) The theory - ie: that larger brains do mean greater intelligence. Between species this is the case (eg: chimpanzees more intelligent than rats), but within a species (ie: humans) it is less clear-cut. Rushton and others have used an evolutionary argument that early humans who left Africa, and became the East Asians and Europeans of today, faced new challenges to survival which needed greater intelligence. Such ideas are speculative, and the differences in populations of early humans do not map easily on to the ethnic groups seen today.
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No.13 - November 2016: Comparative Psychology http://psychologyteachersupdate.yolasite.com/no13--- november-2016-comparative-psychology.php
No.14 - May 2017: Social Media http://psychologyteachersupdate.yolasite.com/no14---may- 2017-social-media.php
No.15 - November 2017: Sleep Health http://psychologyteachersupdate.yolasite.com/no15--- november-2017-sleep-health.php
Old Series
2002
No.1 - September: Memory (http://www.archive.org/details/PsychologyTeachersUpdateN o.1)
2003
No.2 - January: Evolutionary Psychology (http://www.archive.org/details/PsychologyTeachersUpdateN o.2)
No.3 - May: Biological Psychiatry (http://www.archive.org/details/PsychologyTeachersUpdateN o.3)
No.4 - September: Social Constructionism (http://www.archive.org/details/PsychologyTeachersUpdateN o.4)
2004
No.5 - January: Atypical Development (http://www.archive.org/details/PsychologyTeachersUpdateN o.5)
No.6 - May: Issues in Health Psychology (http://www.archive.org/details/PsychologyTeachersUpdateN o.6)
No.7 - Sept: Developmental Psychology (http://www.archive.org/details/PsychologyTeachersUpdateN o.7)
No.7 Supplement (No.1): Child Physical Abuse, Neglect and Disadvantage
Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 47
(http://www.archive.org/details/PsychologyTeachersUpdateN o.7Supplement1)
2005
No.8 - January: Children in Court (http://www.archive.org/details/PsychologyTeachersUpdateN o.8_309)
No.9 - May: An Introduction to Psychoneuroimmunology (http://www.archive.org/details/PsychologyTeachersUpdateN o.9)
No. 10 - September: Qualitative Psychology and Research Methods (http://www.archive.org/details/PsychologyTeachersUpdateN o.10)
2006
No.11 - January: Altruism and Helping Behaviour (http://www.archive.org/details/PsychologyTeachersUpdateN o.11)
No.12 - May: Sleep (http://www.archive.org/details/PsychologyTeachersUpdateN o.12)
No.13 - September: Psychology of Ageing and Older Adults (http://www.archive.org/details/PsychologyTeachersUpdateN o.13)
2007
No.14 - January: Social Psychology (http://www.archive.org/details/PsychologyTeachersUpdateN o.14)
No.14 Supplement (No.2): Social Identity Theory in Recent Years (http://www.archive.org/details/PsychologyTeachersUpdateN o.14SupplementNo.2)
No.15 - May: New Theoretical Ideas (http://www.archive.org/details/PsychologyTeachersUpdateN o.15_912)
No.16 - September: Addiction (http://www.archive.org/details/PsychologyTeachersUpdateN
Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 48
o.16)
2008
No.17 - January: Anomalistic Psychology (http://www.archive.org/details/PsychologyTeachersUpdateN o.17)
No.18 - May: Behavioural Genetics/Peace Psychology (http://www.archive.org/details/PsychologyTeachersUpdateN o.18)
No.19 - September: Schizophrenia (http://www.archive.org/details/PsychologyTeachersUpdateN o.19)
2009
No.20.1 - January: Cognitive Neuropsychology (http://www.archive.org/details/PsychologyTeachersUpdateN o.20.1)
No.20.2 - January: Applied Social Psychology (http://www.archive.org/details/PsychologyTeachersUpdateN o.20.2)
No.21 - May: Consciousness/Consumer Behaviour (http://www.archive.org/details/PsychologyTeachersUpdateN o.21)
No.22 - September: Aspects of Childhood (http://www.archive.org/details/PsychologyTeachersUpdateN o.22)
2010
No.23 - January: Attention Deficit Hyperactivity Disorder (http://www.archive.org/details/PsychologyTeachersUpdateN o.23)
No.24 - May: Social Neuroscience (http://www.archive.org/details/PsychologyTeachersUpdateN o.24)
Psychology Teachers Update New Series No.16; May 2018; ISSN: 1478-4548; Intelligence in the 21st Century; Kevin Brewer 49