Dreaming improves creative problem solving

In a study just out, researchers at the University of California San Diego tested whether “incubating” a problem allowed a flash of insight, and found it did when people entered a type of sleep known as REM.

When we sleep we pass through different ‘stages’ of sleep that are associated with different types of brain activity. REM sleep is the stage that we know as dreaming. It is detectable by electrical activity in the brain that looks much like the waking state, and by rapid eye movements – hence ‘REM’.

Volunteers who had entered REM or rapid eye movement sleep in this Proceedings of the National Academy of Sciences (PNAS) study were then better able to solve a new problem that required lateral thinking. Lateral thinking involves looking at a problem from unconventional angles in order to solve it, making connections that are not obvious. It involves thinking ‘around’ a problem rather than tackling it head-on.

On the morning of the test day, 77 volunteers were given difficult creative problems to solve. After trying to solve them, they were asked to mull over the problem until the afternoon – either by resting but staying awake, or by taking a nap. The naps were monitored, to check whether or not the participants entered REM sleep.

Compared with quiet rest and non-REM sleep, REM sleep increased the chances of success on the problem-solving task – improving creative problem solving ability by close to 40%.

The results suggest a very special role for dreaming in problem solving. It is not just sleep itself, or the passage of time, that is important for the problem solving, but whether REM sleep has occurred.

The researchers believe that dreaming “creates a richer network of associations for future use”.

Other studies indicate it does more than this: it is actually able to help in unravelling logical connections through unconscious reasoning – an important aspect of fluid intelligence.

Dreaming improves finding logical patterns and seeing ‘the big picture’

A study in 2007 – also published in the PNAS – led by Matthew Walker and colleagues demonstrated that solving fluid intelligence type questions (such as those of the last post), involving abstracting logical relations, and seeing ‘the big picture’ in terms of underlying  patterns, was much improved by sleep.

For instance, if a person learns that A is greater than B and B is greater than C, then she knows those two facts. But embedded within those is a third fact – A is greater than C. This can be deduced by an inference. It is this kind of logical relationship finding that dreaming can also enhance. In other words, dreaming helps with fluid intelligence problem solving.

References

Jeffrey M. Ellenbogen, Peter T. Hu, Jessica D. Payne, Debra Titone, andMatthew P. Walker. (2007). Human relational memory requires time and sleep, PNAS, 104, 7723-7728.

IQ & Nature vs Nurture

Scientists have long been debating the relative contributions of genes vs environment, nature vs nurture, to our IQ level.

The following non-genetic / environmental factors have been shown to substantially impact intelligence levels.

Breast Feeding & IQ

The duration of breastfeeding during infancy has been associated with higher IQ in children. Breastfeeding during infancy is associated with enhanced childhood cognitive development by 2–5 IQ points for full-term infants and 8 points for those of low birth weight. These are striking effects. If your baby is premature it is particularly important to breast feed – or supply the equivalent nutrients – for the child’s cognitive development.

Nutrition

In a recent carefully controlled study looking at the association between intelligence and diet at 3.5 and 7 years, with a sample of 591 children, a number of dietary factors to be significantly and positively associated with intelligence. These included the following:

Breads and cereals

At  3.5 years the difference in total IQ scores for children who ate breads or cereals four or more times per day was 4 points  higher than for children not consuming bread and cereals at these levels. Breads and breakfast cereals in this study were the main sources of iron and folate, both of which are known to be important in cognitive development.

Fish

Children who ate fish at least weekly at 7 years of age had significantly higher IQ scores than those children who did not, with a difference of about 3.5 IQ points. Fish contains a number of nutrients that have been associated with cognitive functioning. For example, fish is a good source of protein, bioavailable iron, zinc, vitamin B-12 and iodine. Seafood is also a good source of vitamin B-12, iron and zinc. Importantly, fish provides a rich source of omega-3 which is known to benefit cognitive development.

Margarine

Children who ate margarine daily had IQ scores that were approximately 3 points lower than children who did not.  This negative impact of margarine on IQ was even greater for children who were born underweight. At 7 years of age, these children who ate margarine at least daily had a 6 point lower IQ compared to 7 year olds who did not eat margarine daily! Staying clear of margarine is particularly important for babies who are born small.

Butter, on the other hand, was found not to have a negative impact on intelligence. At 3.5 years of age butter was in fact positively associated with intelligence for babies born underweight. So don’t confuse margarine and butter in your baby’s diet! Stick to butter.

It is likely that it is the trans fatty acids or hydrogenated fats in margarine that are the culprit. Trans fatty acids have been associated with poorer cognitive performance in adults. Trans fatty acids may impair the metabolism of ‘smart fats’ like Omega 3.

The Flynn Effect: Globalization & our knowledge industry

There has been a rise in the average IQ score of around 3 points per decade (although the figure varies from country to country) throughout the 20th century, from the time standardized intelligence tests were first used. A steady rise of intelligence has been observed consistently across the globe, not just western developed countries. Back in 1955 when the Wechsler Adult Intelligence Scale (WAIS) was first published, the average score for 20 year olds was 100 – the test was designed so that the average would be 100. But 20 year olds taking the same exact test now score an average of over 115! Intelligence levels have increased throughout the population. This is called the Flynn Effect, named after the psychologist who identified it and brought it to our attention.

The Flynn Effect in 5 nations

These large increases in intelligence throughout the 20th century show conclusively that intelligence can be changed, on a national basis, by non-genetic influences. Better nutrition is likely to account for some of this IQ increase. In addition, the vast expansion of education – on a global scale – has had a great impact.

Many studies find that children who do not attend school score lower on the tests than their regularly attending peers. In the 1960s, when some Virginia counties closed their public schools to avoid racial integration, compensatory private schooling was available only for white children. During this period, the African-American children who received no formal education fell back at a rate of about six IQ points per year. Length of average schooling – throughout most countries globally – has steadily increased over the past century.

As Nisbett argues, the substantial impact of education on IQ gives out a positive message: If all kids are capable of learning under the right circumstances, parents and teachers should never give up on children who appear to be low performers. Everyone has the inherent ability to be smart.

Along with education, cultural changes, all of which make the world more intellectually challenging and stimulating in our global ‘knowledge economy, are likely to have had a great impact on increasing intelligence levels.

Adult brain training

Intelligence levels can be changed not just through childhood and the process of education and enculturation. Recently a brain training exercise for adults has been successfully developed by Dr Jaeggi and her colleagues at the University of Bern in Switzerland and the University of Michigan and the States, that has been demonstrated to improve fluid intelligence – our ability to reason and problem solve in new situations – by a striking 40% as measured by a well-known, standardized IQ test.  This resulted from just half an hour of training per day for 19 days. Training with this exercise called the ‘dual n-back’ has also been shown to increase neural activity in part of the frontal lobe known to be involved in higher cognitive functioning, and to increase the density of neurons’ receptors for dopamine, a neurotransmitter involved in higher cognition. This is called synaptic plasticity – it occurs at the level of the connections between individual neurons. (This training exercise can be found here - I sell it!)

Summary

This data all suggests that the environment plays a strong role in determining our IQ – both in development, and in targeting brain plasticity in adulthood. A stronger role than I had once thought.

Excellent definitions from Kevin S. McGrew in Intelligence 37 (2009) 1–10. You can’t get much more up-to-date than this.

Fluid intelligence (Gf): The use of deliberate and controlled mental operations to solve novel problems that cannot be performed automatically. Mental operations often include drawing inferences, concept formation, classification, generating and testing hypothesis, identifying relations, comprehending implications, problem solving, extrapolating, and transforming information. Inductive and deductive reasoning are generally considered the hallmark indicators of Gf. Gf has been linked to cognitive complexity which can be defined as a greater use of a wide and diverse array of elementary cognitive processes during performance.

Tests include: General sequential (deductive) reasoning, Inductive reasoning, quantitative reasoning, speed of reasoning

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Crystallized intelligence (Gc): The knowledge of the culture that is incorporated by individuals through a process of acculturation. Gc is typically described as a person’s breadth and depth of acquired knowledge of the language, information and concepts of a specific culture, and/or the application of this knowledge. Gc is primarily a store of verbal or language-based declarative (knowing what) and procedural (knowing how) knowledge acquired through the investment of other abilities during formal and informal educational and general life experiences.

Tests include: Language development, Lexical knowledge, Listening ability, General (verbal) information, Information about culture, Communication ability, Oral production and fluency, Grammatical sensitivity, Foreign language proficiency, Foreign language aptitude.

Simply listening to classical music–the so-called ‘Mozart effect’–does not make you smarter. I have presented the grounds for this conclusion in the last post. In this article we take a look at the question: do music lessons make a child smarter? Do music lessons have ‘collateral benefits’ that extend to non-musical areas of intelligence? Do music lessons increase a child’s overall IQ level, making them better at reasoning, math and language comprehension? How this question has been answered is as interesting as what the answer turns out to be.

Why is this question of interest?

Here is one answer. Children have limited free time to invest into extra-curricular activities, and parents have to make choices between activities for their children. If the choice is between, for example, ballet and music lessons, and music is known to increase intelligence but ballet is not, this might be reason enough to choose music over ballet. Ballet may be good for reasons that music may not be–for motor coordination skills, for example–but at least now the parent has a firmer basis on which choose.

How can we cannot answer the question: do music lessons improve IQ?

The question ‘do music lessons make a child smarter?’ isn’t something that can be answered through common sense and the facts of personal experience. It may be tempting to reason from your observation that all the children you know who take music lessons are doing well at school, that these lessons must be helping them develop their intelligence and school success. But this conclusion isn’t justified. Why not? Because it’s just as likely that they are both doing better at school and taking music because they are from a certain socioeconomic class where the average IQ is higher to begin with. Children with high IQs are more likely than other children to take music lessons because better educated and more affluent parents tend to provide music lessons for their children–it’s part of the culture of the more educated and affluent to provide music lessons. Not all educated and affluent parents, but a lot of them. But this doesn’t necessarily mean that music lessons have any impact on the childrens’ developing intelligence. Many educated and affluent parents also buy certain brands of clothes for their children, but the clothes children wear don’t make them more intelligent.

So we cannot go about trying to figure out whether taking music lessons improves IQ like this.

How we can answer the question: do music lessons improve IQ?

To find out the answer to this question we need to do an experiment. We need to set things up like this: take a lot of children from a variety of backgrounds and randomly assign (by the flip of a coin) half of these children to music lessons for a year, and half to some other extracurricular activity for a year–for instance ballet, or football. We test both groups of children on an IQ test before the lessons, and then again after the lessons, and see if there is a difference between the two groups. If there is a difference–if those who took music lessons on average score higher on the IQ test–we know that it’s not due to family background (because family backgrounds are mixed evenly across the two groups). If we find a difference we will also be more confident that the intelligence gain is specific to music and not any extra curricular activity (whether music, drama, ballet, karate or soccer). In essence, by doing this kind of ‘critical experiment’ we make sure that we’ve pinpointed the effect of the music lessons on intelligence.

Schellenberg’s critical experiment

In 2004 someone did finally this scientific experiment: Glenn Schellenberg from the Department of Psychology, University of Toronto. The study can be found here. He put an advertisement in a local, community newspaper, offering free, weekly arts lessons for 6 year olds for a year. 144 children were then assigned randomly to one of four different groups, with 36 children in each group. Group 1 was given keyboard lessons, Group 2 was given voice/singing lessons, Group 3 was given drama lessons, and Group 4 had no extra-curricular lessons. The instructors were trained, female professionals. The children in all groups took an intelligence test called the WISC-III both before and after the year of lessons. The WISC-III is the most highly regarded and widely used intelligence test for children. All four groups had the same average IQ level at the start of the experiment. Children in each group differed in their intelligence level of course, but the average intelligence of each group was the same. This is obviously important for us to draw any conclusions about the effects of the different types of lessons.

And what did Schellenberg find? Do music lessons increase IQ?

The first interesting finding was that all four groups of children showed an increase in IQ level after the year was up, even the group that took no lessons whatsoever. What explains this general increase in IQ for all children? An increase of IQ known to be a usual consequence of entering grade school. Since all these children started grade school during the period of the experiment, it is easy to explain this general IQ increase as due to simple attendance at school.

But–and this is the crux–the two music lesson groups had significantly greater gains in IQ than the drama and ‘no-lesson’ groups. We can conclude from this data that taking music lessons, but not drama lessons, caused gains in intelligence in addition to the gains obtained by attending school. The type of music lesson didn’t matter (whether keyboard or voice); both groups had the same average IQ score after a year of lessons. And both music groups had a 3 point higher IQ score compared to the drama and n0-lesson groups who didn’t differ from each other in their IQ score.

This relative superiority of IQ in the music groups was not confined to one particular aspect of intelligence–such as spatial intelligence–but was found in all all but 2 of the 12 subtests of the WISC-III intelligence test, across a broad range of cognitive abilities that require intelligence. It benefited all subtests of what is known as fluid intelligence–the ability to reason and find relationships in a way that does not depend on background knowledge.

The size of the effect: how should we judge it?

3 IQ points doesn’t sound like a big effect, but there is a way of looking at this gain in IQ that help put it in perspective and help us evaluate its importance. Compare it to the gain of first going to grade school. The average IQ gain of going to school was about 4 points. The additional gain of taking music lessons (3 points) was, therefore, nearly as much as the full experience of school itself. This is now looking like quite a big effect.

What is special about music?

We need to be clear about one thing. Schellenberg’s experiment shows that music lessons improve IQ for six year olds. It does not tell us that music lessons improve IQ for older children or for adults unfortunately. Six year olds’ brains are known to be highly ‘plastic’–that is, these young brains can be shaped and reorganised to a large extent by experience. Older children and adults have less brain plasticity and it might be predicted that a year of music lessons in this case would have less of an impact on general intelligence–although we don’t know for sure.

In taking music lessons, knowledge and skill relating to music increases, and this is important in itself. But what Schellenberg’s experiment shows is that in addition to this, general cognitive ability is also trained and improved – indirectly. Taking music lessons is good ‘brain training’ at this age! Music lessons involve long periods of focused attention, daily practice, reading musical notation, memorization of extended musical passages, learning about a variety of musical structures (e.g., scales, chords), and progressive mastery fine-motor skills. It is not known exactly which combination of these skills improves general intelligence, and further studies will have to investigate this question.

Introduction

The idea that classical music – particularly Mozart – makes you smarter has received a lot of press, and is widely believed to be an established fact . Music by Mozart sounds highly intelligent – it is intricate, skillful, precise and sophisticated. It seems natural to think that some sort of ‘brain entraining’  occurs just by sitting and listening to Mozart with full concentration – and that this makes you more intelligent.  We can imagine our brain activity becoming coordinated or synchronized better in response to concentrating on the amazing harmony and complexity of Mozart.  This is an appealing idea, but this article should convince you that it is not true that listening to classical music or Mozart makes you more intelligent.

The scientific evidence for the ‘Mozart effect’

Francis Rauscher and her colleagues published a study in the prestigious scientific journal Nature in 1993 that people have been quoting ever since in support of the ‘Mozart effect’ on IQ. They performed an experiment in which participants were randomly divided into three groups: one group sat in silence for 10 minutes, one group listened to a relaxation tape for 10 minutes, and the last group listened to Mozart for 10 minutes. After the 10 minutes were up, all three groups were given three sets of standard spatial intelligence tasks.  They found that the average IQ of the Mozart group was 8-9 points above the average IQ score of the other two groups. They also found that the IQ effect was only short-lived – for about 10-15 minutes.

Data from Rauscher et al. 1993

The public imagination

Based on this study the idea of the ‘Mozart effect’ (as the Press called it) quickly captivated the public imagination. The Mozart recording used in the study quickly sold out in the Boston area in the US. Governor Zell Miller in Georgia was so enthralled by this study’s findings that he actually called for the legislature to allocate $105,000 to give a free classical music CD or tape to every new mother in the state. Tennessee followed with a similar bill, and day-care centers in Florida are now required to play classical music. Needless to say, commercial opportunities were quickly exploited. Businessman Don Campbell trademarked ‘The Mozart Effect’ and published a book by that title, irritating many people with its pseudoscience and false claims. Amazon.com soon advertised half a dozen CD  titles relating to the ‘Mozart effect’ – one whole series called ‘Music for the Mozart Effect’, with other titles like ‘Better Thinking Through Mozart’ or ‘Mozart for Your Mind’ – and even ‘Ultrasound—Music for the Unborn Child’, featuring (you’ve guessed it) Mozart’s music.

But good science is not based on single studies.  There are many examples of single scientific studies that initially catch he public imagination and get a lot of press coverage, but are subsequently proved to be invalid or relatively insignificant. But while the importance of a paper fizzles out in the scientific community, it may continue to live on in the media and public imagination – because it is appealing. The ‘Mozart effect’ is a case in point – as I hope you will be persuaded after considering the points below.

Unreliability of the ‘Mozart effect’

The ‘Mozart effect’ is not consistent – some researchers have found it, some have not. It is unreliable. One of the hallmarks of good science is that a discovery can be replicated – - repeated by other laboratories at other times. Otherwise it could be argued that the so called ‘effect’ that was discovered was due to chance, or due to unintended effects in one particular laboratory that the scientists were not aware of.  Science needs replications to draw sound conclusions. The ‘Mozart effect’ does not reach this standard. In a 1999 review study by of all the ‘Mozart effect’ studies that had been published up to that point, Christopher Chabris concludes that the effect is not significant.

When an effect of listening to Mozart has been found, it is not a general intelligence effect

In those individual studies where an effect of Mozart on cognitive performance has been found,  it is found only on a very specific type of spatial task, which in no way can be considered to be a test of general intelligence.  A closer look at the original study reveals that participants only showed better performance in one of the three spatial IQ tasks they were given, in which you have to visualise folding and cutting a piece of paper. Does this sound like a good test of intelligence to you? Listening to Mozart has been shown to have no effect whatsoever on one of the most valid spatially based measures of general intelligence – the Raven’s Advanced Progressive Matrices. This contrasts with other training methods that have been shown to improve performance on this spatial intelligence test (details here).  Another fact suggests the ‘Mozart effect’ has nothing to do with general intelligence. Working memory is known to be a cognitive memory system that is closely related to general intelligence. Inidivuals with higher intelligence have more working memory capacity. Listening to Mozart has been shown to have no effect on working memory performance. So we can conclude that while listening to Mozart might improve performance on a very specific type of spatial task – and this is questionable because the effect is not reliable – it has been shown to have no effect on the majority of tests for intelligence, or on the spatial tests that are the most valid for measuring intelligence. It is therefore misleading to understand the Mozart effect (if it exists at all) as a effect on intelligence.

The effect is likely to be due to arousal or mood, not changes in cognition

Differences in mood have been shown to have an effect on performance in some reasoning tasks. Listening to Mozart generally puts people in a positive mood, and  this mood might explain better performance on the paper folding spatial task. In support of this claim, one study showed you get exactly the same effect on spatial task performance when participants listened to a Steven King story if they preferred the story to Mozart. The authors of this study concluded:

…although listening to music composed by Mozart might contribute to an improved performance on subsequently presented spatial-temporal task, our research provide no evidence that the improvement differs from that observed with other engaging stimuli that are equally pleasing to participants. (Nantais and Schellenberg 1999)

The authors’ views on their own study

Frances Rauscher,, the lead author of the original Nature study on the ‘Mozart effect’ has repeatedly denounced the over-reaction in the popular press. “I’m horrified—and very surprised—over what has happened,” she has said in an interview. “It’s a very giant leap to think that if music has a short-term effect on college students that it will produce smarter children. When we published the study results, we didn’ t think anyone would care. The whole thing has really gotten out of hand.”  “One of the things we have to be careful about is jumping to conclusions that we don’t have data on at all…I find that ‘Mozart makes you smarter’ thing is quite a bit of a leap.”

Conclusion: Listening to Mozart does not make you smarter

In conclusion, we have gone over a number of very good reasons to be skeptical about the claim that ‘Mozart makes you smart’.  The effect is not reliable. When it is found it lasts for only 10-15 minutes,  and is confined to a particular type of spatial task and not other tasks that are much better measures of general intelligence. And it is likely that the effect is due to changes in mood – not changes in cognition. The authors of the original study that caused all the excitement themselves are horrified at how their results have been interpreted.  After knowing all this, can’t we confidently conclude that listening to Mozart does not make you smarter’. It might put you in a better mood, and it won’t do you any harm, but it won’t make you more intelligent.

Whenever scientists discuss or write about ‘intelligence’, they will often have one of these four theories in mind. Different scientists have different theoretical preferences.

1. Unitary intelligence – g.  Spearman’s theory of unitary intelligence, or g.  Spearman (1923) argued that underlying all cognitive abilities was a single general intelligence factor (g) that all the abilities draw on, and which individuals differ in – in a bell curve (normal) distribution.  From a statistical standpoint, all tests of cognitive ability are correlated positively and in factor analysis load highly on one higher-order general factor that shares variance with them all. When people talk about ‘IQ level’ this is generally the underlying ability that people have in mind. Spearman (1904) argued  that the variance of performance between individuals on ANY given cognitive task (spelling, arithmetic, inductive reasoning, mental rotation, verbal analogies, etc), could be attributed to two underlying factors: g and  s – the domain-specific skill accounting for the variance unique to that type of task. On this theory, g is the more crucial factor, and the most useful in describing individual differences, and explaining differences in cognitive ability. Recent studies have argued that differences in g are due to differences in working memory: that working memory capacity and processing efficiency underly the g factor.

2. Fluid intelligence (Gf or gF) and crystallized intelligence (Gc or gC). This is the theory favoured in this blog. It was originally proposed by Cattell back in 1943, and and later revised by Horn and Cattell in 1967. It holds that cognitive ability is best understood by two discrete factors: fluid intelligence (Gf) and crystallized intelligence (Gc). Fluid g is defined as the ability to reason and problem solve with novel tasks or in unfamiliar contexts (measured by tests of spatial and inductive reasoning), while crystallized g is defined as acquired knowledge and is measured using tests of general knowledge, mathematics, reading comprehension, and vocabulary knowledge.  From an instructional perspective, this two-factor model allows domain-specific knowledge to possibly compensate for limitations in capacity and processing (fluid intelligence). One may succeed due to knowledge about a task or domain (crystallized g), or due to sheer ‘horsepower’ (fluid g).

3. Speed vs capacity. According to Fry and Hale (1996) faster information processing speed allows for more efficient working memory processes that translates into improved performance on the demanding cognitive tasks that are found in intelligence tests. A number of developmental studies show that increases in processing speed at younger ages is followed by increases in working memory, and that these appear to be directed by a unified biological system (Kail, 2000). Reading disabilities may be the result of a domain-general deficit in speed of processing, and speed is associated with more effective imagery that, in turn, was associated with spatial memory span and performance on some sub-tests of intelligence batteries.

As for the capacity factor in this theory, Just and Carpenter (1992) have proposed an acount of capacity for language comprehension (a good measure of IQ) in which individual differences in performance can be accounted for in terms of the total amount of activation that is available in working memory for both processing and storage demands.

4. Verbal, spatial, working memory, and processing speed. This is another hierarchical theory  in the individual difference psychometric tradition. There is consensus that there is a general cognitive factor (g) that accounts for about 50% or so of the variance in a broad range of mental tests given to a large sample of the population. The general factors from different batteries of mental tests show very high correlations, often well above 0.9. When that variance is taken into account, there is still variance attributable to separable  ‘level 2’ factors of intelligence, each of which accounts for under 10% of the variance. On this account those factors are verbal, spatial/perceptual organisation, working memory and processing speed. These factors provide a very good fit to performance on the 13 WAIS-III intelligence scale subtests (Deary, 2001), and the separate indices of ability that you get on WAIS-IV are based on this factor analytic model.

We have discussed speed and working memory in the context of the speed vs capacity theory. As for ‘verbal’ vs ‘spatial’, Paivio’s (1971) dual coding theory, states that incoming information is coded into a verbal-based code, spatial/ imagery-based code, or both in long-term memory. Paivio claimed that the two codes are independent of one another but information that is coded via both codes can establish a more enriched memory structure, with multiple retrieval routes to access the information. These different codes are associated, on this model, with different sub factors of intelligence that can vary independently of each other.

The literature supporting each of these theories of intelligence is deep and well-supported.

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In a recent study by Nietfeld and colleagues in the psychometric tradition, theories 1, 2 and 3 – and the verbal vs spatial componentsof theory 4, were directly tested against each other in a confirmatory factor analysis study. The fluid vs crystallized hierarchical model came out to be the best fitting to the data. This structure is also consistent with Carroll’s (1993) proposed hierarchial structure of intellectual abilities based on his comprehensive meta-analysis of more than 450 independent datasets on intelligence testing. On his account, fluid intelligence and crystallized intelligence factors showed the closest relationship to g of all the broad factors at the second level of his hierarchy.

As theoretical constructs, fluid g and crystallized g are coming through strong!

Reference

Nietfeld et al. (2007). A test of theoretical models that account for information processing demands. Contemporary Educational Psychology, 32, 3, 499-515.

Damian Birney, David Bowman, and Gerry Pallier in their Behavioral and Brain Science commentary on Clancy Blair’s ‘How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability‘ (2006, BBS, 29, 109-160 – link here) make the following excellent point:

“The gF (fluid intelligence) – working memory – executive function  issue is a further case in point. gF has developed meaning from within the psychometric domain, where it is common to define constructs not only by what they are, but also by what they are not. Hence, using factor-analytic techniques, gF has been empirically defined as the latent trait extracted from a variety of reasoning-dominated tests. This gF trait is related to, but empirically (and therefore theoretically) distinct from, the gC latent trait, which is similarly extracted from various tests of (typically verbal based) acculturated knowledge.

WM (working memory) theory was developed within the cognitive-experimental paradigm, mostly using dual-task methodologies to dissociat various storage and processing systems.

EF (executive function) has a more recent history and has been endorsed most actively by cognitive neuropsychology.

The tasks used in these related, yet distinct, research programs have been developed with different purpose in mind.”

The theory of fluid and crystallized intelligence was developed by Cattell and Horn (R. B. Cattell, 1941, 1950; 1971; Horn, 1965; Horn & Cattell, 1966), who used factor analysis to show that primary mental abilities such as reasoning ability, word fluency, verbal comprehension, facility with numbers, spatial visualization, and processing speed, can be organised into two principle classes of ability: fluid intelligence and crystallized intelligence. This conceptualization has withstood the test of time, and is widely used in cognitive neuroscience today.

Fluid intelligence (Gf or gF!) broadly captures our general reasoning ability. To quote from Jonassen &  Hopkins (1993): “Gf represents different forms of reasoning including abstracting, forming and using concepts (classification), perceiving and using relations, identifying correlates, maintaining awareness in reasoning, and abstracting ideas, especially from figural and nonverbal… content” (p. 53). It has been described as the source of intelligence that an individual uses when he or she doesn’t already know what to do.

Crystallized intelligence (Gc) describes abilities depending on specific, acquired knowledge or expertise – the result of learning and acculturation. It is measured in tests of expert knowledge, general information, use of language (vocabulary) and is reflected in a wide variety of acquired, specific skills (Horn & Cattell, 1967). Educational and cultural opportunities are central to its development. Working memory capacity is closely related to fluid intelligence, and there is strong evidence that training working memory can improve fluid intelligence (Jaeggi et al, 2008).

People with a high capacity of Gf tend to acquire more Gc knowledge and at faster rates, and this has been called Gf  investment.

Ingrid Schoon (University of London) and her colleagues have just published a remarkable paper looking at the relationship between intelligence and social and political attitudes. The sample of their study was a huge (8804 individuals!) representative sample of the British population born in 1958.  Each individual’s family socio-economic background and intelligence (g) was measured at age 11. Social attitudes and educational and occupational attainment (qualifications and occupational status) were then measured for each of these individuals 22 years later at age 33.  Interestingly, they found a direct association between higher g at 11 and more liberal social attitudes and political trust at age 33. (Having a more ‘liberal social attitude’ means being more anti-racist, socially liberal (tolerant, with and in support of gender equality. Having more ‘political trust’ means having more trust in the country’s liberal democratic political system.) As far as socio-economic status goes, individuals from more privileged backgrounds showed more political trust,  but did not differ in liberal social attitudes from those who came from less privileged backgrounds.

Structural equation model linking general cognitive ability at age 11 to social attitudes at age 33.
Strength of relationship (-1 to +1) for men (n = 4267) are shown on the left and for women (n = 4537) on the right.

This study’s results are consistent with other research showing that higher intelligence tends to be positively correlated with liberalism and negatively correlated with conservatism. A higher intelligence is also associated with the endorsement of alternatives (such as the Liberal ar Green party) to the two main political parties  in the UK (Deary et al., 2008).

Article reference:

Schoon, I., Chenga, H., Gale, C.R., Batty, D. & Deary, I. J. (in press). Social status, cognitive ability, and educational attainment as predictors of liberal social attitudes and political trust. Intelligence.

A technical note.

The technique used in this study is ’structural equation modeling’. It is a ‘model driven’ (rather than exploratory or descriptive) approach, and it is  ’causal’ rather than ‘correlational’ in its emphasis. It is a way of causally modeling the relationships between variables like g and liberal attitudes; it does not simply give you correlations which are difficult to interpret in terms of the underlying ‘causal arrows’.  These causal links between g and liberal social attitudes and political trust are significant and meaningful but how strong are they? How are we to interpret the ‘path coefficient’ values in this diagram? Well, if there is a path coefficient of 0.20 between g (at age 11) and degree of liberal attitudes at age 33, this means that if g increases by 1 standard deviation from its mean (e.g. around 15 points on a typical IQ test, from 100 to 115), then the ‘liberal social attitude’ measure would be expected to increase by 1/5 of a standard deviation from its own mean (the assumed population mean for ‘liberal social attitudes’) – holding all the other coefficients in the model constant. If liberal social attitudes were measured like a psychometric IQ test, an IQ level of 115 in the population of 11 year olds, would predict (i.e. be causally instrumental in) a liberal attitude score of 103 in the population  at 33 years of age –  3 points above the population mean for liberalism.

So as far as I can see these causal links are not that strong! Nothing like the relationship between IQ and educational attainment (in qualifications) for instance – with a path coefficient of around 0.5. There are many other factors that have an impact on liberal social attitudes other than IQ level. But it DOES have a causal role. IQ does tend to make people more liberal (and to a lesser extent less prone to conspiracy theories or cynicism about the political system).

Here is my  model of explicit motivation (as opposed to implicit, introspectively inaccessible motivation), executive processes and fluid and crystallized intelligence. Lots of reviewing of the literature and experiments to do now!