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18.11: Chapter 14- Traditional Models of Human Intelligence

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    Learning Objectives
    1. Discuss two common tests for measuring intelligence.
    2. Describe at least one “type” of intelligence.
    3. Describe Carroll's three-stratum theory of intelligence and name the factor at the top level in this theory.
    4. Discuss intelligence in simple terms
    5. Name a brain network thought to be associated with intelligence


    The development of tests to measure intelligence has had a major impact on the development of ideas about the nature and structure of human intelligence, and its biological basis in the brain. Most theories of human intelligence are based on data derived from intelligence tests which is analyzed using factor analysis, a mathematical method for analyzing patterns of correlations among different measures of mental abilities. In module 14.2, we have already discussed how this method, invented and used by Spearman (1904), revealed the "g" factor in human intelligence.

    To understand current thinking and research about the biological basis of human intelligence, it is essential to gain at least a general familiarity with the major theoretical models of human intelligence psychologists have developed. The theories we examine in this module are based to a large extent on intelligence testing and factor analysis, while others are more intuitive. This section introduces key historical figures, major theories of intelligence, and common assessment strategies used to measure human intelligence.

    In section 14.2, we discussed a number of enduring, across-generation, universal regularities of the environment which have been incorporated by evolution into brain organization and intelligence. As described in that section, these enduring facts about how the world works include innate, genetically internalized information about objects in three-dimensional space, the passage of time, daily cycles of light and dark, causality relations (forming basis for causal logic and inference), similarity relations (leading to category formation and categorical logic and inference), and predictive relations, based on covariation of events, allowing human and animal brains to mentally project the organism into future time. All of these invariant properties of the world must be included in the brain's neural models or cognitive maps of the world if the brain is to effectively guide adaptive behavior.

    When we examine the traditional models of intelligence in this section, you will recognize that each focuses on only one, or a few of the facets of intelligence discussed in the evolutionary approach taken in section 14.2. In a sense, each theory discussed in this section is akin to the fable of the blind men trying to describe an elephant. Each blind man only knows that part of the elephant which he happens to feel and so each man has a different and incomplete understanding of the whole. Likewise, each theory of intelligence focuses on only part of the complex of processes that we collectively refer to as "intelligence." Nevertheless, each theory makes a contribution, and each, in one or more ways, is related to the evolutionary discussion in section 14.2.

    For example, as you will see, emotional intelligence, including Gardner's intra- and inter-personal intelligence, is related to neural representations of the contingencies of the social environment--brain mechanisms for which are the focus of the new field of social cognitive neuroscience. Gardner's multiple intelligences include spatial intelligence related to representation of objects in three-dimensional space, abilities which require portions of parietal cortex and hippocampus. At Level III in Carroll's theory of intelligence is "g," general intelligence, related to representations of causal, similarity, and predictive relations, likely involving the frontoparietal network (Jung & Haier, 2007). Of these theories, the first, Carroll's three-stratum theory of human intelligence is by far the most widely accepted and most productive in terms of explanatory power and empirical evidence. With this background, we are better prepared to examine the traditional models of intelligence in this module and, perhaps more importantly for this course, we will be better prepared to understand the biological bases of intelligence and thinking, a primary focus of this chapter.


    Every year hundreds of grade school students converge on Washington, D.C., for the annual Scripps National Spelling Bee. The “bee” is an elite event in which children as young as 8 square off to spell words like “cymotrichous” and “appoggiatura.” Most people who watch the bee think of these kids as being “smart” and you likely agree with this description.

    child in a spelling bee with sign that says 260 Ankita
    Figure \(\PageIndex{1}\): A participant in the Scripps National Spelling Bee. (CC BY-NC 2.0; Scripps National Spelling Bee via Flicrk)

    What makes a person intelligent? Is it heredity (two of the 2014 contestants in the bee have siblings who have previously won)(National Spelling Bee, 2014a)? Is it interest and motivation (the most frequently listed favorite subject among spelling bee competitors is math)(NSB, 2014b)? By the end of the module you should be able to define intelligence, discuss methods for measuring intelligence, and describe theories of intelligence. In addition, we will tackle the politically thorny issue of whether there are differences in intelligence between groups such as men and women. As you read through this module, note that we discuss possible links between each theory of intelligence and the material from the previous module on adaptation, evolution and the brain mechanisms of intelligence. Recall that all of the information processing done by the brain, including that involving what we term intelligence and thinking, can have no effect at all on the outside world unless that neural activity converges onto and acts on the motor neurons in the spinal cord and medulla which stimulate the muscles to generate movement, behavior. Intelligence and cognition, as discussed in the last module, are part of the elaborate control systems which guide movement into adaptive patterns of behavior.

    Defining and Measuring Intelligence

    When you think of “smart people” you likely have an intuitive sense of the qualities that make them intelligent. Maybe you think they have a good memory, or that they can think quickly, or that they simply know a whole lot of information. Indeed, people who exhibit such qualities appear very intelligent. That said, it seems that intelligence must be more than simply knowing facts and being able to remember them. One point in favor of this argument is the idea of animal intelligence. It will come as no surprise to you that a dog, which can learn commands and tricks seems smarter than a snake that cannot. In fact, researchers and lay people generally agree with one another that primates—monkeys and apes (including humans)—are among the most intelligent animals (see comparisons among species on cognitive abilities in module 10.1). Apes such as chimpanzees are capable of complex problem solving and sophisticated communication (Kohler, 1924).

    Scientists point to the social nature of primates as one evolutionary source of their intelligence. Primates live together in troops or family groups and are, therefore, highly social creatures. As such, primates tend to have brains that are better developed for communication and long term thinking than most other animals. For instance, the complex social environment has led primates to develop deception, altruism, numerical concepts, and “theory of mind” (a sense of the self as a unique individual separate from others in the group and understanding that others have minds; Gallup, 1982; Hauser, MacNeilage & Ware, 1996). [Also see module on Theory of Mind later in this chapter and at]

    The question of what constitutes human intelligence is one of the oldest inquiries in psychology. When we talk about intelligence we typically mean intellectual ability. This broadly encompasses the ability to learn, remember and use new information, to solve problems and to adapt to novel situations. As discussed in module 10.1, an early scholar of intelligence, Charles Spearman, proposed that intelligence was one thing, a “general factor” sometimes known as simply “g.” He based this conclusion on the observation that people who perform well in one intellectual area such as verbal ability also tend to perform well in other areas such as logic and reasoning (Spearman, 1904).

    Francis Galton, a contemporary of Spearman and a cousin of Charles Darwin, was among those who pioneered psychological measurement (Hunt, 2009). Galton was particularly interested in intelligence, which he thought was heritable in much the same way that height and eye color are. He conceived of several rudimentary methods for assessing whether his hypothesis was true. For example, he carefully tracked the family tree of the top-scoring Cambridge students over the previous 40 years. Although he found specific families disproportionately produced top scholars, intellectual achievement could still be the product of economic status, family culture or other non-genetic factors. Galton was also, possibly, the first to popularize the idea that the heritability of psychological traits could be studied by looking at identical and fraternal twins. Although his methods were crude by modern standards, Galton established intelligence as a variable that could be measured (Hunt, 2009).

    Photo of Alfred Binet with glasses and beard
    Figure \(\PageIndex{2}\): Alfred Binet (public domain)

    The person best known for formally pioneering the measurement of intellectual ability is Alfred Binet. Like Galton, Binet was fascinated by individual differences in intelligence. For instance, he blindfolded chess players and saw that some of them had the ability to continue playing using only their memory (most likely involving use of a type of cognitive map, perhaps involving parietal and prefrontal cortex) to keep the many positions of the pieces in mind (Binet, 1894). Binet was particularly interested in the development of intelligence, a fascination that led him to observe children carefully in the classroom setting.

    Along with his colleague Theodore Simon, Binet created a test of children’s intellectual capacity. They created individual test items that should be answerable by children of given ages. For instance, a child who is three should be able to point to her mouth and eyes, a child who is nine should be able to name the months of the year in order, and a twelve year old ought to be able to name sixty words in three minutes. Their assessment became the first “IQ test.”

    Intelligence tests

    Some examples of the types of items you might see on an intelligence test.

    1. Which of the following is most similar to 1313323?
      1. ACACCBC
      2. CACAABC
      3. ABABBCA
      4. ACACCDC
    2. Jenny has some chocolates. She eats two and gives half of the remainder to Lisa. If Lisa Has six chocolates how many does Jenny have in the beginning?
      1. 6
      2. 12
      3. 14
      4. 18
    3. Which of the following items is not like the others in the list: duck, raft, canoe, stone, rubber ball
      1. Duck
      2. Canoe
      3. Stone
      4. Rubber ball
    4. What do steam and ice have in common?
      1. They can both harm skin
      2. They are both made from water
      3. They are both found in the kitchen
      4. They are both the products of water at extreme temperatures

    “IQ” or “intelligence quotient” is a name given to the score of the Binet-Simon test. The score is derived by dividing a child’s mental age (the score from the test) by their chronological age to create an overall quotient. These days, the phrase “IQ” does not apply specifically to the Binet-Simon test and is used to generally denote intelligence or a score on any intelligence test. In the early 1900s the Binet-Simon test was adapted by a Stanford professor named Lewis Terman to create what is, perhaps, the most famous intelligence test in the world, the Stanford-Binet (Terman, 1916). The major advantage of this new test was that it was standardized. Based on a large sample of children Terman was able to plot the scores in a normal distribution, shaped like a “bell curve” (see Fig. 1). To understand a normal distribution think about the height of people. Most people are average in height with relatively fewer being tall or short, and fewer still being extremely tall or extremely short. Terman (1916) laid out intelligence scores in exactly the same way, allowing for easy and reliable categorizations and comparisons between individuals.

    IQ Score Distribution showing average IQ equal to 100. 68% between 85 and 115 IQ.
    Figure \(\PageIndex{3}\): A graph of IQ Score Distribution: 68% of the population will score between 85 - 115, 95% will score between 70 - 130, 2% will score below 70, 2% will score above 130, 0.1% will score below 55 and 0.1% will score above 145.

    Looking at another modern intelligence test—the Wechsler Adult Intelligence Scale (WAIS)—can provide clues to a definition of intelligence itself. Motivated by several criticisms of the Stanford-Binet test, psychologist David Wechsler sought to create a superior measure of intelligence. He was critical of the way that the Stanford-Binet relied so heavily on verbal ability and was also suspicious of using a single score to capture all of intelligence. To address these issues Wechsler created a test that tapped a wide range of intellectual abilities. This understanding of intelligence—that it is made up of a pool of specific abilities—is a notable departure from Spearman’s concept of general intelligence. The WAIS assesses people's ability to remember, compute, understand language, reason well, and process information quickly (Wechsler, 1955). However, as we will see below, these two approaches were integrated by Carroll (1993) in his model of intelligence.

    One interesting by-product of measuring intelligence for so many years is that we can chart changes over time. Over the last 80 years we have been measuring human intelligence, when new waves of people are asked to take older intelligence tests they tend to outperform the original sample from years ago on which the test was normed. This gain in measured average intelligence is known as the “Flynn Effect,” named after James Flynn, the researcher who first identified it (Flynn, 1987). Several hypotheses have been put forth to explain the Flynn Effect including better nutrition (healthier brains!), greater familiarity with testing in general, and more exposure to visual stimuli. Today, there is no perfect agreement among psychological researchers about the causes of these increases in average scores on intelligence tests over the past 80 years. Keep in mind that these intelligence tests were originally designed to predict school performance. Could it be that improvements over the years in public education, or some other social variable, may, at least in part, account for the Flynn Effect?

    Types of Intelligence

    David Wechsler’s approach to testing intellectual ability was based on the fundamental idea that there are many aspects to intelligence. Other scholars have echoed this idea by going so far as to suggest that there are actually even different types of intelligence. You likely have heard distinctions made between “street smarts” and “book learning.” The former refers to practical wisdom accumulated through experience while the latter indicates formal education. A person high in street smarts might have a superior ability to catch a person in a lie, to persuade others, or to think quickly under pressure. A person high in book learning, by contrast, might have a large vocabulary and be able to remember a large number of facts. Although psychologists don’t use street smarts or book smarts as professional terms some do believe that there are different types of intelligence.

    Carroll's Three-Stratum Model

    There are many ways to parse apart the concept of intelligence. Many scholars believe that a theory proposed by Carroll (1993) provides the best and most comprehensive model of human intelligence. Carroll divided human intelligence into three levels, or strata, descending from the most abstract down to the most specific (see Figures 10.2.4 and 10.2.5). Carroll called the highest level (stratum III) the general intelligence factor “g,” following Spearman's (1904) original concept of a general intelligence factor, evolutionary origins of which were discussed in module 10.1. Below stratum III were more specific stratum II categories which are different subsets of "g" such as fluid intelligence (Gf), crystallized intelligence (Gc), broad visual perception (Gv), processing speed, and a number of others (see Figure 10.2.5). Each of these, in turn, can be sub-divided into very specific components such as spatial scanning, reaction time, and word fluency.

    Thinking of intelligence as Carroll (1993) does, as a collection of specific mental abilities, has helped researchers conceptualize this topic in new ways. For example, Horn and Cattell (1966) were first to distinguish between “fluid” and “crystallized” intelligence, both of which are on stratum II of Carroll’s model. Fluid intelligence refers to basic processes of reasoning and other mental activities that are only minimally dependent upon prior learning and experience (such as education). Fluid intelligence is the ability to think and reason flexibly and abstractly to solve problems and encompasses the ability to see complex relationships. This is closest to the concept of general intelligence discussed in module 10.1 and most likely involves the parietofrontal network described in that module.

    Crystallized intelligence, on the other hand, refers to learned procedures and knowledge and includes the ability to use language, and skills and knowledge accumulated from experience (see chapter on learning and memory for discussion of neural mechanisms of learning). Crystallized intelligence is characterized as acquired knowledge and the ability to retrieve it. Fluid intelligence helps you tackle complex, abstract challenges in your daily life, whereas crystallized intelligence helps you overcome concrete, straightforward problems (Cattell, 1963). The latter increases with age. In general, older people have a relatively superior store of knowledge that can be put to use to solve problems.

    triangle showing components of intelligence at three strata with g, general intelligence, at the top.  See text.
    Figure \(\PageIndex{4}\): A Simplified Summary of Carroll's Model of Intelligence. Stratum I: inductive reasoning, verbal comprehension, foreign languange aptitude, visual memory, spatial scanning, sound localization, word fluency and reaction time. Stratum II: fluid, crystallized, visual perception, auditory perception, broad retrieval, cognitive speediness, processing speed. Stratum III: G (i.e. g, general intelligence).

    Carroll's three-stratum theory is based on a factor-analytic study of the correlation of individual-difference variables from data such as psychological tests, school grades, and competence ratings from more than 460 datasets. These analyses suggested a three-layered model where each layer accounts for the variations in the correlations within the previous layer.

    The three layers (strata) are defined as representing narrow, broad, and general cognitive ability. The factors describe stable and observable differences among individuals in their performance of cognitive tasks. Carroll argues further that they are not mere artifacts of a mathematical process, but likely reflect neurophysiological factors that explain the differences in ability (e.g., nerve firing rates, processing efficiency as proposed by Haier and discussed below, conduction velocity, etc).

    Carroll's taxonomy of intelligence distinguishes between level factors and speed factors. The tasks dependent upon level factors can be sorted by difficulty and individuals' scores are differentiated by whether they have acquired the skill to perform the tasks. Tasks that contribute to speed factors are distinguished by the relative speed with which individuals can complete them. Carroll suggests that the distinction between level and speed factors may be the broadest taxonomy of cognitive tasks that can be offered.

    Diagram showing Carroll's three stratum model of human intelligence.  See text.

    Figure \(\PageIndex{5}\): Carroll's three-stratum model. Key: fluid intelligence (Gf), crystallized intelligence (Gc), general memory and learning (Gy), broad visual perception (Gv), broad auditory perception (Gu), broad retrieval ability (Gr), broad cognitive speediness (Gs), and processing speed (Gt). Carroll regarded the broad abilities as different "flavors" of g.

    Gardener's Multiple Intelligences Theory

    Howard Gardner, a Harvard psychologist and former student of Erik Erikson, is another figure in psychology who is well-known for championing the notion that there are different types of intelligence. In Gardner’s theory, each person possesses at least eight intelligences. Among these eight intelligences, a person typically excels in some and falters in others (Gardner, 1983). Gardner’s theory is appropriately, called “multiple intelligences.” Gardner’s theory is based on the idea that people process information through different “channels” and these are relatively independent of one another. He has identified 8 common intelligences including 1) logic-math, 2) spatial, 3) music-rhythm, 4) verbal-linguistic, 5) bodily-kinesthetic, 6) interpersonal, 7) intrapersonal, and 8) naturalistic (Gardner, 1985). Many people are attracted to Gardner’s theory because it suggests that people each learn in unique ways. There are now many Gardner-influenced schools in the world. Gardener's idea of different intelligences involving different "channels" suggests separate specialized brain mechanisms for these different cognitive abilities. This is consistent with what evolutionary psychologists refer to as a modular model of the mind, brain, and intelligence. On this view the mind/brain consists of a large collection of specialized information processing modules or mini-computers each evolved to process a particular kind of information needed to solve a particular category of adaptive problem (Ermer, et al., 2007). This model is consistent with the concept of localization of function, the hypothesis that different psychological functions are anatomically localized to different areas of the brain.

    Gardner's Mulitiple Intelligences
    Intelligence Type Characteristics Representative Career
    Linguistic intelligence Perceives different functions of language, different sounds and meanings of words, may easily learn multiple languages Journalist, novelist, poet, teacher
    Logical-mathematical intelligence Capable of seeing numerical patterns, strong ability to use reason and logic Scientist, mathematician
    Musical intelligence Understands and appreciates rhythm, pitch, and tone; may play multiple instruments or perform as a vocalist Composer, performer
    Bodily kinesthetic intelligence High ability to control the movements of the body and use the body to perform various physical tasks Dancer, athlete, athletic coach, yoga instructor
    Spatial intelligence Ability to perceive the relationship between objects and how they move in space Choreographer, sculptor, architect, aviator, sailor
    Interpersonal intelligence Ability to understand and be sensitive to the various emotional states of others Counselor, social worker, salesperson
    Intrapersonal intelligence Ability to access personal feelings and motivations, and use them to direct behavior and reach personal goals Key component of personal success over time
    Naturalist intelligence High capacity to appreciate the natural world and interact with the species within it Biologist, ecologist, environmentalist

    Note that Gardener's Bodily kinesthetic intelligence and Spatial intelligence are likely to both involve the parietal lobe, while his Linguistic intelligence involves Broca's and Wernicke's areas, prominent language areas of the brain to be discussed later in this chapter. It is also likely that Gardener's Logical-mathematical intelligence requires processing in the frontoparietal network (Jung & Haier, 2007).

    It has been suggested that Gardner simply relabeled what other theorists called “cognitive styles” as “intelligences” (Morgan, 1996). Furthermore, developing traditional measures of Gardner’s intelligences is extremely difficult (Furnham, 2009; Gardner & Moran, 2006; Klein, 1997).

    Gardner’s inter- and intrapersonal intelligences are often combined into a single type: emotional intelligence.

    Emotional Intelligence

    Emotional intelligence encompasses the ability to understand the emotions of yourself and others, show empathy, understand social relationships and cues, and regulate your own emotions and respond in culturally appropriate ways (Parker, Saklofske, & Stough, 2009). People with high emotional intelligence typically have well-developed social skills and Goleman claims it can be a better predictor of success than traditional intelligence (Goleman, 1995). However, emotional intelligence is difficult to measure and study empirically, with some researchers pointing out inconsistencies in how it is defined and described (Locke, 2005; Mayer, Salovey, & Caruso, 2004).

    Regardless of the specific definition of emotional intelligence, studies have shown a link between this concept and job performance (Lopes, Grewal, Kadis, Gall, & Salovey, 2006). In fact, emotional intelligence is similar to more traditional notions of cognitive intelligence with regards to workplace success.

    Emotional intelligence, as defined by Parker, et al. (2009), likely involves the anterior cingulate cortex (ACC), the limbic system, and the prefrontal cortex and the connections between them (Stevens, et al., 2011).

    Sternberg's Triarchic Model of Human Intelligence

    Robert Sternberg developed another theory of intelligence, which he titled the triarchic theory of intelligence because it sees intelligence as comprised of three parts (Sternberg, 1988): practical, creative, and analytical intelligence (Figure).

    three boxes connected by arrows representing analytical intelligence, creative intelligence, practical intelligence.  See text.
    Figure \(\PageIndex{6}\): Sternberg’s theory identifies three types of intelligence: practical (street smarts and common sense), creative (imaginative and innovative problem solving), and analytical (academic problem solving and computation). (What are Intelligence and Creativity? by OpenStax Colleg licensed CC BY-NC 4.0 via OER Commons)

    Practical intelligence, as proposed by Sternberg, is sometimes compared to “street smarts.” Being practical means you find solutions that work in your everyday life by applying knowledge based on your experiences. This type of intelligence appears to be separate from traditional understanding of IQ; individuals who score high in practical intelligence may or may not have comparable scores in creative and analytical intelligence (Sternberg, 1988).

    Analytical intelligence is closely aligned with academic problem solving and computations. Sternberg says that analytical intelligence is demonstrated by an ability to analyze, evaluate, judge, compare, and contrast.

    Creative intelligence is marked by inventing or imagining a solution to a problem or situation. Creativity in this realm can include finding a novel solution to an unexpected problem or producing a beautiful work of art or a well-developed short story. Imagine for a moment that you are camping in the woods with some friends and realize that you’ve forgotten your camp coffee pot. The person in your group who figures out a way to successfully brew coffee for everyone would be credited as having higher creative intelligence.

    Intelligence and Creativity

    Creativity is the ability to generate, create, or discover new ideas, solutions, and possibilities. Very creative people often have intense knowledge about something, work on it for years, look at novel solutions, seek out the advice and help of other experts, and take risks. Although creativity is often associated with the arts, it can be found in every area of life, from the way you decorate your residence to a new way of understanding how a cell works.

    Dr. Tom Steitz, the Sterling Professor of Biochemistry and Biophysics at Yale University, has spent his career looking at the structure and specific aspects of RNA molecules and how their interactions could help produce antibiotics and ward off diseases. As a result of his lifetime of work, he won the Nobel Prize in Chemistry in 2009. He wrote, “Looking back over the development and progress of my career in science, I am reminded how vitally important good mentorship is in the early stages of one's career development and constant face-to-face conversations, debate and discussions with colleagues at all stages of research. Outstanding discoveries, insights and developments do not happen in a vacuum” (Steitz, 2010, para. 39). Based on Steitz’s comment, it becomes clear that someone’s creativity, although an individual strength, benefits from interactions with others.

    Creativity is often assessed as a function of one’s ability to engage in divergent thinking. Divergent thinking can be described as thinking “outside the box;” it allows an individual to arrive at unique, multiple solutions to a given problem. In contrast, convergent thinking describes the ability to provide a correct, well-established answer or standard solution to a problem (Cropley, 2006; Gilford, 1967).

    Brain Correlates of Intelligence and Creativity

    Jung and Haier (2013) report a number of brain correlates of intelligence and creativity. However, they argue against the idea of one brain area for one cognitive function. Instead, as discussed in the prior module, they argue that brain networks involving multiple brain areas are involved in cognition, especially in complex psychological processes such as intelligence and creativity. Nevertheless, they recognize that brain injury and lesion studies reveal brain structures that are necessary, though not sufficient, for certain psychological functions. They give three examples: 1) Phineas Gage, who survived an iron rod passing through his frontal lobe resulting in personality and emotional changes as well as impaired judgement and loss of many social inhibitions; 2) "Tan," whose brain damage led to identification of Broca's area for language expression; and 3) H.M., whose bilateral surgical removal of temporal lobe structures including hippocampus revealed the role of hippocampus and related structures in formation of new long-term explicit memories and their retrieval.

    Within this context, Jung and Haier (2013) note some interesting observations from post-mortem examination of the brain of the famous theoretical physicist, Albert Einstein (whose work led to the equation, E=mc2), and what it might suggest about brain mechanisms in creativity. Einstein's brain was unremarkable in many ways. Its size and weight were within the normal range for a man of his age, and frontal and temporal lobe morphology and corpus callosum area were no different from control brains. However, there was one pronounced difference. According to Jung and Haier, Einstein's brain was missing the parietal operculum, the typical location of the secondary somatosensory cortex, resulting in a larger inferior parietal lobule. In Einstein's brain, the inferior parietal lobule was approximately 15% wider than in the brains of normal controls. According to Jung and Haier, this region of brain is associated with "visuospatial cognition, mathematical reasoning, and imagery of movement . . . and its expansion was noted in other cases of prominent physicists and mathematicians." They add that further examination of this area of Einstein's brain revealed that rather than more neurons, this region of his brain had a much larger number of glial cells, which provide nutrition to neurons, perhaps indicating an unusually large amount of activity among neurons in this region of his brain.

    Significantly, as described in the prior module, parietal cortex has strong linkages with prefrontal cortex forming a frontoparietal network: the inferior parietal lobule is primarily connected with dorsolateral prefrontal cortex (Bruner, 2010), associated, in part, with abilities for abstract thought, while upper parietal regions, according to Bruner, as discussed in module 14.2, are associated in the literature with functions such as abstract representation, internal mental images, “imagined world[s],. . . and thought experiment” (i.e., imagination). Jung and Haier detail another study of Einstein's right prefrontal association cortex, where researchers found greater packing density of neurons (same number of neurons in a smaller space), which was interpreted as shorter conduction times between cortical neurons in Einstein's brain compared to control brains. Jung and Haier conclude that Einstein's brain differed from controls in the frontoparietal network. These authors have proposed that the frontoparietal network is crucial to human intelligence; furthermore they hypothesized that differences among people in the efficiency of neural communication between the frontal and parietal regions of cortex accounts for differences in intelligence in humans (Jung & Haier, 2007). In part, this idea is based on their finding that high IQ people show less activity in these brain areas during a complex cognitive task, while lower IQ people show more brain activity, suggesting that high IQ is related to efficiency in neural information processing operations. Moreover, higher IQ and ability for abstraction are both inversely correlated with cerebral glucose metabolic rate (Haier et al., 1988, 1992, 2003, 2004), suggesting an efficiency model of individual differences in g in which superior ability for abstraction increases processing efficiency. In their Parietal-Frontal Integration Theory (P-FIT) of the neural basis of intelligence, after sensory processing, information "is then fed forward to the angular, supramarginal, and inferior parietal cortices, wherein structural symbolism and/or abstraction are generated and manipulated. The parietal cortex then interacts with frontal regions that serve to hypothesis test various solutions to a given problem." They add that "the anterior cingulate is involved in response selection as well as inhibition of competing responses. This process is critically dependent on the fidelity of underlying white matter needed to facilitate rapid and error-free transmission of data between frontal and parietal lobes" (Jung & Haier, 2013, p. 239). They also note that research in genetics shows that "intelligence and brain structure (i.e., gray and white matter) share common genes" (p. 240).

    Regarding creativity specifically, these authors refer to a theory by Flaherty (2005) which proposes a frontotemporal system driven by dopaminergic limbic activity which provides the drive for creative expression whether art, music, writing, science, etc. and as measured by tests of divergent thinking. Jung and Haier (2013) explain that the temporal lobe normally inhibits the frontal lobe so that lesion or mild dysfunction of the temporal lobe releases activity from the frontal lobe by disinhibition causing increased interactions of frontal lobe with other brain regions, sometimes leading to increased creative outputs from neurological patients with left side damage. They argue that this and other data from "three structural studies point to a decidedly left lateralized, frontosubcortical, and disinhibitory network of brain regions underlying creative cognition and achievement" (p. 244). They add that this model, which still requires much more empirical investigation, "appears to include the frontal and temporal lobes, with cortical “tone” being modulated via interactions between the frontal lobes, basal ganglia and thalamus (part of the dopamine system) through white-matter pathways" (p. 244). Although this model is speculative for such a complex form of cognition as creativity, it can guide continuing research into how humans develop creative intellectual and artistic products.

    Inferior parietal lobule
    Drawing of lateral view of human brain highlighting the inferior parietal lobule.  See text.

    Figure \(\PageIndex{7}\): Lateral surface of left cerebral hemisphere, viewed from the side. (Inferior parietal lobule is shown in orange.)

    Superficial anatomy of the inferior parietal lobule.  See text.

    Figure \(\PageIndex{8}\): Superficial anatomy of the inferior parietal lobule. (Images from Wikipedia, Inferior Parietal Lobule, retrieved 9/30/21). Purple: Supramarginal gyrus. Blue: Angular gyrus. LS: Lateral sulcus (Sylvian fissure). CS: Central sulcus. IPS: Intraparietal sulcus. STS:Superior temporal sulcus. PN: Preoccipital notch.


    Intelligence is a complex concept involving multiple mental abilities, including, according to Carroll, Spearman's g factor, itself composed of a number of subtypes identified by mathematical analysis of patterns of correlations among scores on different cognitive tasks. Additional models of human intelligence include Gardner's multiple intelligences, emotional intelligence, and Sternberg's triarchic theory of intelligence, each consistent with the view that intelligence is comprised of many interacting factors. Creativity seems to be another facet of intelligence. It may be explained as arising from imagination involving visual-like mental manipulations to explore alternative actions and their probable outcomes (see discussion module 10.1). One way to understand why there is such diversity in conceptions and theories of intelligence among psychologists is that each is focusing on only one or a few aspects of the multiple processes the brain engages in when it generates neural models of the world to guide adaptive behavior. Recall that module 10.1 on Intelligence, Cognition, Language, and Adaptation included discussion of many complex properties of the social and physical environment that must be neurally modeled by the brain in order for human or animal to successfully navigate the social and physical environments. Each of the traditional theories of human intelligence discussed in this module involve representations of one or only a few of those properties of the social and physical environment. The evolutionary analysis in module 10.1 can unify the divergent models of intelligence described in this module by showing how each focuses on different sets of components of intelligence required for the construction of accurate mental/neural models or "cognitive maps" (Behrens, et al., 2018; Tolman, 1948) of the biologically significant facts of the physical and social worlds in order to guide behavior toward successful adaptation.

    Review Questions

    1. Fluid intelligence is characterized by ________.
      1. being able to recall information
      2. being able to create artistic pieces
      3. being able to understand and communicate with different cultures
      4. being able to see complex relationships and solve problems
    2. Which of the following is not one of Gardner’s Multiple Intelligences?
      1. creative
      2. spatial
      3. linguistic
      4. musical
    3. Which theorist put forth the triarchic theory of intelligence?
      1. Goleman
      2. Gardner
      3. Sternberg
      4. Steitz
    4. When you are examining data to look for trends, which type of intelligence are you using most?
      1. practical
      2. analytical
      3. emotional
      4. creative


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    Adapted by Kenneth A. Koenigshofer, PhD., from Intelligence by Robert Biswas-Diener, licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License; Thinking and Intelligence by OpenStax licensed CC BY-NC 4.0 via OER Commons; from What are Intelligence and Creativity? by OpenStax licensed CC BY-NC 4.0 via OER Commons; Wikipedia, Three-stratum theory, retrieved 9/29/21; Wikipedia, Fluid and Crystallized Intelligence, retrieved 9/29/21.

    "Overview," "Brain Correlates of Intelligence and Creativity," and "Summary" is original material written by Kenneth A. Koenigshofer, PhD, Chaffey College, licensed under CC BY 4.0

    Outside Resources

    Blog: Dr. Jonathan Wai has an excellent blog on Psychology Today discussing many of the most interesting issues related to intelligence.

    Video: Hank Green gives a fun and interesting overview of the concept of intelligence in this installment of the Crash Course series.


    G (or g)

    Short for “general factor” and is often used to be synonymous with intelligence itself.


    Short for “intelligence quotient.” This is a score, typically obtained from a widely used measure of intelligence that is meant to rank a person’s intellectual ability against that of others.


    Assessments are given to a representative sample of a population to determine the range of scores for that population. These “norms” are then used to place an individual who takes that assessment on a range of scores in which he or she is compared to the population at large.


    Assessments that are given in the exact same manner to all people . With regards to intelligence tests standardized scores are individual scores that are computed to be referenced against normative scores for a population (see “norm”).


    This page titled 18.11: Chapter 14- Traditional Models of Human Intelligence is shared under a mixed license and was authored, remixed, and/or curated by Kenneth A. Koenigshofer (ASCCC Open Educational Resources Initiative (OERI)) .