Does IQ Correspond to Brain Anatomy or Functioning?

Russell T. Warne

Jun 3, 2025

The reification of IQ as a biological entity has depended upon the conviction that Spearman’s g measures a single, scalable, fundamental “thing” residing in the human brain ... no set of factors has any claim to exclusive concordance with the real world (or in the brain). (Gould, 1996, pp. 295, 299)

The biologist Stephen Jay Gould’s most popular work was The Mismeasure of Man, a book arguing that intelligence testing and intelligence research were part of a lengthy history of social scientists fudging or misinterpreting their data to support their incorrect preconceived (and often racist) beliefs (Gould, 1981, 1996). One of the main arguments in Gould’s book is that intelligence is a reification, which is the term for an abstract idea that is treated as if it were real. The quote above encapsulates one of Gould’s reasons why he believes that intelligence or g is not a real entity: it has no apparent connection with the physical or functional properties of the brain. While this belief was not completely unreasonable when Gould wrote the first edition of his book in 1981, neuroscientists have since amassed findings that suggest that g has real connections to the anatomy and functioning of human brains.

Contributions to intelligence research from neuroscience only date back a few decades. There are two reasons for this: one obvious and another that is not so clear. First, the obvious reason: the tools of neuroscience for most of the twentieth century were too inexact to make important contributions to intelligence research. The pioneers of neuroscience were limited to studying the behavior of individuals who had a brain injury (such as a stroke or other brain damage), experimenting on individuals during neurosurgery, or conducting postmortem autopsies to learn about brain functioning. While there are a few success stories – most famously the discoveries by Broca and Wernicke in localizing important centers of language in the brain – progress was limited for many decades. All this changed with the invention of technologies that could examine the structure or functioning of brains of living individuals. The first of these technologies was electroencephalography (EEG), which measures brain waves via electrodes placed on the scalp. In the 1970s the invention of the computed tomography (CT) scan and magnetic resonance imaging (MRI) both allowed scientists to view living brains without subjecting people to neurosurgery. Later, the invention of positron emission tomography (PET) and functional MRI (fMRI) allowed scientists to determine the location of brain functioning with a much higher degree of precision than EEG technology (though not as quickly or directly). Today, neuroscientists have a wealth of technologies available to them to understand many aspects of brain functioning – including intelligence (Hunt, 2011).

The second reason why neuroscience has until recently made few contributions to the study of intelligence is that most neuroscientists are usually interested in the general principles of brain functioning. As a result, they usually focus on the commonalities of how different people’s brains function or are structured. Intelligence research, though, is built upon a focus on individual differences in task performance (i.e., who is smarter – or not) and pays much less attention to common characteristics across humans. This split in psychology between scientists interested in general principles and those interested in individual differences is an old one and not unique to neuroscience and intelligence research (Cronbach, 1957). But since the 1980s some neuroscientists have worked to bridge the gap between these two fields, and in the twenty-first century their evidence and theories support the belief that g does indeed have connections to brain anatomy and functioning (Haier, 2017a).

Correlations Between IQ and Brain Characteristics

If I Only Had a (Large) Brain. Contrary to Gould’s (1981, 1996) claims, there are brain characteristics that do correlate with IQ scores. One of the best known is the correlation between brain size and intelligence, which when measured via brain-imaging techniques in living individuals is between r = .20 and .40. Other measures of brain size (e.g., data from autopsies, or measures of head circumference) have weaker, though positive, correlations (Cox, Ritchie, Fawns-Ritchie, Tucker-Drob, & Deary, 2019; Gignac & Bates, 2017; J. Lee, McGue, Iacono, Michael, & Chabris, 2019; Pietschnig, Penke, Wicherts, Zeiler, & Voracek, 2015; Rushton & Ankney, 2009). Conversely, better measures of g produce stronger correlations with brain size (Gignac & Bates, 2017), and measures of the size of the whole brain are more strongly correlated than measures of specific brain regions. It is important, though, to note that the size of the frontal lobes seems more strongly correlated with IQ than other regions of the brain (Cox et al., 2019; Flashman, Andreasen, Flaum, & Swayze, 1997). Regardless of the details, Gould dismissed a lot of research that showed a relationship between IQ and brain size (see especially Chapter 3 of Gould, 1996). This viewpoint in Gould’s book must have been deliberate because, as Rushton (1997, p. 170) stated,

I know Gould is aware of them (the studies on the brain size–IQ relationship) because my colleagues and I routinely sent him copies as they appeared and asked him what he thought! For the record, let it be known that Gould did not reply to the missives regarding the published scientific data that destroyed the central thesis of his first edition (of The Mismeasure of Man).

Brain size is the variable that has the most research regarding its correlation with IQ scores. However, it is certainly not the only (or most important) biological variable that is correlated with IQ, nor does brain size fully explain why some people are smarter than others. For example, males and females have equal average intelligence test scores (see Chapter 27), but males have larger brains, even when controlling for their larger body size (Jensen & Johnson, 1994). Moreover, based on estimates from skull size, modern human brains are smaller than the brains of humans who lived about 100,000 years ago (Hennenberg, 1988). Today, human brains are about the same size as the typical Neanderthal brain (Hare, 2017). All of this information makes it hard to argue that “big brains are smart brains” is the entire story about the brain and IQ. As one expert explained, “(Brain) Size dominates the literature not simply because it is important in its own right, but because it is easy to measure ...” (Bouchard, 2014, p. 557). Despite this lukewarm view towards brain size, the research does show that there can be important correlations between IQ and neurological variables.

Other Brain Characteristics. Since the 1990s, neuroscientists have discovered more variables correlated with IQ. For example, research has shown the importance of the white matter in the brain, which is the tissue that connects brain regions to one another. Volume of white matter is correlated with speed of problem solving (Penke et al., 2012) and with IQ, thus showing that connectivity of brain regions is likely to be an important determinant of intelligence levels (Haier, 2017a; Kievit et al., 2016). Likewise, grey matter (where the neuron cell bodies are found) is important; loss of neurons due to disease is associated with decreases in IQ (van Veluw et al., 2012). Additionally, smarter people have more neurons in their brains, and those neurons are more densely packed together (Genç et al., 2018). Another intriguing recent finding is that neurons are better organized in high-IQ individuals’ brains than in the brains of people who score poorly on intelligence tests. This is shown in Figure 3.1, where high-IQ individuals have fewer neurites (the branches off the main body of a neuron) than low-IQ individuals.

Figure 3.1 Depiction of the differences in brain cortex volume and neuron organization in low- and high-IQ individuals. High-IQ individuals have more neurons because of their larger grey matter volume. The neurons in a high-IQ individual tend to be better organized and with fewer, less chaotic branches (called neurites) off the cell body. Source: Genç et al., 2018, p. 7.

Theories of g and the Brain

These correlations are informative because they demolish any claim that g is a statistical artifact, a reification, or a social construct (Jensen, 1998; Jung & Haier, 2007). If g were not real, then IQ scores would not correlate with any properties of the brain. But these correlations do not explain what g is, biologically. Once enough data had been amassed, though, some neuroscientists who study intelligence could create theories about how g arises from the brain. The leading theory today is called the Parieto-Frontal Integration Theory (P-FIT).

Figure 3.2 A schematic showing the P-FIT model. The circles correspond to brain regions that are often functionally important for solving tasks related to g and abstract reasoning. Dark circles are areas that are usually in the left hemisphere of the brain, while light circles are usually in the right hemisphere. The numbers correspond to Brodmann areas, which is a standardized system of mapping regions of the brain. The double-headed arrow corresponds to the arcuate fasciculus, a white matter structure that connects many of these brain regions. Source: Jung & Haier, 2007, p. 138.

A visual representation of P-FIT is depicted in Figure 3.2. In this model, there are several brain regions, mostly located in the frontal lobe (towards the front of the brain) and the parietal lobe (on the top part of the brain, somewhat towards the rear), that are connected with a white matter tract called the arcuate fasciculus. According to the P-FIT model’s creators, g arises from both how well these areas function and how well they are connected to one another in the brain (Jung & Haier, 2007). This explains two initial hurdles to understanding how g relates to the brain. The first is why there is no one area in the brain that is activated when a person engages in reasoning tasks. The second hurdle is why several previous studies had suggested that the size and use of multiple areas in the parietal and frontal lobes were correlated with IQ.

Euler (2018) has recently proposed another theory of how the brain generates g. Rather than focusing on specific brain regions, Euler has built on the predictive processing theory of brain functioning, which posits that the brain is a “prediction machine” that is adapted to help an organism form expectations about the environment and to give attention to events that violate those expectations (e.g., the presence of a danger in a location that the person thought was safe). Euler believes that intelligence may be the manifestation of the brain’s ability to handle unexpected situations and that more intelligent people are better able to manage unexpected events than people with less intelligence.

This theory would explain why intelligence differences are more apparent in more complex tasks (see Chapters 24 and 28). Other characteristics of intelligence that predictive processing theory could explain are the positive manifold (see Chapter 2), why cognitive abilities may form a hierarchy (like in the Cattell–Horn–Carroll model; see the Introduction), and why so many different tasks can measure g (see Chapter 7). This is just a hint at the non-neurological data that support predictive processing theory, and it has the potential to serve as a bridge between the neurology of brain functioning and manifestations of intelligence in people’s behavior.

Readers should recognize that predictive processing and P-FIT are not necessarily contradictory. Predictive processing is based mostly on research about brain functioning, while P-FIT is based mostly on brain-imaging data about the size and performance of larger brain regions. It is possible that both theories are correct and that predictive processing explains how the brain generates intelligent behavior while using the regions highlighted in the P-FIT model.

Conclusion

Research into the neurological basis of g is still in an early stage. It takes time for evidence to accumulate, theories to be tested, and for new data to either support or undermine a theory. Both P-FIT and predictive processing theory are too new for this process to be complete, and it is possible that these theories will need major modifications in order to accommodate the results of future studies. Indeed, both theories may be completely wrong and may one day be replaced. One of the creators of P-FIT recognized this when he told me that, “It’s a framework for testing hypotheses. Results will refine what we know and drive progress, even if P-FIT turns out to be mostly incorrect” (Haier, 2017b, punctuation altered slightly).

Regardless of what the future holds for the neurology of g, enough correlational evidence has accumulated that it is indisputable that some characteristics of brain functioning and anatomy correlate with g. The claim that some people (e.g., Gould, 1996) make that intelligence does not relate to the biology of the brain is completely at odds with decades of neuroscience research. What is most astonishing about these correlations is that they exist at all. Intelligence tests are not designed with the goal to produce scores that correlate with brain size or neuron density – but they do anyway. This is extremely strong evidence that g is real and that it is a product of the biology of the brain (Jensen, 1998).

From "In the Know: Debunking 35 Myths About Human Intelligence" by Dr. Russell Warne (2020)

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