This chapter aims to explore how intelligence research – both current evidence as well as potential new findings that remain undiscovered – might inform education and public policy. We will first address why studying human intelligence is not only an exciting area of research for basic discovery, but also how knowledge about intelligence might be applicable to education and public policy. We will review selected areas of intelligence related research with potential implications for policy. We will discuss research on intelligence test scores as predictors of school performance and later success and research on features of the most promising policy relevant variables for improving intelligence. Finally, we will conclude with a discussion and explanation for researchers about how influencing policy requires (1) learning from policy researchers and practitioners, who have expertise that we argue complements the strengths of intelligence researchers and (2) effectively communicating those findings to policy researchers and practitioners. We write this chapter primarily from the perspective of researchers who are engaged in intelligence research that might be applied to education and policy discussions.

The search for the biological properties that underlie intelligent behavior has held the scientific imagination at least since the pre-Socratic philosophers. Early hypotheses posited a crucial role for the heart (Aristotle; Gross, 1995), the ventricles (Galen; Rocca, 2009), and the “Heat, Moisture, and Driness” of the brain (Huarte, 1594). The advent of neuroimaging technology such as EEG, MEG, and MRI has provided more suitable tools to scientifically study the relationship between mind and brain. To date, many hundreds of studies have examined the association between brain structure and function on the one hand and individual differences in general cognitive abilities on the other. Both qualitative and quantitative reviews have summarized the cross-sectional associations between intelligence and brain volume (Pietschnig, Penke, Wicherts, Zeiler, & Voracek, 2015), as well as more network- and imaging-specific hypotheses which suggest a key role for the frontoparietal system in supporting individual differences in intelligence (Basten, Hilger, & Fiebach, 2015; Deary, Penke, & Johnson, 2010; Jung & Haier, 2007). These findings are bolstered by converging evidence from lesion studies (Barbey, Colom, Paul, & Grafman, 2014), cognitive abilities in disorders associated with physiological abnormalities (Kail, 1998), and the neural signatures associated with the rapid acquisition of new skills (Bengtsson et al., 2005).

The goal of this chapter is to provide an overview and critique of the major theories in the cognitive neuroscience of intelligence. In taking a broad view of this literature, two related themes emerge. First, as might be expected, theoretical developments have generally followed improvements in the methods available to acquire and analyze neural data. In turn, as a result of these developments, along with those in the psychometric and experimental literatures, cognitive neuroscience theories of intelligence have followed a general trajectory that runs from relatively global statements early on, to increasingly precise models and claims. As such, following Haier (2016), it is perhaps most instructive to divide the development of these models into early and later phases.

The purpose of this chapter is to review key principles and findings of intelligence research, with special attention to psychometrics and neuroscience. Following Jensen (1998), the chapter focuses on intelligence defined as general intelligence (g). g represents variance common to mental tests and arises from ubiquitous positive correlations among tests (scaled so that higher scores indicate better performance). The positive correlations indicate that people who perform well on one test generally perform well on all others. The chapter reviews measures of g (e.g., IQ and reaction times), models of g (e.g., Spearman’s model and the Cattell-Horn-Carroll model), and the invariance of g across test batteries.

Most humans can perceive the world, store information in the short- and the long-term, recover the relevant information when required, comprehend and produce language, orient themselves in known and unknown environments, make calculations of high and low levels of sophistication, and so forth. These cognitive actions must be coordinated and integrated in some way and “intelligence” is the psychological factor that takes the lead when humans pursue this goal. The manifestation of widespread individual differences in this factor is well documented in everyday life settings and has been addressed by scientific research from at least three complementary models: psychometric models, cognitive/information-processing models, and biological models.

It is useful to consider three very general approaches to enhancing cognitive functions such as attention, memory, or problem solving (Tang & Posner, 2014). One is training a specific brain network by practice on a task that uses that network (Network Training). Attention and working memory have been two of the most widely used tasks for studying network training. Another approach to enhancement involves a change in brain state by use of physical exercise, meditation, drugs, or playing video games (Brain State). A third approach involves the use of external electrical or magnetic stimulation to activate or inhibit brain pathways (Brain Stimulation). Recently studies have examined these methods in combination (Daugherty et al., 2018; Ward et al., 2017). In this chapter we review examples of each approach designed to improve cognition, related criticisms, and opportunities for further research and application.

Since the dawn of intelligence research, it has been of considerable interest to establish a link between intellectual ability and the various properties of the brain. In the second half of the nineteenth century, scientists such as Broca and Galton were among the first to utilize craniometry in order to investigate relationships between different measures of head size and intellectual ability (Deary, Penke, & Johnson, 2010; Galton, 1888). However, since craniometry can at best provide a very coarse estimate of actual brain morphometry and adequate methods for intelligence testing were not established at that time, respective efforts were not particularly successful in producing insightful evidence. About 100 years later, technical developments in neuroscientific research, such as the introduction of magnetic resonance imaging (MRI), enabled scientists to assess a wide variety of the brain’s structural properties in vivo and relate them to cognitive capacity. One of the most prominent and stable findings from this line of research is that bigger brains tend to perform better at intelligence-related tasks. Meta-analyses comprising a couple of thousand individuals have reported correlation coefficients in the range of .24–.33 for the association between overall brain volume and intelligence (McDaniel, 2005; Pietschnig, Penke, Wicherts, Zeiler, & Voracek, 2015). A common biological explanation for this association is the fact that individuals with more cortical volume are likely to possess more neurons (Pakkenberg & Gundersen, 1997) and thus more computational power to engage in problem-solving and logical reasoning.

Flexibility is central to human intelligence and is made possible by the brain’s remarkable capacity to reconfigure itself – to continually update prior knowledge on the basis of new information and to actively generate internal predictions that guide adaptive behavior and decision making. Rather than lying dormant until stimulated, contemporary research conceives of the brain as a dynamic and active inference generator that anticipates incoming sensory inputs, forming hypotheses about that world that can be tested against sensory signals that arrive in the brain (Clark, 2013; Friston, 2010). Plasticity is therefore critical for the emergence of human intelligence, providing a powerful mechanism for updating prior beliefs, generating dynamic predictions about the world, and adapting in response to ongoing changes in the environment (Barbey, 2018). This perspective provides a catalyst for contemporary research on human intelligence, breaking away from the classic view that general intelligence (g) originates from individual differences in a fixed set of cortical regions or a singular brain network (for reviews, see Haier, 2017; Posner & Barbey, 2020).

The brain’s remarkable inter-individual structural variability provides a wealth of information that is readily accessible via structural Magnetic Resonance Imaging (sMRI). sMRI enables various structural properties of the brain to be captured on a macroscale level – one that is quickly moving towards submillimeter resolution (Budde, Shajan, Scheffler, & Pohmann, 2014; Stucht et al., 2015). This constitutes a remarkable leap forward from historically crude brain measures, such as head circumference measurements, aimed at understanding the neurobiology of intelligence differences.

In his 2011 book, Incognito, Stanford neuroscientist David Eagleman asked us to:

Play fighting in rats is used to show how the four principles can be used to characterise the organisation of the behaviour and then select behavioural markers that can be scored numerically. The partners compete to access the nape of each other’s neck and the behaviour patterns used during these encounters are derived from adult sexual encounters. Body size and agility can affect which tactics are used as can the location in the enclosure in which an encounter takes place. Taking these factors into account reveals that some actions cannot be explained as being compensatory to either gaining or avoiding nape contact. This, in turn, reveals novel aspects of organisation of play fighting and leads to identifying novel behavioural markers to measure those aspects of organisation.