Mike Anderson

In this chapter, the authors aim to detail Annette’s influence on their attempts to shed light on key theoretical questions concerning the nature of intelligence. A great amount of research, some undoubtedly inspired by Annette’s work, has produced a great amount of data evidencing differences in the capabilities of children at various ages. However, Annette was crystal-clear on this point: taking development seriously does not mean simply that one must study children – it is not about cataloguing what children can and cannot do at different ages. Though certainly a troubled history, the study of intelligence has nevertheless revealed important empirical facts that beg the development of better theories. Next, the authors briefly outline these facts and describe why the time has come for the discipline of psychology to finally engage with the core construct of intelligence – g.

We begin with an apology. Our book title leads with ‘Neuroscience in education’ but after reading this book you might be forgiven for thinking this is deceptive. Our stance is that the scientific domain that has most to offer education is the study of cognition and that neuroscience itself has qualified value. Yet there is no intention to deceive in our use because it has entered the lexicon as the term that refers to the interaction of education and brain sciences (including cognitive psychology). For example, this is testified by the title of recent reports on the topic by the Royal Society (Neuroscience: implications for education and lifelong learning) and the ESRC (Neuroscience and education: issues and opportunities) and many current publications (Education and neuroscience—Howard-Jones, 2009; The brain at school: educational neuroscience in the classroom, Geake, 2009) and indeed these and many other contributions have spawned the new label of neuroeducation. So our book is targeted fairly and squarely at the centre of this new field. But the first and most important lesson we have learned in putting the book together is that while the use of the term ‘neuroscience’ is attractive for education it seems to us that it is cognitive psychology that does all the useful work or ‘heavy lifting’. The reason for this is straightforward. We believe that for educators, research indicating that one form of learning is more efficient than another is more relevant than knowing where in the brain that learning happens. There is indeed a gap between neuroscience and education. But that gap is not filled by the ‘interaction’ of neuroscientists and teachers (nearly always constituted by the former patronizing the latter) or ‘bridging’ the two fields by training teachers in basic neuroscience and having neuroscientists as active participators in educating children. Rather what will ultimately fill the gap is the development of evidence-based education where that base is cognitive psychology. Of course this is not an uncontested view and some of our contributors disagree, but ultimately it is for you the reader to draw your own conclusion.

This chapter provides a whistle-stop tour of the major issues and concerns in this meeting of neuroscience and education. The main goal is to draw out what it is about neuroscience that is so beguiling for educators. It may be something as simple as this little syllogism: education is about children's learning; the brain is responsible for learning; and neuroscience (the scientific study of the brain) is fundamental to education. Yet the way learning is instantiated in brain processes, and even more pertinently how the brain develops, and how either relates to the way children are best educated, is more akin to speculation than scientific hypothesis — never mind fact. Be that as it may, it is clear to us that neuroscience will only grow in influence.

This chapter examines the effect of aging on cognitive abilities and asks whether all cognitive abilities show evidence of decline or whether some abilities are relatively spared. Declines in specific abilities would be lacunae of loss sparing the archipelagos of surviving abilities. This chapter looks at evidence from the perspective of maintenance or loss of consistency of relationships between mental abilities as age advances. Analysis of cross-sectional data from a large elderly sample showed age impaired performance on all tests that individuals were given, with the exception of some vocabulary tests. However, the amounts of total variance in test performance associated with differences in age between forty-nine and ninety-two years were very modest-not exceeding 22%. Further analysis of the data set showed that levels of correlations between scores on intelligence tests and other tests of fluid intelligence, between intelligence tests and other cognitive tests, and between other cognitive tests are consistently higher in a sample aged from seventy to ninety-two years than in a sample aged from forty-nine to sixty-nine years.

This chapter will argue that an understanding of the nature of general intelligence and how it influences cognitive development is vital to an understanding of the majority of developmental disorders. There are two main reasons for this: one is empirical and the other theoretical. The empirical reason, while dull, is very important. Most developmental disorders are diagnosed with reference to a discrepancy in levels of performance from that predicted by the general intellectual functioning of the child. This discrepancy criterion may be obscuring our understanding of these disorders for reasons that I will explain below (Dyck et al., 2004). The theoretical reason is anything but dull. If it is true, as I will argue in this chapter, that understanding developmental disorders requires models that explicitly represent the influence of general intelligence on specific cognitive functions, then this will necessitate a major shift in approach from most who currently investigate a particular disorder, A benefit of grasping this particular nettle, however, is that the comorbidity of developmental disorders, something that is the bane of those researchers who like things neat and tidy, might be put in its proper context.

There are false beliefs widely held by many, and often by those who should know better, regarding the influence of race and biology on IQ and intelligence. This chapter presents arguments and data that challenge these myths by establishing that: IQ tests are good measures of intelligence; there is a large unitary factor called general intelligence that accounts for most of the differences in IQ and that is based on biological differences between individuals; individual differences in IQ are, in large part, genetically inherited; and there is no good reason to believe that the difference in group means that exist between black and white Americans in measured IQ is either genetically based or based on race differences in biology.

It has sometimes been uncritically assumed that involution mirrors development, so that individuals regress through developmental stages in a sort of inverse ‘decalagc’, A useful antidote is to remember that the intellectual and methodological problems of describing growing up and aging are very different. Understanding how children manage to acquire cognitive skills and modes of representation of the world that they could not previously attempt requires a quite different intellectual approach from understanding how older people cease to be capable of skills and modes of representation at which they once were superbly competent. However recent, reductionist general models for cognitive changes throughout the lifespan, while ignoring questions of changes in representational structure and skill acquisition and loss, propose that at any stage in the lifespan attainable levels of competence at all cognitive skills is limited by the current level of a single global factor which increases with developmental age, maintains a long plateau at maturity, and ebbs in senescence. Simplistic versions of this idea have directly equated this resource with a single, measurable performance index: the maximum speed with which individuals can make correct decisions in easy laboratory experiments. An attraction of this approach has been that it seems to provide a way of linking empirically measurable behavioural competence to potentially measurable functional property of the cognitive system and even to neurophysiological efficiency, providing, as one author his put it. a biological basis for intelligence’ (Eysenck, 1986). This chapter considers the historical evolution and current plausibility of this general model in three separate fields of research: individual differences in general intelligence, cognitive ageing and developmental psychology.

This chapter uses a particular hypothesis from research on individual differences in cognitive ability—the differentiation hypothesis—to show that individual differences can he informative for cognitive models of the mind. The differentiation hypothesis comes in two forms. The developmental differentiation hypothesis argues that as children develop their abilities become more differentiated and that as adults age their abilities become de-differentiated. The individual differences differentiation hypothesis states that abilities are more differentiated at higher IQ. Differentiation is usually inferred from either a smaller g-factor or a lower average inter-test correlation. Simulations of alternative models that specify different functional relationships between processes underlying the g-factor and specific abilities are presented. They reveal that empirical outcomes are likely to be sensitive to nontrivial assumptions about the precise relationships between the hypothetical processes. In particular, a common but simple interpretation of the apparent de-differentiation of abilities with advancing age, and increasing differentiation with development in children, is that a single common factor underlies both g and developmental change (e.g. speed of processing). The simulations reveal that this simple interpretation is unwarranted. Evidence from the analysis of two datasets (elderly adults and young children) confirms this conclusion.