This is a conceptual chapter (i.e., no coding) that introduces the components of stock-and-flow models and then investigates different types of expected system growth (linear, exponential, logistic, as well as overshoot) and common causes of each. The role of feedbacks in complex systems is introduced.

This chapter explores how state evolution impacts uncertainty in classical systems using majorization and related principles.

In Chapter 1, I explain how the book can be read and used in a nonlinear fashion, providing affordances for further exploration, comparable to the way the book approaches the creation and experience of works of art. The chapter proceeds to present a detailed advance organizer in the form of a point-by-point overview of the main messages and ideas of this book, providing a framework for the way the book can be read and used.

This chapter presents a summary of the career of Richard M. Lerner within the field that, during his professional life, changed its label from child psychology to developmental science. Lerner summarizes the influence of the scholars and the ideas they championed in shaping the breadth of his career, from his days as a doctoral student at the City University of New York in the late 1960s and early 1970s to the more than a quarter century he spent at Tufts University, where he served as the Bergstrom Chair in Applied Developmental Science and Director of the Institute for Applied Research in Youth Development. Lerner explains how the idea of mutually influential coactions between individuals and contexts (i.e., individual⬄context relations) found its roots in the comparative psychology work of T. C. Schneirla and evolved to become embedded in relational developmental systems-based models that emphasize the dynamic character of human development across the life span.

This introductory chapter delves into the inception of developmental cognitive neuroscience, a field shaped by historical inquiries into brain development, childhood learning, and the nature–nurture debate. We trace the origins of this interdisciplinary endeavor, revealing how it has emerged as a pioneering approach to comprehending human development. In this chapter, we dissect the core components of developmental cognitive neuroscience: development, cognition, and neuroscience. We elucidate their interconnectedness, underpinning theories, and evolving methodologies, spotlighting the transformative impact of recent technological strides. Throughout the book, our emphasis remains on the synthesis of these elements, illustrating their collective role in advancing our comprehension of human development. This chapter establishes the groundwork for an engaging exploration of the intricate interplay between brain maturation, cognitive processes, and the unfolding of human potential.

This chapter probes the conceptual architecture of irritability in the eighteenth century. It justifies this case study not through a pre-established research agenda but because automated statistical comparisons reveal a marked transformation both in the term itself and in the broader network in which it is embedded. Irritability has long been marginalised in favour of its sister term, sensibility; yet we demonstrate the abiding significance of the former, in a variety of canonical works (Erasmus Darwin, Edmund Burke) and less familiar medical handbooks. This largely overlooked medical discourse infuses broader thinking on gender, colonialism and aesthetics; it worries the distinction between human and non-human life. We conclude by proving that the emergence of the irritability network holds significant consequences for other forms of conceptual thinking. In particular, we show how it affords a rethinking of the notion of habit, and facilitates the transformation of the cultural concept of system from a largely Newtonian and mechanistic notion, at the beginning of the eighteenth century, to an increasingly dynamical and physiological entity.

This chapter reviews contemporary computational models of psychological development in a historical context, including those based on symbolic rules, artificial neural networks, dynamic systems, robotics, and Bayesian ideas. Emphasis is placed on newer work and the insights that simulation can provide into developmental mechanisms. Within space limitations, coverage is both sufficiently broad to provide a general overview of the field and sufficiently detailed to facilitate understanding of important techniques. Prospects for integrating the dominant approaches of neural networks and Bayesian methods are explored. There is also speculation about how deep-learning networks might begin to impact developmental modeling by increasing the realism of training patterns, particularly in visual perception.

Nearly everyone thinks that it’s your brain, and how it varies from the brains of others, that defines your intelligence as an individual. The terms ‘brainy’ and ‘intelligent’ are used almost interchangeably. If you really want to know about intelligence then you need to know about the brain. You may come across questions like ‘How does the brain give rise to intelligence?’ or ‘Where does intelligence reside in the brain?’. It is generally believed that knowing more about the brain will tell us more about intelligence, and much else, including human nature itself.

Aldous Huxley was not alone in pointing to ‘the most incredible miracles happening all around us … a cell in nine months multiplies its weight thousands and thousands of times and is a child’. Indeed, development strikes everyone as a wonderful, but mysterious, transformative process in which an insignificant speck of matter becomes a coherent, functional being. It all seems so automatic as to look like magic.

Intelligent systems have been a most crucial part of evolution. They furnished adaptability in complex, changing environments. As evolved in humans, our socio-cultural intelligence fostered the construction of shared worlds far beyond the inputs of our individual senses. That has allowed us to adapt the world to ourselves, rather than vice versa, as in all other species.

The dominant concept of intelligence is based on IQ, which is based, in turn, on the concept of the gene. Indeed IQ testing is very largely rooted in that concept. So, if I am trying to change the concept of intelligence in this book (which I am) it’s obvious that we must first tackle the concept of the gene.

The ideology surrounding intelligence has been two-fold. First, it has aimed to convince us that the social order is a consequence of immutable biology – that inequalities and injustices are natural and cannot be eliminated. Second, where problems cannot be ignored, it tells us to look for solutions at the level of the individual rather than the level of society. Undoubtedly, the story has been phenomenally successful. Nearly everyone, across the political spectrum and around the world, accepts it to some extent. A 2020 paper from the Foundation for European Progressive Studies supports that view. It reports a European survey of attitudes of the most affluent individuals to social inequalities. Although hard work and having a supportive family background are mentioned, educational aptitude and being ‘academically bright’ or intelligent are cited as the primary factors.

When people consider intelligence, they will first tend to think of IQ, and scores that distinguish people, one from another. They will also tend to think of those scores as describing something as much part of individuals’ make-up as faces and fingerprints. Today, a psychologist who uses IQ tests and attempts to prove score differences are caused by genetic differences will be described as an ‘expert’ on intelligence. That indicates how influential IQ testing has become, and how much it has become part of society’s general conceptual furniture.

Whether they believe in IQ or not, most people sense that individual differences in intelligence are substantial and at least partly ‘genetic’. The nature–nurture debate about the origins of such differences goes back a long way; at least as far as the philosopher-scientists of Ancient Greece. And most people have probably adopted common-sense views about it for just as long. It is evident today in popular cliches: our genetic blueprints set levels of potential, while nurture determines how much of it is reached; individual differences result from both genes and environments; genes and environments interact to determine individual differences; and so on.

Charles Darwin’s On the Origin of Species is a delightfully sophisticated account of evolution. But the core ideas are not that difficult to understand. Variations in traits in individuals arise by chance, due to what we now think of as mutations in genes. Some of those trait variations are functionally better adapted to part of the environment than others. Individuals so advantaged will tend to survive and leave more offspring. Accordingly, the advantage, and the frequency of the genes causing it, will increase from generation to generation. Conversely, genes causing less advantageous or harmful variations will decrease in frequency. That is natural selection.

So far I have tried to show how intelligence evolved at different levels according to the complexity of the environments faced. We have just seen how the breakthrough to cognitive intelligence emerged from the chatter between neurons in large networks. In this chapter, I show how human evolution involved another, even more stunning, breakthrough in a way not fully appreciated but fully consistent with biological principles. As with intelligent systems generally, it emerged from social interaction at a number of levels, not lucky genetic accidents.