“Writing Is a System” debunks the popular view that writing is an art, best learned by reading selections of good writing and practicing composing. Instead, writing is a system that involves understanding what factors make sentences seem easy to read and paragraphs well organized. This chapter also examines the relevance of readability scores in assessing writing.

This chapter provides an overview of the purpose of the book, namely to help the user of public opinion data develop a systematic analytical approach for understanding, predicting, and engaging public opinion. This includes helping the reader understand how public opinion can be employed as a decision-making input, meaning a factor, or variable, to assess, predict, or influence an outcome. The chapter outlines how information from different disciplines, including cognitive psychology, behavioral economics, and political science, come together to inform the pollster’s work.

This chapter develops a theory of Homeric creativity that is informed by both contemporary cognitive studies and by ancient ideas about poetic craft and divine inspiration, as seen through the lenses of archaic poetry and Plato’s Ion. Sections 1-2 survey the conceptions of the poet as a craftsman vs. divinely inspired in Homer, Hesiod, Archilochus, Pindar, Democritus, and Plato. Section 3 introduces the modern study of creativity in the field of cognitive psychology, the study of jazz improvisation, and the concept of flow. Section 4 introduces the neuroscience of improvisation, including recent fMRI studies on jazz and lyric improvisation. The results of these studies (pointing to a state of hypofrontality when carrying out complex improvisational tasks) are compatible with some ancient ideas on creativity and inspiration (such as the idea that an external source - like the Muses- might be involved in these creative acts).

Chapter 1 starts with examples of typical child-instigated explorations in science, highlighting the importance of EC education as a whole and of developmental and cognitive psychology. This chapter describes children’s wonder and curiosity towards the world as it outlines what science looks like in the early years. As part of the definition of science, the chapter introduces conceptual, procedural and attitudinal science knowledge, and looks at how these relate to young children’s learning of science.

This chapter discusses one of the most basic putative cognitive kinds, concept, arguing that it should be considered a real kind based on our current state of knowledge, contrary to what some philosophers have urged. After surveying empirical work on concepts in cognitive neuroscience and cognitive psychology, the chapter tries to show that this work is pitched at different levels of explanation. Much recent work on concepts using neuroimaging techniques should not be expected to reveal the neural correlates of concepts because the research has different explananda and is investigating different causal processes. Other work on concepts in cognitive science reveals psychological structures (prototypes) associated mainly with automatic processing rather than deliberative reasoning. By contrast, concepts proper can be understood as functional kinds, which are individuated partly etiologically and partly with reference to the thinker’s discriminatory and inferential abilities. Many research programs in cognitive science individuate concepts in this way, combining diachronic and synchronic factors.

The excitement of cognitive psychology as a viable paradigm for contemporary research and application developed from a number of trends deeply imbedded in psychology’s past. In the twentieth century, these influences were clearly evident among the functionalists, the Gestalt movement, Tolman’s purposive behaviorism, as well as in the extended subfields of psychology, such as developmental, social, personality and clinical. In the latter part of the twentieth century, such specified advances as Bartlett’s schema theory, Hebb’s neural networks, and Broadbent’s filter model of attention provided cognitive psychology with substantive direction. The question of artificial intelligence contributed to a paradigm shift through the efforts of pioneers such as Turing and the logical theorist studies of Newell, Shaw and Simon, leading to what is described by some as the cognitive revolution. The seminal research of George Miller, Jerome Bruner, and Ulric Neisser gave cognitive science its form and substance. The question of the sustainability of the cognitive paradigm remains a topic for reflection.

Working memory (WM) is our limited-capacity storage and processing (memory) system that permeates essential facets of our cognitive life such as arithmetic calculation, logical thinking, decision-making, prospective planning, language comprehension, and production. Since the very inception of WM in the early 1960s (Miller et al., 1960), its role in language acquisition and processing has been extensively investigated both empirically and theoretically by researchers from diverse fields of psychology and linguistics, accumulating an increasingly huge body of literature (e.g., see Baddeley, 2003; Gathercole & Baddeley, 1993 for reviews of early studies). Notwithstanding, the field still lacks a comprehensive and updated profile of conceptualizing and implementing working memory in the broad domains of native and second language acquisition, processing, impairments, and training. In this chapter, we introduce a comprehensive handbook in which key areas of inquiry and practice in working memory and language are at the forefront and theoretical ingenuity and empirical robustness are integrated throughout.

To the extent that we can make education a science, we will gain some power to predict future directions for educational improvements. This chapter begins with quotations from some famous people that indicate that in the past, we have not learned from our mistakes. If we can succeed in creating a viable science of education and apply this in all educational settings, we may change the course of history in a positive way. This chapter presents a critique of some of the things we have done, and a description of more promising alternatives.

The chapter begins with a description of the first chance experience that shaped the future of my career, a meeting with a former Cornell PhD student, Bruce Dunn, who was interested in collaborating on research and invited me to do a sabbatical leave at the University of West Florida in 1987-1988. This in turn led to conversation with Dunn’s friend, Kenneth Ford, a new faculty member interested in artificial intelligence. We found that the use of concept mapping was highly facilitated for capturing expert knowledge in a fashion that rendered the knowledge easily applied in artificial intelligence settings. Ford became the director of the Institute for Human and Machine Cognition (IHMC) and he invited his friend, Alberto Cañas, to serve as associate director and to lead a team to create computer software for making concept maps electronically. We soon had available to us software that would work on almost any computer and that would not only allow the construction of concept maps, but also permit attaching digital resources to any map that could be accessed by simply clicking on icons on individual concepts. The software suite created became known as CmapTools, and this software suite is now used all over the world in virtually every field where organized knowledge is important.

In part to illustrate the slow progress in secondary school facilities and programs, I introduce findings from a study done some 50 years ago. Most of the positive changes that occurred in the last 100 years are the result of an occasional creative administrator or school leader. To the best of my knowledge, none of these innovations were introduced on the basis of a comprehensive theory of education. I present evidence to suggest that this situation is changing.

The chapter begins by addressing the question: Why do young children learn so quickly? The short answer is that they are learning names for objects and events they are experiencing directly. These words are concept labels and they are engaged in what we call meaningful learning. In contrast, school learning is too often rote learning where the concepts and principles children are learning are not related to direct experiences with objects and events. David Ausubel’s cognitive psychology was introduced in 1963 and we immediately applied this new psychology as the foundation for all of our future work. We rejected totally the behavioral psychology that had dominated the field of education for some one hundred years. We also rejected positivist epistemology in favor of the emerging constructivist epistemology. It was not until the late 1980s that cognitive psychology and constructivist epistemology became widely adopted.

This chapter opens with the question: Can education become a science? I seek to answer to answer this question by asserting that education is a human activity and like any other human activity, it can be studied scientifically. This means that we can construct concepts, principles, and theories that explain how human beings acquire, use, and construct new knowledge. A comprehensive theory of education must address the question of the nature of knowledge and how human beings build new knowledge, and how to organize education to facilitate these processes. I argue that the major problem with education in the past has been the use of faulty theories of learning and invalid theories of knowledge and knowledge creation, resulting in inadequate instructional practices.