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All the preceding chapters have led to this one fundamental question: can intelligence be increased? It is a simple question, but what exactly does it mean? As discussed in Chapters 2 and 3, from a scientific standpoint, intelligence can mean an assessment score (from a reliable and valid standardized test), a broad factor (like verbal, visuospatial, or perceptual ability), and the general factor common to all mental abilities (the g-factor). The measured performance on any given cognitive test results from the contribution of g, the specific ability tapped by the test, and the specific skills required for such a test. Therefore, when we observe an increase in the measures we administer, the change can be at the test, the ability, or the g level. An increase can be small, albeit statistically significant, or large enough to have a measurable effect on a relevant outcome variable like educational achievement or job performance. An increase can be temporary or long-lasting. In this chapter, we mean something potentially more interesting than an increase in IQ scores, something that is more permanent, and something that impacts g. As you were reading other chapters, perhaps you considered questions like the following:
Is there anything I can do to be more intelligent?
Can intelligence be increased beyond a person’s genetic potential?
Is there a theoretical limit on just how smart any individual can become?
Do children and adults have an inner genius that can be unlocked?
The desire to enhance intelligence dramatically is as ancient as alchemy. So far, this goal is just as elusive as turning lead to gold – but is it even possible that any of these questions can be answered in the affirmative?
We begin our study of rotational motion with the definitions and detailed examination of the fundamental quantities which we will use throughout this book. We then proceed with a description of kinematics in rotational motion by drawing analogies from our knowledge of one-dimensional kinematics in linear motion.
In this chapter, we address several fundamental issues that are important for learning about the scientific concept of intelligence. Many of the arguments about intelligence are less informative than they might be. Often, disagreements are misdirected because concepts of intelligence differ, and there is confusion about the relation between test scores and intelligence. Here we provide a framework for constructively discussing theories, models, and facts about intelligence.
Many of the facts discussed in this book may come as a surprise or even a shock, given that there is so much misinformation about intelligence in high school courses, among teaching faculties in colleges and universities, in the workplace, and in the media. It is quite all right to be skeptical, and we encourage it for every page of this book, but the weight of evidence presented in each chapter supports the following basic statements, which have been replicated across decades and reinforced by recent research:
1. Intelligence can be defined for scientific investigations.
2. Standard measures of intelligence provide quantitative assessments of individual differences for rigorous statistical analyses despite acknowledged limitations.
3. Standard intelligence tests have the required reliability and validity for scientific study.
4. Standard tests of mental ability, including IQ tests, are not biased against any population when administered and interpreted properly.
5. The general factor of intelligence (g), especially when assessed by a diverse battery of mental ability tests, is the single most predictive construct in psychology for a wide variety of real-world relevant social outcomes.
6. Measures of g are related to a number of quantifiable brain features that appear to have developmental sequences.
7. Individual differences in intelligence are influenced by genetics, although details at the molecular level are just beginning to be investigated. Nongenetic factors are also relevant, but there also is no clear model of the mechanics of their impact (although the mechanisms must be biological and ultimately influence the brain).
8. The sources of average population differences for IQ and other measures of mental ability remain unknown, but science is making progress disentangling purported influences.
9. So far, there is no proven way to increase the general ability to integrate cognitive abilities (the g-factor), but the possibility is at an exciting frontier of neuroscience and molecular genetic research.
10. More intelligence is no guarantee of being a better person in any sense, but perhaps enhanced intelligence will help our species solve long-standing global problems.
This is a short list that is to the point. Much of what we know about intelligence is represented in this book, but each chapter also shows how much we have yet to learn. There are still psychometric issues, and the goal of a ratio scale intelligence test remains elusive. Neuroimaging is verifying the roles that brain structure and function play in cognitive differences. The demonstration that not all brains work the same way potentially has profound implications for education and social policy, especially if neuroimaging data can predict outcomes as well as or better than currently available standardized intelligence measures. Applying this kind of neuroscience information has barely been attempted anywhere. Perhaps the most challenging information to process is the rapidly advancing genetic research. When it comes to complex traits like intelligence, some see genetics as limiting, but the fact is that genes are probabilistic, not deterministic. Moreover, understanding genes and how they function can unlock tremendous opportunities to facilitate and enhance rather than limit human behavior. As genetic studies, especially based on DNA technology, begin to answer old questions about intelligence, an informed public is vital for ethical discussions about how new knowledge about what intelligence is and where it comes from can be used to make life better.
The preceding chapter presented severalexamples of perturbations from nominal operationof power electronic systems. These examplesmotivated the need for dynamic modeling of powerconverters, in order to understand suchperturbations and to design appropriatecontrollers. We introduced the idea of circuitaveraging as a means of obtaining simple andinformative dynamic models in circuit form.
Angular displacements that occur about the same axis or parallel axes, on the other hand, do follow the commutative law. Also, infinitesimally small angular displacements are commutative. To avoid confusion, we will treat all finite angular displacements as scalar quantities. However, we will have occasion to treat infinitesimal angular displacements as vectors.
The journey through rotational motion is not quite done. In fact, we are just beginning. This chapter introduces a few topics which would be covered in an intermediate-level mechanics course. The topics include more advanced physical phenomena, such as gyroscopic precession, and the mathematical formalism of parameterizing rotations using matrices.
This book is about the nature of intelligence, its causes and uses, and why it differs among people. Scientific psychology has much to say about intelligence, but unfortunately, much that has been said is misunderstood.
These are statements about the importance of intelligence for everyday life. They seem consistent with our own experiences, but they also could imply a cognitive elitism that many find uncomfortable. Nonetheless, compelling data support these statements and imply that everyday life can be seen as one long continuous intelligence test, and that the test is getting harder as modern life becomes more complex (Gottfredson, 1997; Gordon, 1997; Hunt, 1995).
In this chapter we describe methods used tomodel magnetic components in both the electricaland magnetic domains. Magnetic components such asinductors, coupled inductors, and transformers lieat the heart of most power electronic circuits.Multi-port components especially can exhibit quitecomplex behavior, and advancing the performance ofpower electronic circuits often relies onunderstanding and leveraging this behavior.
In analyzing motion, the first and most basic problem encountered is that of defining and dealing with the concepts of position, posture, and displacement. Since motion can be thought of as a series of displacements between successive positions of a point or postures of a body, it is important to understand exactly the meaning of the terms position and posture. Rules or conventions are established here to make the definitions precise.
The theory of machines and mechanisms is an applied science that allows us to understand the relationships between the geometry and motions of the parts of a machine, or mechanism, and the forces that produce these motions. The subject, and therefore this book, divides itself naturally into three parts. Part I, which includes Chapters 1–5, is concerned with mechanisms and the kinematics of mechanisms, which is the analysis of their motions. Part I lays the groundwork for Part II, comprising Chapters 6–10, in which we study methods of designing mechanisms. Finally, in Part III, which includes Chapters 11–16, we take up the study of kinetics, the time‑varying forces in machines and the resulting dynamic phenomena that must be considered in their design.
The concept of mass in linear motion was quite simple. However, the rotational analog, the moment of inertia, is comparatively complicated. In this chapter, we present a thorough introduction to the moment of inertia, and we develop the tools needed to compute this quantity for point masses, systems of discrete masses, and continuous rigid bodies about different axes of rotation. The chapter ends with some useful theorems that allow us to extend the application of these fundamental tools.