Intelligence

Reconceptualization and Measurement

Helga A.H. Rowe

• Publisher: Psychology Press
• Year: 2013
• eBook ISBN: 9781134764617
• Topic: Psychology
• Subtopic: Cognitive Psychology & Cognition
• Index: Psychology

As reform in all sectors of education continues, it is becoming increasingly important that we develop a rich understanding of what "intelligence" is, and how it can be improved. Reflecting current views on the manifestation, development, and assessment of human intelligence, this volume addresses a rich diversity of theoretical, methodological, and applied issues -- a number of which have not been raised previously. The contributors to this collection -- highly regarded experts from various countries -- propose perspectives for future research, their intent being not so much to predict the future, but to help shape it.

1

Introduction: Paradigm and Context

Helga A.H. Rowe

Australian Council for Educational Research

This book deals with theoretical issues relating to human “intelligence,” its manifestation, development, and measurement. It focuses on intelligence because intelligence plays an important part in learning, involves higher-order thinking, problem solving, and creativity, and because of its importance to an individual’s performance and personal success in everyday life. As reform in education continues, it is becoming particularly important that we develop a rich understanding of what intelligence is and how to foster it.

The authors call attention to some questions that have not been raised previously, and to many of the important and relevant issues that must be addressed in the study of cognition and in more general psychological research. One purpose of this book is to propose perspectives for future research, not so much with the intent of predicting it as with that of shaping it. However, perhaps to the dismay of some readers, the contributors to this book are not providing an agenda for future research. To suggest a single intellectual path or set of paradigms to guide research on intelligence is impossible, and if it were possible would probably be counterproductive. A forward move in intelligence research demands the reflection on its many facets and research from a variety of perspectives, which, hopefully, in the end might lead to certain syntheses.

Intelligence research in the 1980s might go down as a decade of publications that decried the quality of theories, and questioned the usefulness of the concept of intelligence and its measurement. This book is not about new theories. The term reconceptualization is used in the sense of “re-visiting” and “re-orientation” with the intention to come to know again and gain a new understanding of the processes underlying human intellectual functioning, i.e., the development and operation of human intelligence.

As suggested by Kuhn (1962, 1970a, 1970b) paradigmatic change in scientific communities announces itself through the discussion and questioning of the prevailing paradigm. Thus, the 1980s should have been an interesting time for intelligence research, but were they? Have research approaches developed? Have promising fields opened up (especially by the challenges resulting from computer use)? It is doubtful that many would answer these questions in the affirmative.

Paradigm and paradigm shifts have certainly become major topics of discussion among researchers in psychology and in other disciplines of science and social science (for an overview see e.g., Jacob, 1987, 1988). It is important to note, however, that different meanings are being attached to the term “paradigm” by different authors within the same and within different disciplines. For the purposes of this chapter I am using the term paradigm in a metaphorical sense as reflected in the literature of contemporary philosophy of science and new paradigm thinking, rather than equating it, as may be more typical within psychology and education, with a model of inquiry (e.g., Popkewitz, 1984) or with theory (e.g., Torgesen, 1986). I view paradigm as the Weltanschauung of the researcher, i.e., the central overall way of regarding and relating to phenomena and in the context in which he/she lives and works.

Such a conceptualization of paradigm allows for questioning at a more fundamental level than is the case at the level of theory or of model of inquiry. Paradigm directs attention to the basics of our thinking about what is reality and how it becomes known, not just within scientific investigation, but in all of life. It includes perceptions, beliefs, values and practices that collectively are part of the decisions that researchers make about what counts as real, and it also includes how researchers share and disseminate what they have found to be real.

Paradigms give rise not only to theories and research methodologies but also to criteria for the selection of research questions and for their evaluation (Capra, 1986; Lakoff & Johnson, 1980; Lincoln & Guba, 1985; Ogilvy, 1986; Prigogine & Stengers, 1984; Sadawa & Caley, 1985). The paradigm represents “the ultimate benchmarks against which everything else is tested” (Lincoln & Guba, 1985, p. 15). It does not frame the phenomena of interest as theory does, but rather presents a way of seeing (and not seeing) phenomena and thinking (and not thinking) about the world. It contains the perceptions, beliefs and values that ultimately determine our thinking, actions and reactions. When we articulate our paradigm we make explicit how we think about, know, and come to understand the phenomena that engage us in any area of our lives. Paradigms describe our personal epistemology. The need to become aware of and articulate (at least to oneself) the characteristics of one’s ultimate benchmarks certainly arises when the paradigm one “unconsciously” lives by reaches its limitation (see also Capra, 1982, 1986; Lincoln & Guba, 1985; Prigogine & Stengers, 1984).

The prevailing paradigm in the natural and social sciences has arisen from the Newtonian, mechanistic, reductionist assumptions that have guided scientific and applied considerations since the rise of the natural sciences in the 17th century. However, evidence of a dawning awareness about the paradigmatic limitations within which we have worked are found in publications of an increasing number of scientists and philosophers of science during the 1980s (e.g., Berman, 1984; Bemstein, 1983; Capra, 1982; Hesse, 1980; Jantsch, 1980; Le-Shan & Margenau, 1982; Prigogine & Stengers, 1984; Wolf, 1981).

In the remainder of this chapter I (a) address the assumptions about the nature of reality and the nature of knowledge claims that underlie Newtonian mechanistic, reductionist thought in relation to intelligence and its measurement, (b) address the importance of theoretical re-orientation in the area of intelligence research, (c) outline some assumptions about what counts as real, and how we can know what is real within the emerging non-Newtonian, holistic paradigm and discuss the importance of these assumptions for research into intelligence and its application, and (d) introduce the chapters contained in this volume.

Intelligence and the Newtonian Paradigm

Mechanistic assumptions. Current approaches to the conceptualization of intelligence and to its measurement have been shaped largely by the tenets of the Newtonian mechanistic paradigm. The Newtonian assumptions rely on the metaphor of the machine initially set out by Descartes. Newton established that the universe operates as a celestial clockwork following precise, mathematically statable natural laws. Mechanistic theory holds that the same natural laws that direct inanimate events direct the behavior of living organisms. This leads to the postulation of a uniformity and predictability analoguous of a machine-like (mechanical) process taking place throughout nature. A machine can be broken up into its smallest functional parts, and the causal relationships between the parts can be stated with precision. Once the mechanism is known, the future functioning of the machine can be predicted and controlled. Finally, in case of breakdown it is likely that the machine can be repaired.

Simplicity. It is thus regarded as the foundation of the mechanistic paradigm. All complexity is broken down into components, which are to be operationalized as far as possible. The whole is to be understood by understanding the components, often arranged in so called logical and sequential order. In the Newtonian universe time and space are even and continuous, all movement is controlled by outside forces (assumptions that in psychology are reflected in stimulus control and systematic reinforcement theories), fact is separated from value, the observer is quite distinct from the observed, and knowledge is distinct from the person who has the knowledge. Personal meaning and context are irrelevant. Quantification, in terms of comparing what is being measured to an external standard, was the only means by which valid knowledge claims could be made. Certainty of knowledge is obtained by gathering sufficient data. Psychological phenomena came to be seen as observable or perceivable with one or more of the senses, measurable and quantifiable, and verifiable in that several observers should be able to agree on their existence and characteristics (Valle, 1981).

The machine metaphor has had and still has an enormous influence on psychologists’ conceptions of learning, development and other human behavior, and on the criteria used in the selection of research questions, research methodology, and theory building. The assumptions of the mechanistic reductionist paradigm are at the core of practices such as task analysis, isolated skills training, programmed learning, the establishment of behavioral objectives, etc. They lead to the assumption that causality can be established and that prediction, and control become possible. One result is a glut of predictive tests and the practice of diagnostic/prescriptive assessment and instruction.

The mechanistic paradigm has had a particularly limiting effect on the conceptualization of “intelligence” and its identification. It led to the narrow and dogmatic view that educationists and industry evaluate performance potential and practice on the basis of what they regard as scientific and objective knowledge. We all have tended to rely too heavily on measurable outcomes, identified largely through the use of standardized tests. Only standardized procedures satisfy our quest for certainty. Theoretical approaches such as psychometric-, behavioristic-, psychological process-, or cognitive strategies models all share a mechanistic reductionist heritage that embraces assumptions of the following kinds:

• Intelligence (or the lack of it) resides within the individual; as do perceptual and learning problems.
• Knowledge, skills and learning can be segmented into pieces, and “intelligence” can be segmented into pieces of behavior and/or cognition.
• There are correct and incorrect strategies, facts, behaviors and processes that flow directly and indisputably from the assumptions of a particular theory.
• Cognitive skills can be taught in isolation through training that meets diagnosed “deficits”.

One of the fundamental assumptions of the field is that certain limitations or deficiencies in basic psychological processes cause low levels of intelligence, as measured on traditional IQ-tests. Another assumption is that these cognitive limitations are caused by naturally occurring variation in the neurophysiological substrata that support all cognitive activity, or by damage to these areas caused by accident or disease. These two assumptions provide the rationale for intervention oriented research and practice in psychology and education.

Paradigm versus theory. As already noted, some major theoretical reorientations are occurring in an attempt to remedy the inadequacy of current approaches. In fact, there is considerable evidence in psychology and other social sciences of a shift from the mechanistic reductionist to an alternative, holistic paradigm. Within intelligence research a major obstacle to such a transition relates to a certain amount of confusion between theory and paradigm. Some authors refer to existing theories as paradigms. I believe that this is unfortunate because it gives the impression that a new level of inquiry has been reached, when in fact only variations in theory (however important they may be) are discussed within the assumptions of the mechanistic paradigm.

Paradigm and theory focus on different levels of inquiry. A paradigm delineates the assumptions held of reality and of knowledge per se. A theory is always more narrowly focused than a paradigm and delineates specific phenomena. When a group of researchers in a discipline come to share similar ideas about the basic assumptions they are making in their area, share criteria for the determination of the importance of questions that might be asked, the explanatory concepts that are preferred and the methodologies that are appropriate, they are seen as sharing a scientific paradigm (Kuhn, 1962, 1970a, 1970b). Paradigms can be distinguished from one another by their intellectual history, the research tools they employ and the language used by their adherents (Lachman, Lachman, & Butterfield, 1979). Theories are sets of propositions and hypothetical constructs that are developed to explain particular phenomena or a group of phenomena. Within a paradigm one can modify, expand or completely change a theory without changing the paradigm. It is possible to have several different theories about the same phenomenon within one paradigm. Whereas a paradigm sets a general orientation to inquiry, theories attempt to provide specific explanations for particular phenomena within a paradigm.

Theoretical Reorientations

The importance of interaction. The shift from a mechanistic to a holistic paradigm is a shift from the machine metaphor to a human metaphor. In a holistic paradigm, mechanistic assumptions are replaced by assumptions that emerge directly from the individual’s perceptions and knowing. Knowledge and knowing result from processes of social interchange and interaction with the environment. The acquisition of knowledge is seen no longer as absorbing in one way or another the truth, as defined by some outside criteria, into one’s mind, as it would be in mechanistic thought, but is seen as a direct outcome of processes of social interaction.

We generally count as knowledge that which is represented in linguistic propositions – stored in books, journals, floppy disks, and the like. These renderings … are constituents of social practices. From this perspective knowledge is not something people possess somewhere in their heads, but rather something people do together (Gergen, 1985, p. 270).

This is not to say that all individual differences in learning and achievement can be explained on the basis of social interaction alone, or that cognitive processes are completely specific to the context and occasion in which they were originally acquired or applied. In order to function, people must be able to generalize some aspects of knowledge and skills to new situations. However, there is considerable support for the view that social interchange and interaction with the environment mediate knowing and intellectual development. What individuals perceive and experience is mediated by their own interpretation of the phenomena surrounding them. These interpretations are largely the result of interactions with other people and they are used by the individual to achieve specific goals (for review and further discussion see e.g., Manis & Meltzer, 1978; Meltzer, Petras & Reynolds, 1975; Rogoff & Lave, 1984).

… individuals’ experiences are mediated by their own interpretations of experience. These interpretations are created by individuals through interaction with others and used by individuals to achieve specific goals (Jacob, 1987, p. 27).

Social and cultural expectations and traditional patterns of social interaction make up a large part of the environmental context in which these idiosyncratic interpretations, which so strongly influence cognitive activity, are created (see also e.g., Cole & Scribner, 1975; Rogoff & Lave, 1984; Scribner, 1984; Scribner & Cole, 1981). Rogoff (1984) summarizes these influences as follows:

Central to everyday contexts in which cognitive activity occurs is interaction with other people and use of socially provided tools and schemas for solving problems. Cognitive activity is socially defined, interpreted and supported. People, usually in conjunction with each other and always guided by social norms, set goals, negotiate appropriate means to reach the goals and assist each other in implementing the means and resetting the goals as activities evolve… The social context affects cognitive activity at two levels, according to Vygotsky (1978). First, sociocultural history provides tools for cognitive activity (e.g. writing and calculators) and practices to facilitate reaching appropriate solutions to problems (e.g., norms for the arrangement of grocery shelves to aid shoppers in locating what they need, common mnemonic devices, scripts and frames for interpreting events). Second, the immediate social interactional context structures individual cognitive activity (Rog...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright
• Contents
• Preface
• Notes on Contributors
• 1. Introduction: Paradigm and Context
• 2. Reconciling Views on Intelligence?
• 3. Is the High IQ Person Really In Trouble? Why?
• 4. Intelligence as an Expression of Acquired Knowledge
• 5. Multimodal Learning and the Quality of Intelligent Behavior
• 6. Cognitive Developmental Perspectives on Intelligence
• 7. The Effects of Training and Practice on Human Abilities
• 8. Intelligence, Task Complexity, and the Distinction Between Automatic and Effortful Mental Processing
• 9. On the Neurology of Intelligence and Intelligence Factors
• 10. Cognitive Integration: Alternative Model of Intelligence
• 11. Theory-Based Testing of Intellectual Abilities: Rationale for the Triarchic Abilities Test
• 12. Cognitive Models for Understanding and Assessing Spatial Abilities
• 13. The Control of Complex Systems and Performance in Intelligence Tests
• 14. New Intelligence with Information Technologies
• 15. Improving Intelligence by Fostering Creativity in Everyday Settings
• 16. Intelligence, Economics, and Schooling
• Author Index
• Subject Index