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Cognitive Mechanisms of Learning presents experimental research works on the issue of knowledge acquisition in Cognitive Psychology. These research works – initiated by groups of researchers with academic backgrounds in Philosophy, Psychology, Linguistics and Artificial Intelligence – explore learning mechanisms by viewing humans as information processing systems. Although the book is centered on research studies conducted in a laboratory, one chapter is dedicated to applied research studies, derived directly from the fundamental research works. Computer modeling of learning mechanisms is presented, based on the concept of cognitive architecture. Three important issues – the methodology, the achievements and the evolution – in the field of learning research are also examined.
An accessible introduction to some of the cognitive issues important for thinking and learning in scientific or other complex domains (such as mathematics, physics, chemistry, engineering, or expository writing), with practical educational applications and implementation methods. Many students find it difficult to learn the kind of knowledge and thinking required by college or high school courses in mathematics, science, or other complex domains. Thus they often emerge with significant misconceptions, fragmented knowledge, and inadequate problem-solving skills. Most instructors or textbook authors approach their teaching efforts with a good knowledge of their field of expertise but little awareness of the underlying thought processes and kinds of knowledge required for learning in scientific domains. In this book, Frederick Reif presents an accessible coherent introduction to some of the cognitive issues important for thinking and learning in scientific or other complex domains (such as mathematics, science, physics, chemistry, biology, engineering, or expository writing). Reif, whose experience teaching physics at the University of California led him to explore the relevance of cognitive science to education, examines with some care the kinds of knowledge and thought processes needed for good performance; discusses the difficulties faced by students trying to deal with unfamiliar scientific domains; describes some explicit teaching methods that can help students learn the requisite knowledge and thinking skills; and indicates how such methods can be implemented by instructors or textbook authors. Writing from a practically applied rather than predominantly theoretical perspective, Reif shows how findings from recent research in cognitive science can be applied to education. He discusses cognitive issues related to the kind of knowledge and thinking skills that are needed for science or mathematics courses in high school or colleges and that are essential prerequisites for more advanced intellectual performance. In particular, he argues that a better understanding of the underlying cognitive mechanisms should help to achieve a more scientific approach to science education.
A study of mechanisms of cognitive development. It is part of the "Carnegie Mellon Symposia on Cognition Series" and focuses on behavioural and neural perspectives of cognitive development.
This book examines how evolution influences learning and memory processes in both human and nonhuman animals.
Social Learning and Cognition examines the cognitive mechanisms of social learning and the social learning determinants of cognitive competencies. The explanatory principles of social learning are applied to the highest manifestations of human intellect: judgment, language, and thought. The book also explicates a social learning perspective on the social origins of complex abilities, and how these progressively evolve as children grow older. Comprised of four chapters, this book begins with a discussion on the interrelationships among cognition, behavior change, and social learning. Cognitive explanations for human behavior, and the kinds of evidence cited by cognitive theorists in support of their position, are considered, along with the major psychological theories that address abstract, rule-governed activities. The second chapter deals with children's acquisition and refinement of language, paying particular attention to the objections and misunderstandings raised by psycholinguists to counter modeling explanations of language learning. The third chapter examines relational judgments and categorical decisions and presents evidence showing that diverse modeling procedures can be powerful influences on language and verbal behavior. The final chapter summarizes and integrates research bearing upon the effect of modeling influences on a wide diversity of conceptual activities, ranging from the formation of simple concepts to elaborate intellectual demands that involve complex styles of reasoning and strategies for seeking and organizing information. This monograph is intended for advanced undergraduates, graduate students, and professionals from such diverse fields as child development, social psychology, psychiatry, social work, clinical psychology, education, and rehabilitation.
The brain ... There is no other part of the human anatomy that is so intriguing. How does it develop and function and why does it sometimes, tragically, degenerate? The answers are complex. In Discovering the Brain, science writer Sandra Ackerman cuts through the complexity to bring this vital topic to the public. The 1990s were declared the "Decade of the Brain" by former President Bush, and the neuroscience community responded with a host of new investigations and conferences. Discovering the Brain is based on the Institute of Medicine conference, Decade of the Brain: Frontiers in Neuroscience and Brain Research. Discovering the Brain is a "field guide" to the brainâ€"an easy-to-read discussion of the brain's physical structure and where functions such as language and music appreciation lie. Ackerman examines: How electrical and chemical signals are conveyed in the brain. The mechanisms by which we see, hear, think, and pay attentionâ€"and how a "gut feeling" actually originates in the brain. Learning and memory retention, including parallels to computer memory and what they might tell us about our own mental capacity. Development of the brain throughout the life span, with a look at the aging brain. Ackerman provides an enlightening chapter on the connection between the brain's physical condition and various mental disorders and notes what progress can realistically be made toward the prevention and treatment of stroke and other ailments. Finally, she explores the potential for major advances during the "Decade of the Brain," with a look at medical imaging techniquesâ€"what various technologies can and cannot tell usâ€"and how the public and private sectors can contribute to continued advances in neuroscience. This highly readable volume will provide the public and policymakersâ€"and many scientists as wellâ€"with a helpful guide to understanding the many discoveries that are sure to be announced throughout the "Decade of the Brain."
Based on the proceedings of the twelfth biennial conference on life-span developmental psychology, most of the contributions in this volume deal with the mechanisms of everyday cognition. However, a broad spectrum of additional concerns is addressed within the domain of everyday cognition: its metatheoretical underpinnings, theory and theoretical issues, methods of investigation, empirical considerations, and social issues and applications. Addressing everyday cognition in infancy, childhood, adolescence, young and middle adulthood, and old age, this book is consistent with the chronological life-span theme of this series. The contributors collectively discuss some of the traditional concerns of life-span psychology: the dialectical nature of everyday cognition, individual differences, and contextual influences. Leading and concluding chapters provide overview, integration, and summary. In bringing together a wide array of age periods and points of view within the domain of everyday cognition, the editors hope that students and researchers in developmental psychology and cognitive science will find a useful cross-fertilization of ideas. A huge variety of theoretical perspectives is presented ranging from the position that everyday cognition and academic (laboratory) cognition are different manifestations of the same underlying processes to the position that the underlying processes are completely separate. Also of importance, a large assortment of research methods is illustrated including interviews, laboratory simulations, real-life observations and psychometric methods.
Although deep learning models have achieved great progress in vision, speech, language, planning, control, and many other areas, there still exists a large performance gap between deep learning models and the human cognitive system. Many researchers argue that one of the major reasons accounting for the performance gap is that deep learning models and the human cognitive system process visual information in very different ways. To mimic the performance gap, since 2014, there has been a trend to model various cognitive mechanisms from cognitive neuroscience, e.g., attention, memory, reasoning, and decision, based on deep learning models. This book unifies these new kinds of deep learning models and calls them deep cognitive networks, which model various human cognitive mechanisms based on deep learning models. As a result, various cognitive functions are implemented, e.g., selective extraction, knowledge reuse, and problem solving, for more effective information processing. This book first summarizes existing evidence of human cognitive mechanism modeling from cognitive psychology and proposes a general framework of deep cognitive networks that jointly considers multiple cognitive mechanisms. Then, it analyzes related works and focuses primarily but not exclusively, on the taxonomy of four key cognitive mechanisms (i.e., attention, memory, reasoning, and decision) surrounding deep cognitive networks. Finally, this book studies two representative cases of applying deep cognitive networks to the task of image-text matching and discusses important future directions.
Arguments over the developmental origins of human knowledge are ancient, founded in the writings of Plato, Aristotle, Descartes, Hume, and Kant. They have also persisted long enough to become a core area of inquiry in cognitive and developmental science. Empirical contributions to these debates, however, appeared only in the last century, when Jean Piaget offered the first viable theory of knowledge acquisition that centered on the great themes discussed by Kant: object, space, time, and causality. The essence of Piaget's theory is constructivism: The building of concepts from simpler perceptual and cognitive precursors, in particular from experience gained through manual behaviors and observation.The constructivist view was disputed by a generation of researchers dedicated to the idea of the "competent infant," endowed with knowledge (say, of permanent objects) that emerged prior to facile manual behaviors. Taking this possibility further, it has been proposed that many fundamental cognitive mechanisms -- reasoning, event prediction, decision-making, hypothesis testing, and deduction -- operate independently of all experience, and are, in this sense, innate. The competent-infant view has an intuitive appeal, attested to by its widespread popularity, and it enjoys a kind of parsimony: It avoids the supposed philosophical pitfall posed by having to account for novel forms of knowledge in inductive learners. But this view leaves unaddressed a vital challenge: to understand the mechanisms by which new knowledge arises.This challenge has now been met. The neoconstructivist approach is rooted in Piaget's constructivist emphasis on developmental mechanisms, yet also reflects modern advances in our understanding of learning mechanisms, cortical development, and modeling. This book brings together, for the first time, theoretical views that embrace computational models and developmental neurobiology, and emphasize the interplay of time, experience, and cortical architecture to explain emergent knowledge, with an empirical line of research identifying a set of general-purpose sensory, perceptual, and learning mechanisms that guide knowledge acquisition across different domains and through development.