cognitive psychology anderson

Related to the earlier well-known ACT production system theory, this book’s basic goal is to present evidence for the psychological reality of a production system model of mind. Distinguished from the original theory in three ways, this volume uses the rational analyses of Anderson (1990) to improve upon that theory and extend its scope. It also relates the theory to a great deal of new data on the performance and acquisition of cognitive skills.

The new theory — ACT-R — involves a neurally plausible implementation of a production system architecture. Rational analysis is used to structure and parameterize the system to yield optimal information processing. The theory is applicable to a wide variety of research disciplines, including memory, problem solving, and skill acquisition. Using intelligent tutors, much of the data is concerned with the acquisition of cognitive skills. The book provides analyses of data sets describing the extended course of the acquisition of mathematical and computer programming skills.

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What factors affect creativity and the generation of creative images? What factors affect the ability to reinterpret those images? Research described in this book indicates that expectations constrain both of these attributes of creativity. Characteristics of the imagined pattern, such as cohesiveness or its psychological “goodness”, also affect image generation and reinterpretation. Other evidence indicates that images can be combined mentally to yield new, manipulable composites. Cognitive models encompass the research and extend it to fields as diverse as architecture, music, and problem solving.

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This book achieves a goal that was set 25 years ago when the HAM theory of human memory was published. This theory reflected one of a number of then-current efforts to create a theory of human cognition that met the twin goals of precision and complexity. Up until then the standard for precision had been the mathematical theories of the 1950s and 1960s. These theories took the form of precise models of specific experiments along with some informal, verbally-stated understanding of how they could be extended to new experiments. They seemed to fall far short of capturing the breadth and power of human cognition that was being demonstrated by the new experimental work in human cognition. The next 10 years saw two major efforts to address the problems of scope. In 1976, the ACT theory was first described and included a production rule system of procedural memory to complement HAM’s declarative memory. This provided a computationally adequate system which was indeed capable of accounting for all sorts of cognition. In 1993, a new version of ACT–ACT-R–was published. This was an effort to summarize the theoretical progress made on skill acquisition in the intervening 10 years and to tune the subsymbolic level of ACT-R with the insights of the rational analysis of cognition.

Although the appearance of generally-available, full-function code set off a series of events which was hardly planned, it resulted in this book. The catalyst for this was the emergence of a user community. Lebiere insisted that assembling a critical mass of users was essential to the ultimate success of the theory and that a physical gathering was the only way to achieve that goal. This resulted in the First Annual ACT-R Summer School and Workshop, held in 1994. In writing the book, the authors became seized by an aspiration that went beyond just describing the theory correctly. They decided to try to display what the theory could do by collecting together and describing some of its in-house applications. This book reflects decades of work in ACT-R accumulated by many researchers. The chapters are authored by the people that did that particular work. No doubt the reader will be impressed by the scope of the research and the quality of the individual work. Less apparent, but no less important, was the effort that everyone put into achieving the overall consistency and technical integrity of the book. This is the first work in cognitive science to precisely model such a wide range of phenomena with a single theory.

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An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author’s own ideas.Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author’s brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject.The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.

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The Development of Intelligence is an attempt to influence the next generation who will research the central question of this book – what is the nature of intelligence and how does it develop? The book provides a contemporary review of methods and theories of the development of intellectual abilities from infancy to adulthood by the major researchers in the field. It is unusual, because edited books usually bring together a collection of researchers who publish in the same journals, attend the same conferences and so on. In this case, the contributors come from quite different research areas with different approaches. The editor deliberately selected a group of eminent researchers with diverse and often conflicting theoretical orientations who use a wide range of different research methodologies. Consequently, there is coverage of a range of topics not usually found in a single book, including: the measurement of intelligence; infancy predictors of later IQ differences; developmental behaviour genetics; theories of cognitive change; general intelligence and specific abilities; multiple intelligences; savant syndrome; cognitive development in the intellectually disabled; and intervention studies. A concluding chapter by the editor pulls together the main themes generated by the contributors and sets out a new research agenda for developmental studies of intellectual abilities.

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A comprehensive overview of the current state of research on memory and mind, this book captures the career and influence of Gordon H. Bower (as told by 22 of his students and colleagues), showing how Bower’s research and mentoring of students has broadly and deeply affected modern research. In addition to many personal reminisces about Bower’s research and graduate training in the 1950s through 1990s, this book illustrates how Bower’s early research and ideas lay the groundwork for much of modern psychological studies of memory, expertise, psychological assessment, and mental imagery.

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