Models Of Cognitive Development

In spite of its obvious importance and popularity, the field of cognitive development remains highly fragmented, scientifically. Instead of theoretical convergence towards a generally accepted set of principles, there remains a vast diversity of models of what knowledge and reasoning are, and how they develop. Courses and books tend to deal with this perplexing situation by simply presenting students with either a specific, favoured line, or by offering selections from the theoretical salad. As a result, students have great difficulty in obtaining a cohesive picture of the area. They are frequently bewildered by the diversity of schools, frameworks and approaches, with seemingly little connection between them. More seriously, they are deprived of a critical grasp of the area, and thus forced into a habit of early selectivity, rote memory of specific models in isolation, and regurgitation at exams. This in turn deprives the area of cognitive development of important critical impetus for future improvement. Models of Cognitive Development is an attempt to overcome these problems. It does this by arguing that the vast diversity of theories or models can be organised into groups according to a much smaller set of underlying assumptions or preconceptions, which themselves can be historically interrelated. By understanding these, students may be helped to find their way more confidently around the area as a whole, to see the 'wood' as well as the theoretical forest, and thus find themselves in a position to react to individual models more positively and more critically. Such criticism may, in turn, assist theoretical progress and unity in the future. Models of Cognitive Development covers all the contemporary theoretical and research strands in the area, with numerous examples, in a clear and straightforward manner, and should be useful to all students, researchers, and comparative theoreticians in the area.

In spite of its obvious importance and popularity, the field of cognitive development remains highly fragmented due to the vast diversity of models of what knowledge and reasoning are, and how they develop. This new Classic Edition of Models of Cognitive Development aims to overcome this barrier through its careful introduction, illustrated examples, and approach to helping students think more critically about the subject. In this significant work, Richardson provides students, researchers, and comparative theoreticians with a cohesive understanding of the area by organizing diverse schools, frameworks, and approaches according to a much smaller set of underlying assumptions or preconceptions, which themselves can be historically interrelated. By understanding these, it’s possible to find pathways around the area more confidently as a whole, to see the “wood” as well as the theoretical trees, and be able to react to individual models more critically and constructively. The Classic Edition of this core text will be essential reading for undergraduate and graduate students of cognitive development.

This work argues that cognitive development is experience driven, and processes entailed in acquiring information about the world are analyzed based on recent models of learning and induction. The way information is represented and accessed when performing cognitive tasks is considered paying particular attention to the implications of Parallel Distributed Processing (PDP) models for cognitive development. The first half of the book contains analyses of human reasoning processes (drawing on PDP models of analogy), development of strategies, and task complexity -- all based on aspects of PDP representations. It is proposed that PDP representations become more differentiated with age, so more vectors can be processed in parallel, with the result that structures of greater complexity can be processed. This model gives an account of previously unexplained difficulties in children's reasoning, including some which were influential in stage theories. The second half of the book examines processes entailed in some representative cognitive developmental tasks, including transitive inference, deductive inference (categorical syllogisms), hypothesis testing, learning set acquisition, acquisition and transfer of relational structures, humor, hierarchical classification and inclusion, understanding of quantity, arithmetic word problems, algebra, conservation, mechanics, and the concept of mind. Process accounts of tasks are emphasized, based on applications of recent developments in cognitive science.

Cognitive psychologists have found the production systems class of computer simulation models to be one of the most direct ways to cast complex theories of human intelligence. There have been many scattered studies on production systems since they were first proposed as computational models of human problem-solving behavior by Allen Newell some twenty years ago, but this is the first book to focus exclusively on these important models of human cognition, collecting and giving many of the best examples of current research. In the first chapter, Robert Neches, Pat Langley, and David Klahr provide an overview of the fundamental issues involved in using production systems as a medium for theorizing about cognitive processes, emphasizing their theoretical power. The remaining chapters take up learning by doing and learning by understanding, discrimination learning, learning through incremental refinement, learning by chunking, procedural earning, and learning by composition. A model of cognitive development called BAIRN is described, and a final chapter reviews John Anderson's ACT theory and discusses how it can be used in intelligent tutoring systems, including one that teaches LISP programming skills. In addition to the editors, the contributors are Yuichiro Anzai (Hokkaido University, Japan), Paul Rosenbloom (Stanford) and Allen Newell (Carnegie-Mellon), Stellan Ohlsson (University of Pittsburgh), Clayton Lewis (University of Colorado, Boulder), Iain Wallace and Kevin Bluff (Deakon University, Australia), and John Anderson (Carnegie-Mellon). David Klahr is Professor and Head of the Department of Psychology at Carnegie-Mellon University. Pat Langley is Associate Professor, Department ofInformation and Computer Science, University of California, Irvine, and Robert Neches is Research Computer Scientist at University of Southern California Information Sciences Institute. "Production System Models of Learning and Development" is included in the series Computational Models of Cognition and Perception, edited by Jerome A. Feldman, Patrick J. Hayes, and David E.Rumelhart. A Bradford Book.

In spite of its obvious importance and popularity, the field of cognitive development remains highly fragmented, scientifically. Instead of theoretical convergence towards a generally accepted set of principles, there remains a vast diversity of models of what knowledge and reasoning are, and how they develop. Courses and books tend to deal with this perplexing situation by simply presenting students with either a specific, favoured line, or by offering selections from the theoretical salad. As a result, students have great difficulty in obtaining a cohesive picture of the area. They are frequently bewildered by the diversity of schools, frameworks and approaches, with seemingly little connection between them. More seriously, they are deprived of a critical grasp of the area, and thus forced into a habit of early selectivity, rote memory of specific models in isolation, and regurgitation at exams. This in turn deprives the area of cognitive development of important critical impetus for future improvement. Models of Cognitive Development is an attempt to overcome these problems. It does this by arguing that the vast diversity of theories or models can be organised into groups according to a much smaller set of underlying assumptions or preconceptions, which themselves can be historically interrelated. By understanding these, students may be helped to find their way more confidently around the area as a whole, to see the 'wood' as well as the theoretical forest, and thus find themselves in a position to react to individual models more positively and more critically. Such criticism may, in turn, assist theoretical progress and unity in the future. Models of Cognitive Development covers all the contemporary theoretical and research strands in the area, with numerous examples, in a clear and straightforward manner, and should be useful to all students, researchers, and comparative theoreticians in the area.

An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and Bayesian models; and models of social interaction. All mathematical concepts are introduced gradually, with no background in advanced topics required. Hints and solutions for exercises and a glossary follow the main text. All code in the book is Python, with the Spyder editor in the Anaconda environment. A GitHub repository with Python files enables readers to access the computer code used and start programming themselves. The book is suitable as an introduction to modeling cognitive processes for students across a range of disciplines and as a reference for researchers interested in a broad overview.

This volume presents peer-reviewed versions of papers presented at the 14th Neural Computation and Psychology Workshop (NCPW14), which took place in July 2014 at Lancaster University, UK. The workshop draws international attendees from the cutting edge of interdisciplinary research in psychology, computational modeling, artificial intelligence and psychology, and aims to drive forward our understanding of the mechanisms underlying a range of cognitive processes.

Cognitive Developmental Change makes a fascinating contribution to the fields of developmental, cognitive and educational science by bringing together a uniquely diverse range of perspectives for analysing the dynamics of change. Connecting traditional Piagetian, information processing, and psychometric approaches with newer frameworks for the analysis of developmental change it provides the reader with an account of the latest theory and research at the time of publication. The contributors to the volume, all internationally respected experts, were asked when writing to consider three main aspects of cognitive change. Its object (what changes in the mind during development), its nature (how does change occur?) and its causes (why does change occur? Or, what are the internal and external factors responsible for cognitive change?). As a result chapters cover key theories of cognitive change, the factors that affect change including neurological, emotional and socio-cultural factors and methods for measuring and modelling change.