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A comprehensive introduction to the computational modeling of human cognition.
Responding to an explosion of new mathematical and computational models used in the fields of cognitive science, this book provides simple tutorials concerning the development and testing of such models. The authors focus on a few key models, with a primary goal of equipping readers with the fundamental principles, methods, and tools necessary for evaluating and testing any type of model encountered in the field of cognitive science.
This book presents an integrated framework for developing and testing computational models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models.
Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.
A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.
This book is a practical guide to building computational models of high-level cognitive processes and systems. High-level processes are those central cognitive processes involved in thinking, reasoning, planning, and so on. These processes appear to share representational and processing requirements, and it is for this reason that they are considered together in this text. The book is divided into three parts. Part I considers foundational and background issues. Part II provides a series of case studies spanning a range of cognitive domains. Part III reflects upon issues raised by the case studies. Teachers of cognitive modeling may use material from Part I to structure lectures and practical sessions, with chapters in Part II forming the basis of in-depth student projects. All models discussed in this book are developed within the COGENT environments. COGENT provides a graphical interface in which models may be sketched as "box and arrow" diagrams and is both a useful teaching tool and a productive research tool. As such, this book is designed to be of use to both students of cognitive modeling and active researchers. For students, the book provides essential background material plus an extensive set of example models, exercises and project material. Researchers of both symbolic and connectionist persuasions will find the book of interest for its approach to cognitive modeling, which emphasizes methodological issues. They will also find that the COGENT environment itself has much to offer.
The development of cognitive models is a key step in the challenging research program to advance our understanding of human cognition and behavior. Dynamical models represent a general and flexible approach to cognitive modeling. This introduction focuses on applications of stochastic processes and dynamical systems to model cognition. The dynamical approach is particularly useful to emphasize the strong link between experimental research (and its paradigms), data analysis, and mathematical models including their computer implementation for numerical simulation. Most of specific examples are from the domain of eye movement research, with concepts being applicable to a broad range of problems in cognitive modeling. The textbook aims at the graduate and/or advanced undergraduate level for students in Cognitive Science and related disciplines such as Psychology and Computer Science. Joint introduction of the theory of cognitive processes and mathematical models, their underlying mathematical concepts, numerical simulation, and analysis; The focus on eye movements provide a theoretically coherent, but very general application area; Computer code in R Programming Language for Statistical Computing is available for all examples, figures, and solutions to exercises.
An accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science This practical and readable work provides students and researchers, who are new to cognitive modeling, with the background and core knowledge they need to interpret published reports, and develop and apply models of their own. The book is structured to help readers understand the logic of individual component techniques and their relationships to each other.
This book presents interdisciplinary research in the science of Human Cognition through mathematical and computational modeling and simulation. Featuring new approaches developed by leading experts in the field of cognitive science, it highlights the relevance and depth of this important area of social sciences and its expanding reach into the biological, physical, computational and mathematical sciences. This contributed volume compiles the most recent advancements and cutting-edge applications of cognitive modeling, employing a genuinely multidisciplinary approach to simulate thinking, memory, and decision-making. The topics covered encompass a wide range of subjects, such as Agent-based Modeling in psychological research, the Nyayasutra proof pattern, the utilization of the Pheromone Trail Algorithm for modeling Analog Memory, the theory and practical applications of Social Laser Theory, addressing the challenges of probabilistic learning in brain and behavior models, adopting a Physicalistic perspective to understand the emergence of cognition and computation, an in-depth analysis of the conjunction fallacy as a factual occurrence, exploring quantum modeling and causality in physics and its extensions, examining compositional vector semantics within spiking neural networks, delving into the realms of Optimality, Prototypes, and Bilingualism, and finally, investigating the intricate dimensionality of color perception. Given its scope and approach, the book will benefit researchers and students of computational social sciences, mathematics and its applications, quantum physics.
Exploration of a new integrative intellectual enterprise: the cognitive social sciences.