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Author: Jerome R. Busemeyer Publisher: SAGE ISBN: 0761924507 Category : Psychology Languages : en Pages : 225
Book Description
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.
Author: Jerome R. Busemeyer Publisher: SAGE ISBN: 0761924507 Category : Psychology Languages : en Pages : 225
Book Description
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.
Author: Simon Farrell Publisher: Cambridge University Press ISBN: 1108547141 Category : Psychology Languages : en Pages : 485
Book Description
Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of 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. Both the development of models and key features of any model are covered, as are the applications of models in a variety of domains across the behavioural sciences. A number of chapters are devoted to fitting models using maximum likelihood and Bayesian estimation, including fitting hierarchical and mixture models. Model comparison is described as a core philosophy of scientific inference, and the use of models to understand theories and advance scientific discourse is explained.
Author: Joseph W. Houpt Publisher: Frontiers Media SA ISBN: 2889450562 Category : Cognitive psychology Languages : en Pages : 142
Book Description
To deal with the abundant amount of information in the environment in order to achieve our goals, human beings adopt a strategy to accumulate some information and filter out other information to ultimately make decisions. Since the development of cognitive science in the 1960s, researchers have been interested in understanding how human beings process and accumulate information for decision-making. Researchers have conducted extensive behavioral studies and applied a wide range of modeling tools to study human behavior in simple-detection tasks and two-choice decision tasks (e.g., discrimination, classification). In general, researchers often assume that the manner in which information is processed for decision-making is invariant across individuals given a particular experimental context. Independent variables, including speed-accuracy instructions, stimulus properties (i.e., intensity), and characteristics of the participants (i.e., aging, cognitive ability) are assumed to affect the parameters in a model (i.e., speed of information accumulation, response bias) but not the way that participants process information (e.g., the order of information processing). Given these assumptions, much modeling has been accomplished based on the grouped data, rather than the individual data. However, a growing number of studies have demonstrated that there were individual differences in the perceptual decision process. In the same task context, different groups of the participants may process information in different manners. The capacity and architecture of the decision mechanism were found to vary across individuals, implying that humans’ decision strategies can vary depending on the context to maximize their performance. In this special issue, we focused on a particular subset of cognitive models, particularly accumulator models, multinomial processing trees and systems factorial technology (SFT) as applied to perceptual decision making. The motivation for the focus on perceptual decision-making is threefold. Empirical studies of perception have grown out of a history of making a large number of observations for each individual so as to achieve precise estimates of each individual’s performance. This type of data, rather than a small number of observations per individual, is most amenable to achieving precision in individual-level and group-level cognitive modeling. Second, the interaction between the acquisition of perceptual information and the decisions based on that information (to the extent that those processes are distinguishable) offers rich data for scientific exploration. Finally, there is an increasing interest in the practical application of individual variation in perceptual ability, whether to inform perceptual training and expertise, or to guide personnel decisions. Although these practical applications are beyond the scope of this issue, we hope that the research presented herein may serve as the foundation for future endeavors in that domain.
Author: Thomas L. Griffiths Publisher: MIT Press ISBN: 0262049414 Category : Science Languages : en Pages : 649
Book Description
The definitive introduction to Bayesian cognitive science, written by pioneers of the field. How does human intelligence work, in engineering terms? How do our minds get so much from so little? Bayesian models of cognition provide a powerful framework for answering these questions by reverse-engineering the mind. This textbook offers an authoritative introduction to Bayesian cognitive science and a unifying theoretical perspective on how the mind works. Part I provides an introduction to the key mathematical ideas and illustrations with examples from the psychological literature, including detailed derivations of specific models and references that can be used to learn more about the underlying principles. Part II details more advanced topics and their applications before engaging with critiques of the reverse-engineering approach. Written by experts at the forefront of new research, this comprehensive text brings the fields of cognitive science and artificial intelligence back together and establishes a firmly grounded mathematical and computational foundation for the understanding of human intelligence. The only textbook comprehensively introducing the Bayesian approach to cognition Written by pioneers in the field Offers cutting-edge coverage of Bayesian cognitive science's research frontiers Suitable for advanced undergraduate and graduate students and researchers across the sciences with an interest in the mind, brain, and intelligence Features short tutorials and case studies of specific Bayesian models
Author: Thomas Parr Publisher: MIT Press ISBN: 0262362287 Category : Science Languages : en Pages : 313
Book Description
The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.
Author: Bruno D. Zumbo Publisher: Springer ISBN: 3319561294 Category : Social Science Languages : en Pages : 374
Book Description
This volume addresses an urgent need across multiple disciplines to broaden our understanding and use of response processes evidence of test validity. It builds on the themes and findings of the volume Validity and Validation in Social, Behavioral, and Health Sciences (Zumbo & Chan, 2014), with a focus on measurement validity evidence based on response processes. Approximately 1000 studies are published each year examining the validity of inferences made from tests and measures in the social, behavioural, and health sciences. The widely accepted Standards for Educational and Psychological Testing (1999, 2014) present five sources of evidence for validity: content-related, response processes, internal structure, relationships with other variables, and consequences of testing. Many studies focus on internal structure and relationships with other variables sources of evidence, which have a long history in validation research, known methodologies, and numerous exemplars in the literature. Far less is understood by test users and researchers conducting validation work about how to think about and apply new and emerging sources of validity evidence. This groundbreaking volume is the first to present conceptual models of response processes, methodological issues that arise in gathering response processes evidence, as well as applications and exemplars for providing response processes evidence in validation work.
Author: Konstantin V. Baev Publisher: Springer Science & Business Media ISBN: 1461241006 Category : Medical Languages : en Pages : 307
Book Description
This book is devoted to a novel conceptual theoretical framework of neuro science and is an attempt to show that we can postulate a very small number of assumptions and utilize their heuristics to explain a very large spectrum of brain phenomena. The major assumption made in this book is that inborn and acquired neural automatisms are generated according to the same func tional principles. Accordingly, the principles that have been revealed experi mentally to govern inborn motor automatisms, such as locomotion and scratching, are used to elucidate the nature of acquired or learned automat isms. This approach allowed me to apply the language of control theory to describe functions of biological neural networks. You, the reader, can judge the logic of the conclusions regarding brain phenomena that the book derives from these assumptions. If you find the argument flawless, one can call it common sense and consider that to be the best praise for a chain of logical conclusions. For the sake of clarity, I have attempted to make this monograph as readable as possible. Special attention has been given to describing some of the concepts of optimal control theory in such a way that it will be under standable to a biologist or physician. I have also included plenty of illustra tive examples and references designed to demonstrate the appropriateness and applicability of these conceptual theoretical notions for the neurosciences.
Author: Michael Zgurovsky Publisher: Springer Nature ISBN: 3031374509 Category : Technology & Engineering Languages : en Pages : 468
Book Description
This book contains the latest scientific work of Ukrainian scientists and their colleagues from other countries of the world in three interrelated areas: systems analysis, artificial intelligence and data mining. The included articles present the theoretical foundations and practical applications of the latest tools and methods of artificial intelligence, scenario planning, decision making and computational intelligence for important areas of human activity. The tools and methods presented in the book are continuously evolving and finding new applications across various fields, contributing to advancements and efficiencies in different industries: healthcare, finance, retail and E-commerce, manufacturing and industrial automation, transportation and logistics advancements and cybersecurity. The results of the book are useful to teachers, scientists, graduate students of universities and managers of large companies specializing in strategic planning, engineering design of complex systems, decision-making, optimization of operations and other related fields of knowledge and practice.
Author: Sergey Kovalev Publisher: Springer Nature ISBN: 3031196201 Category : Technology & Engineering Languages : en Pages : 533
Book Description
This book contains the works connected with the key advances in Intelligent Information Technologies for Industry presented in the main track of IITI 2022, the Sixth International Scientific Conference on Intelligent Information Technologies for Industry held on October 31 - November 6, 2022, in Istanbul, Turkey. The works were written by the experts in the field of artificial intelligence including topics such as machine learning, decision making intelligent systems, fuzzy logic, bioinspired systems and Bayesian networks. The following industrial application domains were touched: railway automation, intelligent medical systems, flexible socio-technical systems, navigation systems and energetic systems. The editors believe that this book will be helpful for all scientists and engineers interested in the modern state of applied artificial intelligence.
Author: Jerome R. Busemeyer
Author: Jerome R. Busemeyer
Author: Simon Farrell
Author: Joseph W. Houpt
Author: Thomas L. Griffiths
Author: Thomas Parr
Author: Bruno D. Zumbo
Author: Konstantin V. Baev
Author: Birte U. Forstmann
Author: Michael Zgurovsky
Author: Sergey Kovalev