Cognitive Choice Modeling


Book Description

The emerging interdisciplinary field of cognitive choice models integrates theory and recent research findings from both decision process and choice behavior. Cognitive decision processes provide the interface between the environment and brain, enabling choice behavior, and the basic cognitive mechanisms underlying decision processes are fundamental to all fields of human activity. Yet cognitive processes and choice processes are often studied separately, whether by decision theorists, consumer researchers, or social scientists. In Cognitive Choice Modeling, Zheng Joyce Wang and Jerome R. Busemeyer introduce a new cognitive modeling approach to the study of human choice behavior. Integrating recent research findings from both cognitive science and choice behavior, they lay the groundwork for the emerging interdisciplinary field of cognitive choice modeling.




Decision Making


Book Description

This book offers an exciting new collection of recent research on the actual processes that humans use when making decisions in their everyday lives and in business situations. The contributors use cognitive psychological techniques to break down the constituent processes and set them in their social context. The contributors are from many different countries and draw upon a wide range of techniques, making this book a valuable resource to cognitive psychologists in applied settings, economists and managers.




Introduction to Modeling Cognitive Processes


Book Description

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.




Decision Making and Modelling in Cognitive Science


Book Description

This book discusses the paradigm of quantum ontology as an appropriate model for measuring cognitive processes. It clearly shows the inadequacy of the application of classical probability theory in modelling the human cognitive domain. The chapters investigate the context dependence and neuronal basis of cognition in a coherent manner. According to this framework, epistemological issues related to decision making and state of mind are seen to be similar to issues related to equanimity and neutral mind, as discussed in Buddhist perspective. The author states that quantum ontology as a modelling tool will help scientists create new methodologies of modelling in other streams of science as well.




Quantum Models of Cognition and Decision


Book Description

Introduces principles drawn from quantum theory to present a new framework for modeling human cognition and decision.




Goal-Directed Decision Making


Book Description

Goal-Directed Decision Making: Computations and Neural Circuits examines the role of goal-directed choice. It begins with an examination of the computations performed by associated circuits, but then moves on to in-depth examinations on how goal-directed learning interacts with other forms of choice and response selection. This is the only book that embraces the multidisciplinary nature of this area of decision-making, integrating our knowledge of goal-directed decision-making from basic, computational, clinical, and ethology research into a single resource that is invaluable for neuroscientists, psychologists and computer scientists alike. The book presents discussions on the broader field of decision-making and how it has expanded to incorporate ideas related to flexible behaviors, such as cognitive control, economic choice, and Bayesian inference, as well as the influences that motivation, context and cues have on behavior and decision-making. - Details the neural circuits functionally involved in goal-directed decision-making and the computations these circuits perform - Discusses changes in goal-directed decision-making spurred by development and disorders, and within real-world applications, including social contexts and addiction - Synthesizes neuroscience, psychology and computer science research to offer a unique perspective on the central and emerging issues in goal-directed decision-making




Efficient Cognition


Book Description

An argument that representational decision making is more cognitively efficient, allowing an organism to adjust more easily to changes in the environment. Many organisms (including humans) make decisions by relying on mental representations. Not simply a reaction triggered by perception, representational decision making employs high-level, non-perceptual mental states with content to manage interactions with the environment. A person making a decision based on mental representations, for example, takes a step back from her perceptions at the time to assess the nature of the world she lives in. But why would organisms rely on representational decision making, and what evolutionary benefits does this reliance provide to the decision maker? In Efficient Cognition, Armin Schulz argues that representational decision making can be more cognitively efficient than non-representational decision making. Specifically, he shows that a key driver in the evolution of representational decision making is that mental representations can enable an organism to save cognitive resources and adjust more efficiently to changed environments. After laying out the foundations of his argument—clarifying the central questions, the characterization of representational decision making, and the relevance of an evidential form of evolutionary psychology—Schulz presents his account of the evolution of representational decision making and critically considers some of the existing accounts of the subject. He then applies his account to three open questions concerning the nature of representational decision making: the extendedness of decision making, and when we should expect cognition to extend into the environment; the specialization of decision making and the use of simple heuristics; and the psychological sources of altruistic behaviors.




Towards Cognitive Autonomous Networks


Book Description

Learn about the latest in cognitive and autonomous network management Towards Cognitive Autonomous Networks: Network Management Automation for 5G and Beyond delivers a comprehensive understanding of the current state-of-the-art in cognitive and autonomous network operation. Authors Mwanje and Bell fully describe todays capabilities while explaining the future potential of these powerful technologies. This book advocates for autonomy in new 5G networks, arguing that the virtualization of network functions render autonomy an absolute necessity. Following that, the authors move on to comprehensively explain the background and history of large networks, and how we come to find ourselves in the place were in now. Towards Cognitive Autonomous Networks describes several novel techniques and applications of cognition and autonomy required for end-to-end cognition including: • Configuration of autonomous networks • Operation of autonomous networks • Optimization of autonomous networks • Self-healing autonomous networks The book concludes with an examination of the extensive challenges facing completely autonomous networks now and in the future.




Neuroscience of Decision Making


Book Description

The intersection between the fields of behavioral decision research and neuroscience has proved to be fertile ground for interdisciplinary research. Whereas the former is rich in formalized models of choice, the latter is rife with techniques for testing behavioral models at the brain level. As a result, there has been the rapid emergence of progressively more sophisticated biological models of choice, geared toward the development of ever more complete mechanistic models of behavior. This volume provides a coherent framework for distilling some of the key themes that have emerged as a function of this research program, and highlights what we have learned about judgment and decision making as a result. Although topics that are theoretically relevant to judgment and decision making researchers are addressed, the book also ventures somewhat beyond the traditional boundaries of this area to tackle themes that would of interest to a greater community of scholars. Neuroscience of Decision Making provides contemporary and essential reading for researchers and students of cognitive psychology, neuroscience, philosophy, and economics.




The Oxford Handbook of Cognitive Science


Book Description

The Oxford Handbook of Cognitive Science emphasizes the research and theory most central to modern cognitive science: computational theories of complex human cognition. Additional facets of cognitive science are discussed in the handbook's introductory chapter.