J. Busemeyer – The Oxford Handbook of Computational and Mathematical Psychology (2015)

Автор: J. Busemeyer
Название книги: The Oxford Handbook of Computational and Mathematical Psychology
Формат: PDF
Жанр: Психология
Страницы: 425
Качество: Изначально компьютерное, E-book

This Oxford Handbook offers a comprehensive and authoritative review of important developments in computational and mathematical psychology. With chapters written by leading scientists across a variety of subdisciplines, it examines the field's influence on related research areas such as cognitive psychology, developmental psychology, clinical psychology, and neuroscience. The Handbook emphasizes examples and applications of the latest research, and will appeal to readers possessing various levels of modeling experience.
The Oxford Handbook of Computational and mathematical Psychology covers the key developments in elementary cognitive mechanisms (signal detection, information processing, reinforcement learning), basic cognitive skills (perceptual judgment, categorization, episodic memory), higher-level cognition (Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (Bayesian estimation and other new model comparison methods), and emerging new directions in computation and mathematical psychology (neurocognitive modeling, applications to clinical psychology, quantum cognition).
The Handbook would make an ideal graduate-level textbook for courses in computational and mathematical psychology. Readers ranging from advanced undergraduates to experienced faculty members and researchers in virtually any area of psychology-including cognitive science and related social and behavioral sciences such as consumer behavior and communication-will find the text useful.
Preface.
Review of Basic Mathematical Concepts Used in Computational and Mathematical Psychology.
Elementary Cognitive Mechanisms.
Multidimensional Signal Detection Theory.
Modeling Simple Decisions and Applications Using a Diffusion Model.
Features of Response Times: Identification of Cognitive Mechanisms through Mathematical Modeling.
Computational Reinforcement Learning.
Basic Cognitive Skills.
Why Is Accurately Labeling Simple Magnitudes So Hard? A Past, Present, and Future Look at Simple Perceptual Judgment.
An Exemplar-Based Random-Walk Model of Categorization and Recognition.
Models of Episodic Memory.
Higher Level Cognition.
Structure and Flexibility in Bayesian Models of Cognition.
Models of Decision Making under Risk and Uncertainty.
Models of Semantic Memory.
Shape Perception.
New Directions.
Bayesian Estimation in Hierarchical Models.
Model Comparison and the Principle of Parsimony.
Neurocognitive Modeling of Perceptual Decision Making.
Mathematical and Computational Modeling in Clinical Psychology.
Quantum Models of Cognition and Decision.