Mathematical Modelling In Quantum Psychology Pdf

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The Oxford Handbook of Computational and Mathematical Psychology

Edited by: , , , and

Abstract

A comprehensive and authoritative review on most important developments in computational and mathematical psychology that have impacted many other fields in past decades. Written in tutorial style by leading scientists in each topic area, with an emphasis on examples and applications. Each chapter is self-contained and aims to engage readers with various levels of modeling experience. The Handbook covers the key developments in elementary cognitive mechanisms (e.g., signal detection, information processing, reinforcement learning), basic cognitive skills (e.g., perceptual judgment, categorization, episodic memory), higher-level cognition (e.g., Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (e.g., Bayesian estimation and other new model comparison methods), and emerging new directions (e.g., neurocognitive modeling, applications to clinical psychology, quantum cognition) in computation and mathematical psychology. The chapters were written for a typical graduate student in virtually any area of psychology, cognitive science, and related social and behavioral sciences, such as consumer behavior and communication. We also expect it to be useful for readers ranging from advanced undergraduate students to experienced faculty members and researchers. Beyond being a handy reference book, it should be beneficial as a textbook for self-teaching, and for graduate level (or advanced undergraduate level) courses in computational and mathematical psychology.

Keywords: Mathematical functions, derivatives and integrals, probability theory, expectations, maximum likelihood estimation

Bibliographic Information

Publisher:

Oxford University Press

Print Publication Date:

Apr 2015

ISBN:

9780199957996

Published online:

Dec 2015

DOI:

10.1093/oxfordhb/9780199957996.001.0001

Editors

Jerome R. Busemeyer, editor
Department of Psychological and Brain Sciences, Cognitive Science Program, Indiana University, Bloomington, IN

Zheng Wang, editor
School of Communication, Center for Cognitive and Brain Sciences, The Ohio State University, Columbus, OH

James T. Townsend, editor
James T. Townsend, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN

Ami Eidels, editor
Ami Eidels, School of Psychology, University of Newcastle, Callaghan, NSW, Australia

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• Front Matter
  • Oxford Library of Psychology
  • The Oxford Handbook of Computational and Mathematical Psychology
  • Dedication
  • Oxford Library of Psychology
  • About the Editors
  • Contributors
  • Preface
• Review of Basic Mathematical Concepts Used in Computational and Mathematical PsychologyJerome R. Busemeyer, Zheng Wang, Ami Eidels, and James T. Townsend
• Part I Elementary Cognitive Mechanisms
  • Multidimensional Signal Detection TheoryF. Gregory Ashby and Fabian A. Soto
  • Modeling Simple Decisions and Applications Using a Diffusion ModelRoger Ratcliff and Philip Smith
  • Features of Response Times: Identification of Cognitive Mechanisms through Mathematical ModelingDaniel Algom, Ami Eidels, Robert X. D. Hawkins, Brett Jefferson, and James T. Townsend
  • Computational Reinforcement LearningTodd M. Gureckis and Bradley C. Love
• Part II Basic Cognitive Skills
  • Why Is Accurately Labeling Simple Magnitudes So Hard? A Past, Present, and Future Look at Simple Perceptual JudgmentChris Donkin, Babette Rae, Andrew Heathcote, and Scott D. Brown
  • An Exemplar-Based Random-Walk Model of Categorization and RecognitionRobert M. Nosofsky and Thomas J. Palmeri
  • Models of Episodic MemoryAmy H. Criss and Marc W. Howard
• Part III Higher Level Cognition
  • Structure and Flexibility in Bayesian Models of CognitionJoseph L. Austerweil, Samuel J. Gershman, Joshua B. Tenenbaum, and Thomas L. Griffiths
  • Models of Decision Making under Risk and UncertaintyTimothy J. Pleskac, Adele Diederich, and Thomas S. Wallsten
  • Models of Semantic MemoryMichael N. Jones, Jon Willits, and Simon Dennis
  • Shape PerceptionTadamasa Sawada, Yunfeng Li, and Zygmunt Pizlo
• Part IV New Directions
  • Bayesian Estimation in Hierarchical ModelsJohn K. Kruschke and Wolf Vanpaemel
  • Model Comparison and the Principle of ParsimonyJoachim Vandekerckhove, Dora Matzke, and Eric-Jan Wagenmakers
  • Neurocognitive Modeling of Perceptual Decision MakingThomas J. Palmeri, Jeffrey D. Schall, and Gordon D. Logan
  • Mathematical and Computational Modeling in Clinical PsychologyRichard W. J. Neufeld
  • Quantum Models of Cognition and DecisionJerome R. Busemeyer, Zheng Wang, and Emmauel Pothos
• End Matter
  • Index

Mathematical Modelling In Quantum Psychology Pdf

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