Open AccessBook
The Oxford Handbook of Computational and Mathematical Psychology
TLDR
This book presents Quantum Models of Cognition and Decision, a new approach to Mathematical and Computational Modeling in Clinical Psychology that combines Bayesian Estimation in Hierarchical Models and Quantum Models, and its Applications.Abstract:
Preface 1. Introduction Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels Part I. Elementary Cognitive Mechanisms 2. Multidimensional Signal Detection Theory F. Gregory Ashby and Fabian A. Soto 3. Modeling Simple Decisions and Applications Using a Diffusion Model Roger Ratcliff and Philip Smith 4. Features of Response Times: Identification of Cognitive Mechanisms through Mathematical Modeling Daniel Algom, Ami Eidels, Robert X. D. Hawkins, Brett Jefferson, and James T. Townsend 5. Computational Reinforcement Learning Todd M. Gureckis and Bradley C. Love Part II. Basic Cognitive Skills 6. Why Is Accurately Labeling Simple Magnitudes So Hard? A Past, Present, and Future Look at Simple Perceptual Judgment Chris Donkin, Babette Rae, Andrew Heathcote, and Scott D. Brown 7. An Exemplar-Based Random-Walk Model of Categorization and Recognition Robert M. Nosofsky and Thomas J. Palmeri 8. Models of Episodic Memory Amy H. Criss and Marc W. Howard Part III. Higher Level Cognition 9. Structure and Flexibility in Bayesian Models of Cognition Joseph L. Austerweil, Samuel J. Gershman, and Thomas L. Griffiths 10. Models of Decision Making under Risk and Uncertainty Timothy J. Pleskac, Adele Diederich, and Thomas S. Wallsten 11. Models of Semantic Memory Michael N. Jones, Jon Willits, and Simon Dennis 12. Shape Perception Tadamasa Sawada, Yunfeng Li, and Zygmunt Pizlo Part IV. New Directions 13. Bayesian Estimation in Hierarchical Models John K. Kruschke and Wolf Vanpaemel 14. Model Comparison and the Principle of Parsimony Joachim Vandekerckhove, Dora Matzke, and Eric-Jan Wagenmakers 15. Neurocognitive Modeling of Perceptual Decision Making Thomas J. Palmeri, Jeffrey D. Schall, and Gordon D. Logan 16. Mathematical and Computational Modeling in Clinical Psychology Richard W. J. Neufeld 17. Quantum Models of Cognition and Decision Jerome R. Busemeyer, Zheng Wang, and Emmanuel Pothos Indexread more
Citations
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"Cognitive, social, and physiological determinants of emotional state": Erratum
TL;DR: The problem of which cues, internal or external, permit a person to label and identify his own emotional state has been with us since the days that James first tendered his doctrine that "the bodily changes follow directly the perception of the exciting fact".
Journal ArticleDOI
The Theory of Stochastic Processes. By D. R. Cox and H. D. Miller. Pp. x, 398. 70s. (Methuen)
Journal ArticleDOI
Balancing Type I Error and Power in Linear Mixed Models
TL;DR: This paper showed that for typical psychological and psycholinguistic data, higher power is achieved without inflating Type I error rate if a model selection criterion is used to select a random effect structure that is supported by the data.
References
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A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book
Judgment Under Uncertainty: Heuristics and Biases
Amos Tversky,Daniel Kahneman +1 more
TL;DR: The authors described three heuristics that are employed in making judgements under uncertainty: representativeness, availability of instances or scenarios, and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.