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© 2018

Likelihood-Free Methods for Cognitive Science

Book

Table of contents

  1. Front Matter
    Pages i-xiv
  2. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 1-11
  3. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 13-53
  4. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 55-79
  5. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 81-93
  6. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 95-114
  7. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 115-117
  8. James J. Palestro, Per B. Sederberg, Adam F. Osth, Trisha Van Zandt, Brandon M. Turner
    Pages 119-119
  9. Back Matter
    Pages 121-129

About this book

Introduction

This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function. As a result, ABC can be used to estimate posterior distributions of parameters for simulation-based models. Simulation-based models are now very popular in cognitive science, as are Bayesian methods for performing parameter inference. As such, the recent developments of likelihood-free techniques are an important advancement for the field.

Chapters discuss the philosophy of Bayesian inference as well as provide several algorithms for performing ABC. Chapters also apply some of the algorithms in a tutorial fashion, with one specific application to the Minerva 2 model. In addition, the book discusses several applications of ABC methodology to recent problems in cognitive science.

Likelihood-Free Methods for Cognitive Science will be of interest to researchers and graduate students working in experimental, applied, and cognitive science. 


Keywords

Likelihood-free Bayesian analysis Approximate Bayesian computation Minerva 2 Tutorial Model Estimation Probability Density Approximation ABCDE Gibbs ABC Cognitive Psychology Modeling Bayesian methods

Authors and affiliations

  1. 1.Department of PsychologyThe Ohio State UniversityColumbusUSA
  2. 2.Department of PsychologyThe Ohio State UniversityColumbusUSA
  3. 3.University of MelbourneParkvilleAustralia
  4. 4.Department of PsychologyThe Ohio State UniversityColumbusUSA
  5. 5.Department of PsychologyThe Ohio State UniversityColumbusUSA

About the authors

James J. Palestro is a doctoral student in the Psychology Department at The Ohio State University. He received a B.A. from Youngstown State University in psychology in 2012 and a M.A. from the Ohio State University in 2017. His research interests include cognitive modeling and the neural bases of perceptual decision making.  

Per B. Sederberg is an Associate Professor in the Department of Psychology at the University of Virginia. He received his undergraduate degree in Cognitive Science from the University of Virginia in 1996. He then worked as a computer programmer in industry before returning to earn his Ph.D. in Neuroscience from the University of Pennsylvania in 2006. He spent four years as a postdoctoral fellow at Princeton University where he studied machine-learning applications to the study of the brain. In 2010 he began as an Assistant Professor at The Ohio State University and moved to the University of Virginia in the summer of 2017. As a computational cognitive neuroscientist, his research seeks to develop a comprehensive mathematical understanding of cognition, with a particular focus on human memory and decision-making, that links neuroscience and behavior.

Adam F. Osth is a Lecturer in the Melbourne School of Psychological Sciences at the University of Melbourne. He received a B.A. in psychology from the University of California Santa Cruz, and both an M. A. in 2011 and Ph D in 2014 in Psychology from The Ohio State University. He spent two years as a postdoctoral fellow at The University of Newcastle in New South Wales, Australia. His research focuses on both empirical and computational modeling investigations of episodic memory, with focuses in recognition memory, serial order memory, and the integration of models of memory and decision making.

Trisha Van Zandt is a professor of Psychology at The Ohio State University. She is a member of the Society for Mathematical Psychology, of which she was President in 2006-2007, and the American Statistical Association. She has received multiple research grants from the National Science Foundation and the Presidential Early Career Award for Scientists and Engineers in 1997. She is co-author of review chapters “Designs for and Analyses of Response Time Experiments” in the Oxford Handbook of Quantitative Methods and “Mathematical Psychology” in the APA Handbook of Research Methods in Psychology.

Brandon M. Turner is an Assistant Professor in the Psychology Department at The Ohio State University. He received a B.S. from Missouri State University in mathematics and psychology in 2008, a MAS from The Ohio State University in statistics in 2010, and a Ph.D. from The Ohio State University in 2011. He then spent one year as a postdoctoral researcher at University of California, Irvine, and two years as a postdoctoral fellow at Stanford University. His research interests include dynamic models of cognition and perceptual decision making, efficient methods for performing likelihood-free and likelihood-informed Bayesian inference, and unifying behavioral and neural explanations of cognition. 

Bibliographic information

  • Book Title Likelihood-Free Methods for Cognitive Science
  • Authors James J. Palestro
    Per B. Sederberg
    Adam F. Osth
    Trisha Van Zandt
    Brandon M. Turner
  • Series Title Computational Approaches to Cognition and Perception
  • Series Abbreviated Title Computational Approaches to Cognition and Perception
  • DOI https://doi.org/10.1007/978-3-319-72425-6
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Behavioral Science and Psychology Behavioral Science and Psychology (R0)
  • Hardcover ISBN 978-3-319-72424-9
  • Softcover ISBN 978-3-319-89181-1
  • eBook ISBN 978-3-319-72425-6
  • Series ISSN 2510-1889
  • Series E-ISSN 2510-1897
  • Edition Number 1
  • Number of Pages XIV, 129
  • Number of Illustrations 20 b/w illustrations, 7 illustrations in colour
  • Topics Cognitive Psychology
  • Buy this book on publisher's site