Modeling Psychophysical Data in R [electronic resource] /by Kenneth Knoblauch, Laurence T. Maloney.
Contributor(s): Maloney, Laurence T [author.] | SpringerLink (Online service).Material type: BookSeries: Use R!: 32Publisher: New York, NY : Springer New York : Imprint: Springer, 2012Description: XV, 365 p. 103 illus., 4 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461444756.Subject(s): Statistics | Statistics | Statistics and Computing/Statistics Programs | Statistical Theory and Methods | Statistics, general | Statistics for Social Science, Behavorial Science, Education, Public Policy, and LawDDC classification: 519.5 Online resources: Click Here-Springer eBooks (In-Campus Access) | Click Here-Springer eBooks (Off-Campus Access)
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A First Tour through R by Example -- Modeling in R -- Signal Detection Theory (SDT) -- The Psychometric Function: Introduction -- The Psychometric Function: Continuation -- Classification Images -- Maximum Likelihood Difference Scaling (MLDS) -- Maximum Likelihood Conjoint Measurement (MLCM) -- Mixed-Effect Models -- Some Basics of R -- Statistical Background -- References -- Index.
Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM). This book illustrates how to fit data from a variety of psychophysical paradigms using modern statistical methods and the statistical language R. The paradigms include signal detection theory, psychometric function fitting, classification images and more. In two chapters, recently developed methods for scaling appearance, maximum likelihood difference scaling and maximum likelihood conjoint measurement are examined. The authors also consider the application of mixed-effects models to psychophysical data. R is an open-source programming language that is widely used by statisticians and is seeing enormous growth in its application to data in all fields. It is interactive, containing many powerful facilities for optimization, model evaluation, model selection, and graphical display of data. The reader who fits data in R can readily make use of these methods. The researcher who uses R to fit and model his data has access to most recently developed statistical methods. This book does not assume that the reader is familiar with R, and a little experience with any programming language is all that is needed to appreciate this book. There are large numbers of examples of R in the text and the source code for all examples is available in an R package MPDiR available through R. Kenneth Knoblauch is a researcher in the Department of Integrative Neurosciences in Inserm Unit 846, The Stem Cell and Brain Research Institute and associated with the University Claude Bernard, Lyon 1, in France. Laurence T. Maloney is Professor of Psychology and Neural Science at New York University. His research focusses on applications of mathematical models to perception, motor control and decision making.