Modeling psychophysical data in R

Kenneth Knoblauch, Laurence Maloney

Research output: Book/ReportBook

Abstract

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.

Original languageEnglish (US)
PublisherSpringer New York
Number of pages367
ISBN (Electronic)9781461444756
ISBN (Print)9781461444749
DOIs
StatePublished - Jan 1 2012

Fingerprint

Data structures
Computer programming languages
Maximum likelihood
Statistical methods
Image classification
Signal detection
Display devices

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Modeling psychophysical data in R. / Knoblauch, Kenneth; Maloney, Laurence.

Springer New York, 2012. 367 p.

Research output: Book/ReportBook

Knoblauch, Kenneth ; Maloney, Laurence. / Modeling psychophysical data in R. Springer New York, 2012. 367 p.
@book{a2c3ed14b721462c9dfa4f52ea2b6b1b,
title = "Modeling psychophysical data in R",
abstract = "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.",
author = "Kenneth Knoblauch and Laurence Maloney",
year = "2012",
month = "1",
day = "1",
doi = "10.1007/978-1-4614-4475-6",
language = "English (US)",
isbn = "9781461444749",
publisher = "Springer New York",
address = "United States",

}

TY - BOOK

T1 - Modeling psychophysical data in R

AU - Knoblauch, Kenneth

AU - Maloney, Laurence

PY - 2012/1/1

Y1 - 2012/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84860698812&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84860698812&partnerID=8YFLogxK

U2 - 10.1007/978-1-4614-4475-6

DO - 10.1007/978-1-4614-4475-6

M3 - Book

SN - 9781461444749

BT - Modeling psychophysical data in R

PB - Springer New York

ER -