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2012 | OriginalPaper | Buchkapitel

3. Signal Detection Theory

verfasst von : Kenneth Knoblauch, Laurence T. Maloney

Erschienen in: Modeling Psychophysical Data in R

Verlag: Springer New York

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Abstract

Signal detection theory (SDT), developed in the 1950s, is a framework of statistical methods used to model how observers classify sensory events [72, 169]. In this chapter we describe commonly used signal detection models and methods for fitting them. A recurring theme in this book is the use of the generalized linear model (GLM) to fit psychophysical data, and here we describe the use of the GLM to fit signal detection data.

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Vorheriges Kapitel Modeling in R
Nächstes Kapitel The Psychometric Function: Introduction
Fußnoten
1
The log of the odds ratio is the default link function, the “logit,” used with a binomial GLM.
 
2
The incident on which this anecdote is based is recounted at greater length in [155].
 
3
Since the matrix of this system of equations is an involution (i.e., it is its own inverse), we do not need to use solve, but doing so so helps us keep track of which set of variables we are computing.
 
4
Note that this assumes that level A, the first level of the factor, corresponds to the signal absent case. See ?relevel for a simple way to reset the first level of a factor variable.
 
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Metadaten
Titel
Signal Detection Theory
verfasst von
Kenneth Knoblauch
Laurence T. Maloney
Copyright-Jahr
2012
Verlag
Springer New York
Buch
Modeling Psychophysical Data in R

Print ISBN: 978-1-4614-4474-9

Electronic ISBN: 978-1-4614-4475-6

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-1-4614-4475-6_3