Elementary Signal Detection Theory
1st Edition
Published on 11. October 2001
277 pages
978-0-19-535780-6 (ISBN)
Description
Signal detection theory, as developed in electrical engineering and based on statistical decision theory, was first applied to human sensory discrimination about 40 years ago. The theory's intent was to explain how humans discriminate and how we might use reliable measures to quantify this ability. An interesting finding of this work is that decisions are involved even in the simplest of discrimination tasks--say, determining whether or not a sound has been heard (a yes-no decision). Detection theory has been applied to a host of varied problems (for example, measuring the accuracy of diagnostic systems, survey research, reliability of lie detection tests) and extends far beyond the detection of signals. This book is a primer on signal detection theory, useful for both undergraduates and graduate students.
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Thomas D. Wickens
Elementary Signal Detection Theory
Book
10/2001
Oxford University Press Inc
€109.70
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Thomas D. Wickens is Professor of Psychology at the University of California, Los Angeles.
Content
- Intro
- Contents
- 1 The signal-detection model
- 1.1 Some examples
- 1.2 Hits and false alarms
- 1.3 The statistical decision representation
- Reference notes
- Exercises
- 2 The equal-variance Gaussian model
- 2.1 The Gaussian detection model
- 2.2 The equal-variance model
- 2.3 Estimating d' and ?
- 2.4 Measuring bias
- 2.5 Ideal observers and optimal performance
- Reference notes
- Exercises
- 3 Operating characteristics and the Gaussian model
- 3.1 The operating characteristic
- 3.2 Isocriterion and isobias contours
- 3.3 The equal-variance Gaussian operating characteristic
- 3.4 The unequal-variance Gaussian model
- 3.5 Fitting an empirical operating characteristic
- 3.6 Computer programs
- Reference notes
- Exercises
- 4 Measures of detection performance
- 4.1 The distance between distributions
- 4.2 Distances to the isosensitivity line
- 4.3 The area under the operating characteristic
- 4.4 Recommendations
- 4.5 Measures of bias
- 4.6 Aggregation of detection statistics
- Reference notes
- Exercises
- 5 Confidence ratings
- 5.1 The rating experiment
- 5.2 The detection model for rating experiments
- 5.3 Fitting the rating model
- Exercises
- 6 Forced-choice procedures
- 6.1 The forced-choice experiment
- 6.2 The two-alternative forced-choice model
- 6.3 Position bias
- 6.4 Forced-choice and yes/no detection tasks
- 6.5 The K-alternative forced-choice procedure
- Exercises
- 7 Discrimination and identification
- 7.1 The two-alternative discrimination task
- 7.2 The relationship between detection and discrimination
- 7.3 Identification of several stimuli
- Reference notes
- Exercises
- 8 Finite-state models
- 8.1 The high-threshold model
- 8.2 The high-threshold operating characteristic
- 8.3 Other finite-state representations
- 8.4 Rating-scale data
- Reference notes
- Exercises
- 9 Likelihoods and likelihood ratios
- 9.1 Likelihood-ratio tests
- 9.2 The Bayesian observer
- 9.3 Likelihoods and signal-detection theory
- 9.4 Non-Gaussian distributions
- Reference notes
- Exercises
- 10 Multidimensional stimuli
- 10.1 Bivariate signal detection
- 10.2 Likelihood ratios
- 10.3 Compound signals
- 10.4 Signals with correlated components
- 10.5 Uncertainty effects
- Reference notes
- Exercises
- 11 Statistical treatment
- 11.1 Variability in signal-detection studies
- 11.2 Fundamental sampling distributions
- 11.3 Simple detection statistics
- 11.4 Confidence intervals and hypothesis tests
- 11.5 Goodness-of-fit tests
- 11.6 Comparison of hierarchical models
- 11.7 Interobserver variability
- Reference notes
- Exercises
- Appendix: A summary of probability theory
- A.1 Basic definitions
- A.2 Random variables
- A.3 Some specific distributions
- References
- Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- R
- S
- T
- U
- V
- W
- X
- Z
