Description
Book SynopsisSignal 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.
Trade Review"This book contains the theoretical explications of the ways observers detect weak, uncertain, or ambiguous signals. It explains the math underlying the theory, and outlines its uses in measuring an observer's sensitivity. The book is intended to serve as an introductory text for undergraduate or graduate courses in sensation and perception, psychophysics, cognition, and quantitative methods; it may also be used as a reference for researchers. Wickens teaches at the University of California, Los Angeles."--SciTech Book News "This book contains the theoretical explications of the ways observers detect weak, uncertain, or ambiguous signals. It explains the math underlying the theory, and outlines its uses in measuring an observer's sensitivity. The book is intended to serve as an introductory text for undergraduate or graduate courses in sensation and perception, psychophysics, cognition, and quantitative methods; it may also be used as a reference for researchers. Wickens teaches at the University of California, Los Angeles."--SciTech Book News
Table of Contents1. The signal-detection model ; 2. The equal-variance Gaussian model ; 3. Operating characteristics and the Gaussian model ; 4. Measures of detection performance ; 5. Confidence ratings ; 6. Forced-choice procedures ; 7. Discrimination and identification ; 8. Finite-state models ; 9. Likelihoods and likelihood ratios ; 10. Multidimensional stimuli ; 11. Statistical treatment ; A. A summary of probability theory ; References
