Elementary Signal Detection TheorySignal 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. |
Contents
I | 3 |
II | 6 |
III | 9 |
IV | 15 |
V | 16 |
VI | 17 |
VII | 20 |
VIII | 22 |
XLI | 114 |
XLII | 118 |
XLIII | 124 |
XLIV | 129 |
XLVI | 131 |
XLVII | 137 |
XLVIII | 140 |
XLIX | 143 |
IX | 26 |
X | 32 |
XI | 36 |
XII | 37 |
XIII | 39 |
XIV | 42 |
XV | 45 |
XVI | 48 |
XVII | 52 |
XVIII | 56 |
XIX | 58 |
XXI | 60 |
XXII | 61 |
XXIII | 64 |
XXIV | 66 |
XXV | 72 |
XXVI | 74 |
XXVII | 78 |
XXVIII | 81 |
XXX | 83 |
XXXI | 85 |
XXXII | 88 |
XXXIII | 91 |
XXXIV | 93 |
XXXV | 96 |
XXXVI | 98 |
XXXVII | 104 |
XXXVIII | 106 |
XXXIX | 111 |
XL | 113 |
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Common terms and phrases
analysis assumptions Atrap axis bivariate calculated Chapter chi-square distribution components confidence interval correct response criteria cumulative distribution function decision bound degrees of freedom density function described detection model detection statistics detection task detection theory discrimination equal equal-variance Gaussian model equal-variance model Equation estimates Example experiment false-alarm rate Figure fn(x frequencies Gaussian coordinates Gaussian distribution gives goodness-of-fit H₁ high-threshold model hit rate hypothesis independent interval likelihood likelihood-ratio test log ẞ logistic distribution mean measure multinomial distribution multivariate noise distribution noise events noise stimuli noise trials null hypothesis observer's operating characteristic optimal optimal bias parameters performance plotted procedure random variable rating-scale ratio representation sampling Section session signal and noise signal distribution signal trials signal-detection model signal-detection theory standard deviation standard error stimulus Table theoretical two-alternative univariate variance YES responses yes/no
Popular passages
Page vi - The goal of this book is to introduce the reader to the most important aspects of signal-detection theory.
Page 255 - Probability functions. In M. Abramowitz & IA Stegun (Eds.), Handbook of mathematical functions with formulas, graphs, and mathematical tables (pp. 925-995). Washington, DC: National Bureau of Standards. Index Page numbers in italic type refer to computational examples. Page numbers followed by "n
Page 255 - Maximum-likelihood estimation of a multivariate Gaussian rating model with excluded data. Journal of Mathematical Psychology, 36, 213-234.