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Macmillan N., Creelman D. — Detection Theory: A User's Guide
Macmillan N., Creelman D. — Detection Theory: A User's Guide



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Íàçâàíèå: Detection Theory: A User's Guide

Àâòîðû: Macmillan N., Creelman D.

Àííîòàöèÿ:

Originally developed for evaluation of electronic detection, detection theory was adopted by psychologists as a way to understand sensory decision-making and has been used in areas as diverse as animal behavior and X-ray diagnosis. This resource for students, behavioral scientists and other researchers explains the basic principles of detection theory. Both one-dimensional and multidimensional models are discussed. The second edition features a new chapter on ideal observers and updated material on adaptive threshold measurement.


ßçûê: en

Ðóáðèêà: Ìàòåìàòèêà/Âåðîÿòíîñòü/Ñòàòèñòèêà è ïðèëîæåíèÿ/

Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö

ed2k: ed2k stats

Èçäàíèå: 2nd edition

Ãîä èçäàíèÿ: 2004

Êîëè÷åñòâî ñòðàíèö: 492

Äîáàâëåíà â êàòàëîã: 05.06.2005

Îïåðàöèè: Ïîëîæèòü íà ïîëêó | Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
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Ïðåäìåòíûé óêàçàòåëü
Isobias curve, empirical      39—40
Isobias curve, monotonicity of      40—41
Isosensitivity curve      see "ROC"
Jittering      181
jnd (just-noticeable difference)      22 120—121
Joint distribution      146
Kaernbach’s adaptive method      277 282 292 293
Least-squares estimation      354
Lie detection      6 49
Likelihood ratio for unequal-variance model      67—69
Likelihood ratio in multi-interval designs      see "Response bias under specific designs"
Likelihood ratio, as response bias measure      see "B''" " " "
Likelihood ratio, as ROC slope      
33—34
Likelihood ratio, Choice Theory      see "$\beta_L$" " "B''"
Likelihood ratio, decision rule      42—44
Likelihood ratio, SDT      see "$\beta$"
Log-odds transformation      95—96
Log-odds transformation and Choice Theory      95—96
Log-odds transformation and logistic regression      337—339
Logarithms      357—358
Logistic distribution      108—109 349
Logistic distribution, as psychometric function      275 284 see Choice "Log-odds
Logistic regression      337—339
mAFC      246
mAFC in adaptive methods      293
mAFC vs 2AFC      249 251 293
mAFC vs other designs      253—255
mAFC, as an example of multidimensional identification      249—250
mAFC, decision space      249—250
mAFC, psychometric functions      253
mAFC, response bias      250—251
mAFC, sensitivity      250 426—430
mAFC, statistical properties of d'      329—330
mAFC, threshold model      251—252
Market research      250—251
Matching experiment      182—183
Matching experiment, brightness      271—272
Matching-to-sample      see "ABX"
Maximum (-output) rule      154—158 see
Maximum-likelihood estimation      291 354—355
Maximum-likelihood estimation of empirical thresholds      284—285 294
Maximum-likelihood estimation of ROCs      70 330
Mean (-shift) integrality      195—196
Mean category scale      130—131
Memory as limitation in perception      133—135 175—179
Minimum (-output) rule      154—158
MISS      xviii 4 142—144
MLE      see "Maximum-likelihood estimation"
Monte Carlo techniques      see "Computer simulations"
Multidimensional Signal Detection Analysis (MSDA)      260—262 433
Multiple-choice exams      249—252
Multiple-look experiments      206—207
Noise, external      see "Variance external"
Noise, internal      see "Variance internal"
Nonparametric analysis      100—104 130—132
Normal distribution      35 320—322 348—349 374—378
Normal distribution, as psychometric function      117—120 274 see "z-transformation"
Normal distribution, bivariate      144—152 322—323 349—351
Oddity      235—238
Oddity vs other designs      253—255
Oddity, decision space      236—237
Oddity, differencing model      236—237
Oddity, independent-observation model      237
Oddity, sensitivity      236—238 420—425
Oddity, statistical properties of d'      329—330
Oddity, threshold model      238
One-interval design      1 see "Yes-no
One-interval design vs other designs      see "Specific design"
Optimality      see "Decision rule likelihood "Ideal
p(c) (proportion correct)      7
p(c) (proportion correct) and d'      9—13
p(c) (proportion correct), as sensitivity measure in 2AFC      170—175
p(c) (proportion correct), as sensitivity measure in identification      131—132 see
p(c) (proportion correct), as sensitivity measure in yes-no      7
Parameter estimation      319
Parameter Estimation by Sequential Testing      see "PEST"
Parameter estimation, pooled sensitivity and bias      331—337
Parameter estimation, proportions      320—322
Parameter estimation, response bias      328
Parameter estimation, ROC points      322—323
Parameter estimation, sensitivity      323—330 see "Maximum-likelihood
payoffs      71
Perceptual dependence and independence      149 260 262
Perceptual integrality      195—196 260—262
Perceptual separability      195—196 260—262
Perfect performance      see "Sensitivity near-perfect"
PEST      282—287
PEST vs other methods      291
PEST, MOUSE and RAT modes      286
PEST, stepping rules      282—283
Point of subjective equality      see "PSE"
Poisson distribution      301—302
Pooled data      331—337
Presentation probabilities      7
Presentation probabilities and bias      42—44
Presentation probabilities and ROC generation      72
Presentation probabilities in one-dimensional classification      129—130
probability      343—351
Probit analysis      274 293 see as
Product rule      151 350
Projection of multidimensional distributions      146—149
Proportion correct      see "p(c)"
PSE      120—121 273
Pseudo-d'      122 124
Psychometric function      119 272—276
Psychometric function in 2AFC      273—274
Psychometric function in mAFC      253 see "Normal "Weibull
Psychometric function, shape of      273—276
Psychometric function, slope      293
Psychophysics vs psychoacoustics      312—313
Psychophysics, history      22—24
Quest      284—286
QUEST vs other methods      286 291
Radiology      see "X-ray reading"
Random variables      345—349
Range-frequency model      130
Rating experiment      2 51—70
Rating experiment, calculating response rates      53—57
Rating experiment, decision space      64—69
Rating experiment, design      51—52
Rating experiment, graphing data      55—57
Rating experiment, response sets      52 see
Receiver operating characteristic      see "ROC"
Recognition      1
Recognition of faces      3—6
Recognition of letters      246—249
Recognition of odors      51—57 64—66
Recognition of words      40 57—59 90—92 160—161 166—170 185 193—194
Rectangular distribution      see "Underlying distributions in threshold theory"
Relative operating characteristic      see "ROC"
Reminder paradigm      180—182 255
Reminder paradigm vs other designs      181—183 see
Response bias      27—44 362 366
Response bias in below-chance performance      41
Response bias in multi-interval designs      see "Response bias under specific design"
Response bias measures      362 363 366
Response bias measures and sensitivity measures      41—42
Response bias measures for rating experiment      64—69
Response bias measures in multi-interval designs      see "Response bias under specific design"
Response bias measures, characteristics of      28—29
Response bias measures, Choice Theory      see "b" "b'" "B''" " "
Response bias measures, comparisons of      
36—42
Response bias measures, nonparametric      see "B''" "
Response bias measures, SDT      see "c" "c'" "
Response bias measures, threshold theory      
85—86 see "False-alarm as "Yes
Response bias, as criterion location      29—31
Reversal (in adaptive methods)      283—286
Reward function      see "Payoffs"
ROC      10 51—77
ROC for group data      337
ROC in multi-interval designs      see "ROC under specific design"
ROC in z-coordinates      11 55—59 see ROC" "Rating
ROC slope (linear coordinates)      11 33—34
ROC slope (z-coordinates)      14 59 330—331
ROC slope (z-coordinates) and sensitivity      74—77
ROC slope (z-coordinates) and uncertainty      76
ROC slope (z-coordinates) in multi-interval designs      see "ROC under specific design"
ROC slope (z-coordinates), nonunit slope      57—59
ROC slope (z-coordinates), unit slope      14
ROC space      10
ROC, empirical      55—59 66—77
ROC, fitting to data      70 330 433
ROC, generation methods      71—72
ROC, implied      9—13
ROC, regularity      11 18
ROC, symmetry      14
ROC, threshold      12—13 83—84 89—92 110
ROC, Type-2      73—74
Roving discrimination      see "Designs roving"
S      see "ROC slope (z-coordinates)"
S',(sensitivity measure for rating design)      104
Same-different      214—228
Same-different vs other designs      216—217 228 253—255
Same-different, decision space      215—218 222—224
Same-different, differencing model      221—227
Same-different, hit and false-alarm rates      215 223
Same-different, independent-observation model      216—217
Same-different, isobias curves      219—220 226—227
Same-different, response bias      218—220 225—227
Same-different, ROC      220 223—225
Same-different, sensitivity      216—220 223—225 380—419
Same-different, statistical properties of d'      329—330
Same-different, threshold model      217—218
Sampling distribution      351—352
Saturated model      see "Logistic regression"
Sensitivity      3 361 363 365
Sensitivity in multi-interval designs      see "Sensitivity under specific design"
Sensitivity measure in ROC space      12 59—64
Sensitivity measures      3 361 362 365 see "
Sensitivity measures and bias measures      
41—42
Sensitivity measures for nonunit-slope ROCs      see "$A_z$" " " " "
Sensitivity measures for unit-slope ROCs      
see "d'" "
Sensitivity measures in Choice Theory      
see "$\alpha$"
Sensitivity measures in classification, one-dimensional      see "Classification one-dimensional sensitivity"
Sensitivity measures in multi-interval designs      see "Sensitivity under specific design"
Sensitivity measures in SDT      see "$A_z$" "d'" " " " "
Sensitivity measures in threshold theory      
82—89
Sensitivity measures, area-based      see "A'" " "Area "
Sensitivity measures, characteristics of      
5—7
Sensitivity measures, nonparametric      see "A'" " "S'"
Sensitivity, as mean difference in decision space      18—20
Sensitivity, as perceptual distance      15
Sensitivity, medical use of term      6
Sensitivity, near-chance      8—9 40—41
Sensitivity, near-perfect      8—9 129 224—225 321 336
Separability      194—195
Sequential effects      183
Simulations      see "Computer simulations"
Simultaneous detection and identification      255—259
Simultaneous simple and compound detection      200—202
Specificity      6
Staircase procedure      281—282
Standard stimulus      113—114 see
State diagram      81
Statistical bias      352
Statistical bias of d' estimates      323—325
Statistical bias of pooled sensitivity estimates      331—335
Statistical bias of threshold estimates      290
Statistics      351—355
Statistics and detection theory      319—341 see "Maximum-likelihood "Parameter
Stimulus repetition      see "Compound detection" "Multiple
Subliminal perception      105—106 258—259
Sweat factor      290
Target proportion (of an adaptive method)      see "Adaptive methods target
Threshold and response bias      287—289
Threshold theories      81—94 104—107
Threshold theories for multi-interval designs      see "Specific design"
Threshold theories, double high-threshold      88—94
Threshold theories, low threshold      86—88
Threshold theories, single high-threshold      82—86
Threshold theories, three-state      110
Threshold, compared with criterion      22—23
Threshold, empirical      119—120 269—296
Thurstonian scaling      see "Classification one-dimensional"
Time order errors      176—177
Trace coding      178—179
Trace-context theory      133—135 178—179 310—311
Trading relations      114 124—126
Training, effects of      46
Transformations, arcsine      103
Transformations, log-odds      95
Transformations, logarithmic and exponential      274 357—358
Triangular method      see "Oddity"
Type-I error      44
UDTR      278 281 289
UDTR vs other methods      292
UDTR, decision rule      278
Unbiased performance      see "$p(c)_{max}$"
Uncertain detection      188—202
Uncertain detection on one dimension      189—191
Uncertain detection vs identification      see "Simultaneous detection and identification"
Uncertain detection, independent-observation rule      197—199
Uncertain detection, optimal model      199
Uncertain detection, summation rule      196—197
Uncertainty      see also "Uncertain detection"
Uncertainty, extrinsic vs intrinsic      188
Underlying distributions      16
Underlying distributions and transformations      19—20
Underlying distributions in Choice Theory      98—100
Underlying distributions in multi-interval designs      see "Decision space under specific design"
Underlying distributions in SDT      16—20
Underlying distributions in threshold theory      82—91
Underlying distributions with unequal variances      57—64 173—175
Underlying distributions yes-no      16 see
Underlying distributions, multidimensional      144—152
Unsaturated model      see "Logistic regression"
Up-Down Transformed Method      see "UDTR"
Variance (in statistics)      see "Confidence interval under specific statistic"
Variance, external      297—298 302—303
Variance, internal      297—298 302—303
Variance, internal, context      134—135
Variance, internal, sensory      134—135 178—179
Variance, internal, trace      178—179 see incomplete"
Visual search      311
Wald rule in adaptive methods      278—280
Weibull function      275—276
X-ray reading      28—35
Yes rate      92—93
Yes-no design      1—50 361—362
Yes-no design in adaptive methods      271—272 293
Yes-no design vs other designs      167—168 175—176 181—182 228 234 253—255
Z-transformation      8
z-transformation for one-dimensional classification      117—128
z-transformation for psychometric functions      117—121
z-transformation for ROCs      11—12 55—64
z-transformation, variance of      325—327
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