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| Ïîèñê ïî óêàçàòåëÿì |
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| Macmillan N., Creelman D. — Detection Theory: A User's Guide |
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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 " " " "B''"
Likelihood ratio, decision rule 42—44
Likelihood ratio, SDT see " "
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 " " " " " "
Sensitivity measures for unit-slope ROCs see "d'" "
Sensitivity measures in Choice Theory see " "
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 "" "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 " "
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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