Detection Theory : A User's Guide

Detection Theory is an introduction to one of the most important tools for analysis of data where choices must be made and performance is not perfect. Originally developed for evaluation of electronic detection, detection theory was adopted by psychologists as a way to understand sensory decision making, then embraced by students of human memory. It has since been utilized in areas as diverse as animal behavior and X-ray diagnosis. This book covers the basic principles of detection theory, with separate initial chapters on measuring detection and evaluating decision criteria. Some other features include: \*complete tools for application, including flowcharts, tables, pointers, and software; \*student-friendly language; \*complete coverage of content area, including both one-dimensional and multidimensional models; \*separate, systematic coverage of sensitivity and response bias measurement; \*integrated treatment of threshold and nonparametric approaches; \*an organized, tutorial level introduction to multidimensional detection theory; \*popular discrimination paradigms presented as applications of multidimensional detection theory; and \*a new chapter on ideal observers and an updated chapter on adaptive threshold measurement. This up-to-date summary of signal detection theory is both a self-contained reference work for users and a readable text for graduate students and other researchers learning the material either in courses or on their own. Contents......Page 8 Preface......Page 14 Introduction......Page 18 PART I. Basic Detection Theory and One-Interval Designs......Page 22 Understanding Yes-No Data......Page 24 Implied ROCs......Page 30 The Signal Detection Model......Page 37 Calculational Methods......Page 41 Essay: The Provenance of Detection Theory......Page 43 Summary......Page 45 Problems......Page 46 Two Examples......Page 48 Measuring Response Bias......Page 49 Alternative Measures of Bias......Page 52 Isobias Curves......Page 56 Comparing the Bias Measures......Page 57 How Does the Participant Choose a Decision Rule?......Page 63 Coda: Calculating Hit and False-Alarm Rates From Parameters......Page 65 Essay: On Human Decision Making......Page 67 Summary......Page 68 Problems......Page 69 Design of Rating Experiments......Page 72 ROC Analysis......Page 74 ROC Analysis With Slopes Other Than 1......Page 78 Estimating Bias......Page 85 Systematic Parameter Estimation and Calculational Methods......Page 91 Alternative Ways to Generate ROCs......Page 92 Another Kind of ROC: Type 2......Page 94 Essay: Are ROCs Necessary?......Page 95 Computational Appendix......Page 98 Problems......Page 99 4 Alternative Approaches: Threshold Models and Choice Theory......Page 102 Single High-Threshold Theory......Page 103 Low-Threshold Theory......Page 107 Double High-Threshold Theory......Page 109 Choice Theory......Page 115 Measures Based on Areas in ROC Space: Unintentional Applications of Choice Theory......Page 121 Essay: The Appeal of Discrete Models......Page 125 Summary......Page 128 Computational Appendix......Page 129 Problems......Page 130 Design of Classification Experiments......Page 134 Perceptual One-Dimensionality......Page 135 Two-Response Classification......Page 136 Experiments With More Than Two Responses......Page 147 Nonparametric Measures......Page 151 Comparing Classification and Discrimination......Page 153 Summary......Page 156 Problems......Page 157 PART II. Multidimensional Detection Theory and Multi-Interval Discrimination Designs......Page 160 6 Detection and Discrimination of Compound Stimuli: Tools for Multidimensional Detection Theory......Page 162 Distributions in One- and Two-Dimensional Spaces......Page 163 Some Characteristics of Two-Dimensional Spaces......Page 170 Compound Detection......Page 173 Inferring the Representation From Data......Page 180 Problems......Page 182 7 Comparison (Two-Distribution) Designs for Discrimination......Page 186 Two-Alternative Forced Choice (2AFC)......Page 187 Reminder Paradigm......Page 201 Essay: Psychophysical Comparisons and Comparison Designs......Page 203 Problems......Page 205 8 Classification Designs: Attention and Interaction......Page 208 One-Dimensional Representations and Uncertainty......Page 209 Two-Dimensional Representations......Page 212 Two-Dimensional Models for Extrinsic Uncertain Detection......Page 217 Uncertain Simple and Compound Detection......Page 221 Selective and Divided Attention Tasks......Page 223 Attention Operating Characteristics (AOCs)......Page 227 Summary......Page 230 Problems......Page 231 9 Classification Designs for Discrimination......Page 234 Same-Different......Page 235 ABX (Matching-to-Sample)......Page 250 Oddity (Triangular Method)......Page 256 Summary......Page 259 Computational Appendix......Page 261 Problems......Page 263 10 Identification of Multidimensional Objects and Multiple Observation Intervals......Page 266 Object Identification......Page 267 Interval Identification: m-Alternative Forced Choice (mAFC)......Page 270 Comparisons Among Discrimination Paradigms......Page 273 Simultaneous Detection and Identification......Page 276 Using Identification to Test for Perceptual Interaction......Page 280 Essay: How to Choose an Experimental Design......Page 283 Summary......Page 285 Problems......Page 286 PART III. Stimulus Factors......Page 288 11 Adaptive Methods for Estimating Empirical Thresholds......Page 290 Two Examples......Page 291 Psychometric Functions......Page 293 The Tracking Algorithm: Choices for the Adaptive Tester......Page 298 Evaluation of Tracking Algorithms......Page 310 Two More Choices: Discrimination Paradigm and the Issue of Slope......Page 313 Summary......Page 315 Problems......Page 316 12 Components of Sensitivity......Page 318 Stimulus Determinants of d' in One Dimension......Page 319 Basic Processes in Multiple Dimensions......Page 325 Hierarchical Models......Page 331 Essay: Psychophysics versus Psychoacoustics (etc.)......Page 333 Problems......Page 335 PART IV. Statistics......Page 338 13 Statistics and Detection Theory......Page 340 Hit and False-Alarm Rates......Page 341 Sensitivity and Bias Measures......Page 344 Sensitivity Estimates Based on Averaged Data......Page 352 Systematic Statistical Frameworks for Detection Theory......Page 358 Summary......Page 360 Computational Appendix......Page 361 Problems......Page 362 Probability......Page 364 Statistics......Page 372 Appendix 2 Logarithms and Exponentials......Page 378 Appendix 3 Flowcharts to Sensitivity and Bias Calculations......Page 380 Chart 1: Guide to Subsequent Charts......Page 381 Chart 2: Yes-No Sensitivity......Page 382 Chart 3: Yes-No Response Bias......Page 383 Chart 4: Rating-Design Sensitivity......Page 384 Chart 5: Definitions of Multi-Interval Designs......Page 385 Chart 6: Multi-Interval Sensitivity......Page 386 Chart 7: Multi-Interval Bias......Page 387 Chart 8: Classification......Page 388 Appendix 4 Some Useful Equations......Page 390 A5.1. Normal Distribution (p to z), for Finding d', c, and Other SDT Statistics......Page 395 A5.2. Normal Distribution (z to p)......Page 397 A5.3. Values of d' for Same-Different (Independent-Observation Model) and ABX (Independent-Observation and Differencing Models)......Page 401 A5.4. Values of d' for Same-Different (Differencing Model)......Page 422 A5.5. Values of d' for Oddity, Gaussian Model......Page 441 A5.6. Values of p(c) given d' for Oddity (Differencing and Independent-Observation Model, Normal)......Page 445 A5.7. Values of d' for m-Interval Forced Choice or Identification......Page 447 Listing......Page 452 Web Sites......Page 454 Appendix 7 Solutions to Selected Problems......Page 456 A......Page 468 B......Page 469 C......Page 470 D......Page 472 E......Page 473 F......Page 474 I......Page 475 M......Page 476 O......Page 477 P......Page 478 R......Page 479 S......Page 480 T......Page 482 Z......Page 483 References......Page 484 D......Page 498 H......Page 499 M......Page 500 T......Page 501 Z......Page 502 A......Page 504 C......Page 505 D......Page 506 H......Page 507 L......Page 508 P......Page 509 R......Page 510 S......Page 511 W......Page 512 Z......Page 513

Detection Theory is an introduction to one of the most important tools for analysis of data where choices must be made and performance is not perfect. Originally developed for evaluation of electronic detection, detection theory was adopted by psychologists as a way to understand sensory decision making, then embraced by students of human memory. It has since been utilized in areas as diverse as animal behavior and X-ray diagnosis.

This book covers the basic principles of detection theory, with separate initial chapters on measuring detection and evaluating decision criteria. Some other features include:
•complete tools for application, including flowcharts, tables, pointers, and software;
•student-friendly language;
•complete coverage of content area, including both one-dimensional and multidimensional models;
•separate, systematic coverage of sensitivity and response bias measurement;
•integrated treatment of threshold and nonparametric approaches;
•an organized, tutorial level introduction to multidimensional detection theory;
•popular discrimination paradigms presented as applications of multidimensional detection theory; and
•a new chapter on ideal observers and an updated chapter on adaptive threshold measurement.

This up-to-date summary of signal detection theory is both a self-contained reference work for users and a readable text for graduate students and other researchers learning the material either in courses or on their own.

"Detection Theory, Second Edition is an introduction to one of the most important tools for analysis of data where choices must be made and performance is not perfect. Originally developed for evaluation of electronic detection, detection theory was adopted by psychologists as a way to understand sensory decision making, then embraced by students of human memory. It has since been utilized in areas as diverse as animal behavior and X-ray diagnosis."--Jacket 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 the