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Detection Theory : A User's Guide - Neil A. Macmillan

Detection Theory

A User's Guide

Paperback Published: 23rd August 2004
ISBN: 9780805842319
Number Of Pages: 512
For Ages: 1 - 17 years old

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"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.

"For the last many years I have been suggesting Macmillan and Creelman to those who ask me for a reference to detection theory. It is an excellent book and has proved useful to a wide variety of behavioral scientists who need detection theory as a tool. I am delighted to have this new edition to recommend, an edition which includes material that should make it of use to still more investigators. The new information about multidimensional signal-detection theory allows analysis of more complex experimental designs and, even more importantly from my perspective, analysis of situations where there are multiple detectors, or channels, or pathways."

-Norma Graham
Department of Psychology, Columbia University

"Rarely, I believe, has a book so fine in its first edition been as enhanced in its second. It continues to serve handsomely as a handbook, neatly laying out practically everything an experimenter needs in order to select from and apply a wide range of methods and measures. Its purpose as a textbook has been notably advanced: for example, early chapters on basic detection theory and alternatives are reorganized to make fundamental ideas more accessible and the later material on complex stimuli and methods is integrated by a tutorial treatment of recent developments in multidimensional detection theory. This volume's friendliness to the reader, and its broad coverage and considerable sophistication (do see the "essays"), make it highly suitable for the student and very likely informative even for the experienced investigator."


-John Swets
BBN Technologies--Chief Scientist (emeritus), Harvard Medical School--Lecturer o

Prefacep. xiii
Introductionp. xvii
Basic Detection Theory and One-Interval Designs
The Yes-No Experiment: Sensitivityp. 3
Understanding Yes-No Datap. 3
Implied ROCsp. 9
The Signal Detection Modelp. 16
Calculational Methodsp. 20
Essay: The Provenance of Detection Theoryp. 22
Summaryp. 24
Problemsp. 25
The Yes-No Experiment: Response Biasp. 27
Two Examplesp. 27
Measuring Response Biasp. 28
Alternative Measures of Biasp. 31
Isobias Curvesp. 35
Comparing the Bias Measuresp. 36
How Does the Participant Choose a Decision Rule?p. 42
Coda: Calculating Hit and False-Alarm Rates From Parametersp. 44
Essay: On Human Decision Makingp. 46
Summaryp. 47
Computational Appendixp. 48
Problemsp. 48
The Rating Experiment and Empirical ROCsp. 51
Design of Rating Experimentsp. 51
ROC Analysisp. 53
ROC Analysis With Slopes Other Than 1p. 57
Estimating Biasp. 64
Systematic Parameter Estimation and Calculational Methodsp. 70
Alternative Ways to Generate ROCsp. 71
Another Kind of ROC: Type 2p. 73
Essay: Are ROCs Necessary?p. 74
Summaryp. 77
Computational Appendixp. 77
Problemsp. 78
Alternative Approaches: Threshold Models and Choice Theoryp. 81
Single High-Threshold Theoryp. 82
Low-Threshold Theoryp. 86
Double High-Threshold Theoryp. 88
Choice Theoryp. 94
Measures Based on Areas in ROC Space: Unintentional Applications of Choice Theoryp. 100
Nonparametric Analysis of Rating Datap. 104
Essay: The Appeal of Discrete Modelsp. 104
Summaryp. 107
Computational Appendixp. 108
Problemsp. 109
Classification Experiments for One-Dimensional Stimulus Setsp. 113
Design of Classification Experimentsp. 113
Perceptual One-Dimensionalityp. 114
Two-Response Classificationp. 115
Experiments With More Than Two Responsesp. 126
Nonparametric Measuresp. 130
Comparing Classification and Discriminationp. 132
Summaryp. 135
Problemsp. 136
Multidimensional Detection Theory and Multi-Interval Discrimination Designs
Detection and Discrimination of Compound Stimuli: Tools for Multidimensional Detection Theoryp. 141
Distributions in One- and Two-Dimensional Spacesp. 142
Some Characteristics of Two-Dimensional Spacesp. 149
Compound Detectionp. 152
Inferring the Representation From Datap. 159
Summaryp. 161
Problemsp. 161
Comparison (Two-Distribution) Designs for Discriminationp. 165
Two-Alternative Forced Choice (2AFC)p. 166
Reminder Paradigmp. 180
Essay: Psychophysical Comparisons and Comparison Designsp. 182
Summaryp. 184
Problemsp. 184
Classification Designs: Attention and Interactionp. 187
One-Dimensional Representations and Uncertaintyp. 188
Two-Dimensional Representationsp. 191
Two-Dimensional Models for Extrinsic Uncertain Detectionp. 196
Uncertain Simple and Compound Detectionp. 200
Selective and Divided Attention Tasksp. 202
Attention Operating Characteristics (AOCs)p. 206
Summaryp. 209
Problemsp. 210
Classification Designs for Discriminationp. 213
Same-Differentp. 214
ABX (Matching-to-Sample)p. 229
Oddity (Triangular Method)p. 235
Summaryp. 238
Computational Appendixp. 240
Problemsp. 242
Identification of Multidimensional Objects and Multiple Observation Intervalsp. 245
Object Identificationp. 246
Interval Identification: m-Alternative Forced Choice (mAFC)p. 249
Comparisons Among Discrimination Paradigmsp. 252
Simultaneous Detection and Identificationp. 255
Using Identification to Test for Perceptual Interactionp. 259
Essay: How to Choose an Experimental Designp. 262
Summaryp. 264
Problemsp. 265
Stimulus Factors
Adaptive Methods for Estimating Empirical Thresholdsp. 269
Two Examplesp. 270
Psychometric Functionsp. 272
The Tracking Algorithm: Choices for the Adaptive Testerp. 277
Evaluation of Tracking Algorithmsp. 289
Two More Choices: Discrimination Paradigm and the Issue of Slopep. 292
Summaryp. 294
Problemsp. 295
Components of Sensitivityp. 297
Stimulus Determinants of d' in One Dimensionp. 298
Basic Processes in Multiple Dimensionsp. 304
Hierarchical Modelsp. 310
Essay: Psychophysics versus Psychoacoustics (etc.)p. 312
Summaryp. 314
Problemsp. 314
Statistics
Statistics and Detection Theoryp. 319
Hit and False-Alarm Ratesp. 320
Sensitivity and Bias Measuresp. 323
Sensitivity Estimates Based on Averaged Datap. 331
Systematic Statistical Frameworks for Detection Theoryp. 337
Summaryp. 339
Computational Appendixp. 340
Problemsp. 341
Appendices
Elements of Probability and Statisticsp. 343
Probabilityp. 343
Statisticsp. 351
Logarithms and Exponentialsp. 357
Flowcharts to Sensitivity and Bias Calculationsp. 359
Guide to Subsequent Chartsp. 360
Yes-No Sensitivityp. 361
Yes-No Response Biasp. 362
Rating-Design Sensitivityp. 363
Definitions of Multi-Interval Designsp. 364
Multi-Interval Sensitivityp. 365
Multi-Interval Biasp. 366
Classificationp. 367
Some Useful Equationsp. 369
Tablesp. 374
Normal Distribution (p to z), for Finding d', c, and Other SDT Statisticsp. 375
Normal Distribution (z to p)p. 376
Values of d' for Same-Different (Independent-Observation Model) and ABX (Independent-Observation and Differencing Models)p. 380
Values of d' for Same-Different (Differencing Model)p. 401
Values of d' for Oddity, Gaussian Modelp. 420
Values of p(c) given d' for Oddity (Differencing and Independent-Observation Model, Normal)p. 424
Values of d' for m-Interval Forced Choice or Identificationp. 426
Software for Detection Theoryp. 431
Listingp. 431
Web Sitesp. 433
Solutions to Selected Problemsp. 435
Glossaryp. 447
Referencesp. 463
Author Indexp. 477
Subject Indexp. 483
Table of Contents provided by Rittenhouse. All Rights Reserved.

ISBN: 9780805842319
ISBN-10: 0805842314
Audience: Professional
For Ages: 1 - 17 years old
Format: Paperback
Language: English
Number Of Pages: 512
Published: 23rd August 2004
Country of Publication: US
Dimensions (cm): 22.76 x 16.05  x 2.74
Weight (kg): 0.69
Edition Number: 2
Edition Type: New edition