Detection Theory

ISBN-10: 0805842314
ISBN-13: 9780805842319
Edition: 2nd 2004 (Revised)
List price: $83.95 Buy it from $52.27
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Description: Detection Theoryis 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  More...

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Book details

List price: $83.95
Edition: 2nd
Copyright year: 2004
Publisher: Taylor & Francis Group
Publication date: 8/23/2004
Binding: Paperback
Pages: 512
Size: 5.75" wide x 8.75" long x 1.25" tall
Weight: 1.540

Detection Theoryis 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.

Preface
Introduction
Basic Detection Theory and One-Interval Designs
The Yes-No Experiment: Sensitivity
Understanding Yes-No Data
Implied ROCs
The Signal Detection Model
Calculational Methods
Essay: The Provenance of Detection Theory
Summary
Problems
The Yes-No Experiment: Response Bias
Two Examples
Measuring Response Bias
Alternative Measures of Bias
Isobias Curves
Comparing the Bias Measures
How Does the Participant Choose a Decision Rule?
Coda: Calculating Hit and False-Alarm Rates From Parameters
Essay: On Human Decision Making
Summary
Computational Appendix
Problems
The Rating Experiment and Empirical ROCs
Design of Rating Experiments
ROC Analysis
ROC Analysis With Slopes Other Than 1
Estimating Bias
Systematic Parameter Estimation and Calculational Methods
Alternative Ways to Generate ROCs
Another Kind of ROC: Type 2
Essay: Are ROCs Necessary?
Summary
Computational Appendix
Problems
Alternative Approaches: Threshold Models and Choice Theory
Single High-Threshold Theory
Low-Threshold Theory
Double High-Threshold Theory
Choice Theory
Measures Based on Areas in ROC Space: Unintentional Applications of Choice Theory
Nonparametric Analysis of Rating Data
Essay: The Appeal of Discrete Models
Summary
Computational Appendix
Problems
Classification Experiments for One-Dimensional Stimulus Sets
Design of Classification Experiments
Perceptual One-Dimensionality
Two-Response Classification
Experiments With More Than Two Responses
Nonparametric Measures
Comparing Classification and Discrimination
Summary
Problems
Multidimensional Detection Theory and Multi-Interval Discrimination Designs
Detection and Discrimination of Compound Stimuli: Tools for Multidimensional Detection Theory
Distributions in One- and Two-Dimensional Spaces
Some Characteristics of Two-Dimensional Spaces
Compound Detection
Inferring the Representation From Data
Summary
Problems
Comparison (Two-Distribution) Designs for Discrimination
Two-Alternative Forced Choice (2AFC)
Reminder Paradigm
Essay: Psychophysical Comparisons and Comparison Designs
Summary
Problems
Classification Designs: Attention and Interaction
One-Dimensional Representations and Uncertainty
Two-Dimensional Representations
Two-Dimensional Models for Extrinsic Uncertain Detection
Uncertain Simple and Compound Detection
Selective and Divided Attention Tasks
Attention Operating Characteristics (AOCs)
Summary
Problems
Classification Designs for Discrimination
Same-Different
ABX (Matching-to-Sample)
Oddity (Triangular Method)
Summary
Computational Appendix
Problems
Identification of Multidimensional Objects and Multiple Observation Intervals
Object Identification
Interval Identification: m-Alternative Forced Choice (mAFC)
Comparisons Among Discrimination Paradigms
Simultaneous Detection and Identification
Using Identification to Test for Perceptual Interaction
Essay: How to Choose an Experimental Design
Summary
Problems
Stimulus Factors
Adaptive Methods for Estimating Empirical Thresholds
Two Examples
Psychometric Functions
The Tracking Algorithm: Choices for the Adaptive Tester
Evaluation of Tracking Algorithms
Two More Choices: Discrimination Paradigm and the Issue of Slope
Summary
Problems
Components of Sensitivity
Stimulus Determinants of d' in One Dimension
Basic Processes in Multiple Dimensions
Hierarchical Models
Essay: Psychophysics versus Psychoacoustics (etc.)
Summary
Problems
Statistics
Statistics and Detection Theory
Hit and False-Alarm Rates
Sensitivity and Bias Measures
Sensitivity Estimates Based on Averaged Data
Systematic Statistical Frameworks for Detection Theory
Summary
Computational Appendix
Problems
Appendices
Elements of Probability and Statistics
Probability
Statistics
Logarithms and Exponentials
Flowcharts to Sensitivity and Bias Calculations
Guide to Subsequent Charts
Yes-No Sensitivity
Yes-No Response Bias
Rating-Design Sensitivity
Definitions of Multi-Interval Designs
Multi-Interval Sensitivity
Multi-Interval Bias
Classification
Some Useful Equations
Tables
Normal Distribution (p to z), for Finding d', c, and Other SDT Statistics
Normal Distribution (z to p)
Values of d' for Same-Different (Independent-Observation Model) and ABX (Independent-Observation and Differencing Models)
Values of d' for Same-Different (Differencing Model)
Values of d' for Oddity, Gaussian Model
Values of p(c) given d' for Oddity (Differencing and Independent-Observation Model, Normal)
Values of d' for m-Interval Forced Choice or Identification
Software for Detection Theory
Listing
Web Sites
Solutions to Selected Problems
Glossary
References
Author Index
Subject Index

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