Detection Theory: A User's Guide

✍ Scribed by Michael J. Hautus, Neil A. Macmillan, C. Douglas Creelman

• Publisher: Routledge
• Year: 2021
• Tongue: English
• Leaves: 453
• Edition: 3
• Category: Library

✦ Synopsis

Detection Theory: A User’s Guide is an introduction to one of the most important tools for the analysis of data where choices must be made and performance is not perfect. In these cases, detection theory can transform judgments about subjective experiences, such as perceptions and memories, into quantitative data ready for analysis and modeling.

For beginners, the first three chapters introduce measuring detection and discrimination, evaluating decision criteria, and the utility of receiver operating characteristics. Later chapters cover more advanced research paradigms, including: complete tools for application, including flowcharts, tables, and software; student-friendly language; complete coverage of content area, including both one-dimensional and multidimensional models; integrated treatment of threshold and nonparametric approaches; an organized, tutorial level introduction to multidimensional detection theory; and popular discrimination paradigms presented as applications of multidimensional detection theory.

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

✦ Table of Contents

Cover
Endorsement Page
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Introduction
Part I Basic Detection Theory and One-Interval Designs
Chapter 1 The Yes-No Experiment: Sensitivity
Understanding Yes-No Data
Implied Receiver Operating Characteristics
The Signal-Detection Model
Calculational Methods
Essay: The Provenance of Detection Theory
Summary
Computational Appendix
Calculation of the Function for the ROC Curve on Probability Coordinates
Supplementary Material
Problems
References
Chapter 2 The Yes-No Experiment: Response Bias
Two Examples
Measuring Response Bias
Alternative Measures of Bias
Isobias Curves
Experimental Manipulation of Bias
Comparing the Bias Measures
How Does the Participant Choose a Decision Rule?
Calculating Hit and False-Alarm Rates from Parameters
Variability of Decision Criteria
Essay: On Human Decision-Making
Summary
Computational Appendix
Supplementary Material
Problems
References
Chapter 3 Beyond Binary Responses: The Rating Experiment and Empirical Receiver Operating Characteristics
Design of Rating Experiments
Receiver Operating Characteristic Analysis
Relationship between Binary and Rating Responses
ROC Analysis with Slopes Other Than 1
Estimating Bias
Systematic Parameter Estimation and Methods of Calculation
Alternative Ways to Generate ROCs
Another Kind of ROC: Type 2
Essay: Are ROCs Necessary?
Summary
Computational Appendix
Supplementary Material
Problems
References
Chapter 4 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
References
Chapter 5 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
References
Part II Multidimensional Detection Theory and Multi-Interval Discrimination Designs
Chapter 6 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
References
Chapter 7 Comparison (Two-Distribution) Designs for Discrimination
Two-Alternative Forced-Choice
Yes-No Reminder Paradigm
Two-Alternative Forced-Choice Reminder
Essay: Psychophysical Comparisons and Comparison Designs
Summary
Computational Appendix
Problems
References
Chapter 8 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
Summary
Problems
References
Chapter 9 Classification Designs for Discrimination
Same-Different
ABX (Matching-to-Sample)
Oddity (Triangle Task)
Summary
Computational Appendix
Problems
References
Chapter 10 Identification of Multidimensional Objects and Multiple Observation Intervals
Object Identification
Interval Identification: m-Alternative Forced-Choice
Comparisons among Discrimination Paradigms
Simultaneous Detection and Identification
Using Identification to Test for Perceptual Interaction
Essay: How to Choose an Experimental Design
Summary
Problems
References
Part III Stimulus Factors
Chapter 11 Adaptive Methods for Estimating Empirical Thresholds
Two Examples
The Tracking Algorithm: Choices for the Adaptive Tester
Psychometric Functions
Evaluation of Tracking Algorithms
Two More Choices: Discrimination Paradigm and the Issue of Slope
Discrimination Paradigm
Summary
Problems
References
Chapter 12 Components of Sensitivity
Stimulus Determinants of d′ in One Dimension
Basic Processes in Multiple Dimensions
Hierarchical Models
Essay: Psychophysics versus Psychoacoustics (etc.)
Summary
Problems
References
Part IV Statistics
Chapter 13 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
References
Appendix 1: Elements of Probability and Statistics
Probability
Statistics
Reference
Appendix 2: Logarithms and Exponentials
Appendix 3: Flowcharts to Sensitivity and Bias Calculations
Chart 1: Guide to Subsequent Charts
Chart 2: Yes-No Sensitivity
Chart 3: Yes-No Response Bias
Chart 4: Rating-Design Sensitivity
Chart 5: Definitions of Multi-Interval Designs
Chart 6: Multi-Interval Sensitivity
Chart 7: Multi-Interval Bias
Chart 8: Classification
References
Appendix 4: Some Useful Equations
Yes-No (Equal-Variance Signal Detection Theory)
Yes-No (Choice Theory)
Yes-No (Unequal-Variance Signal Detection Theory)
Threshold and “Nonparametric”
One-Dimensional Classification
Forced-Choice (Two-Alternative Forced-Choice)
Forced-Choice (m-Alternative Forced-Choice)
Reminder Paradigm
Same-Different
ABX
Statistics
Appendix 5: Tables
Table A5.1: Instructions for Finding d′, c, and β for the Yes-No Design
Table A5.3: Instructions for Finding d′ for Same-Different (Independent-Observation Model) and ABX
Appendix 6: Software for Detection Theory
SDT Assistant
Websites
References
Appendix 7: Solutions to Selected Problems
Glossary
Index

✦ Subjects

data analysis; detection theory;