Detection Theory: A User’s Guide, 3rd Edition
- Length: 452 pages
- Edition: 3
- Language: English
- Publisher: Routledge
- Publication Date: 2021-09-28
- ISBN-10: 0815360096
- ISBN-13: 9780815360094
- Sales Rank: #0 (See Top 100 Books)
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.
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 Estimating Bias Systematic Parameter Estimation and Methods of Calculation Alternative Ways to Generate ROCs Another Kind of ROC: Type 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
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