Description
Taylor & Francis Ltd Detection Theory 2004 Edition by Neil A. MacMillan, C. Douglas Creelman
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: Preface. Introduction. Part I: Basic Detection Theory and One-Interval Designs. The Yes-No Experiment: Sensitivity. The Yes-No Experiment: Response Bias. The Rating Experiment and Empirical ROCs. Alternative Approaches: Threshold Models and Choice Theory. Classification Experiments for One-Dimensional Stimulus Sets. Part II: Multidimensional Detection Theory and Multi-Interval Discrimination Designs. Detection and Discrimination of Compound Stimuli: Tools for Multidimensional Detection Theory. Comparison (Two-Distribution) Designs for Discrimination. Classification Designs: Attention and Interaction. Classification Designs for Discrimination. Identification of Multidimensional Objects and Multiple Observation Intervals. Part III: Stimulus Factors. Adaptive Methods for Estimating Empirical Thresholds. Components of Sensitivity. Part IV: Statistics. Statistics and Detection Theory. Appendices: Elements of Probability and Statistics. Logarithms and Exponentials. Flowcharts to Sensitivity and Bias Calculations. Some Useful Equations. Tables. Software for Detection Theory. Solutions to Selected Problems.