Detection theory entered psychology as a way to explain detection experiments, in which weak visual or auditory signals must be distinguished from a “noisy” background. In Signal Detection Theory and Psychophysics (1966), David Green and John Swets portrayed observers as decision makers trying to optimize performance in the face of unpredictable variability, and they prescribed experimental methods and data analyses for separating decision factors from sensory ones.
Since Green and Swets' classic was published, both the content of detection theory and the way it is used have changed. The theory has deepened to include alternative theoretical assumptions and has been used to analyze many experimental tasks. The range of substantive problems to which the theory has been applied has broadened greatly. The contemporary user of detection theory may be a sensory psychologist, but more typically is interested in memory, cognition, or systems for medical or nonmedical diagnosis. In this book, we draw heavily on the work of Green, Swets, and other pioneers, but aim for a seamless meshing of historical beginnings and current perspective. In recognition that these methods are often used in situations far from the original problem of finding a “signal” in background noise, we have omitted the word signal from the title and usually refer to these methods simply as detection theory.
We are writing with two types of readers in mind: those learning detection theory, and those applying it. For those encountering detection theory for the first time, this book is a textbook. It could be the basic text in a one-semester graduate or upper level undergraduate course, or it could be a supplementary text in a broader course on psychophysics, methodology, or a substantive topic. We imagine a student who has survived one semester of “behavioral” statistics at the undergraduate level, and have tried to make the book accessible to such a person in several ways. First, we provide appen-