signal detection theory (redirected from Detection theory)

signal detection theory [′sig·nəl di′tek·shən ‚thē·ə·rē]

(psychology)

A theory which characterizes not only the acuity of an individual's discrimination but also the psychological factors that bias his judgment.

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Signal detection theory

A theory in psychology which characterizes not only the acuity of an individual's discrimination but also the psychological factors that bias the individual's judgments. Failure to separate these two aspects of discrimination had tempered the success of theories based upon the classical concept of a sensory threshold. The theory provides a modern and more complete account of the process whereby an individual makes fine discriminations.

The theory of signal detection has two parts of quite different origins. The first comes from mathematical statistics and is a translation of the theory of statistical decisions. The major contribution of this part of the theory is that it permits a determination of the individual's discriminative capacity, or sensitivity, that is independent of the judgmental bias or decision criterion the individual may have had when the discrimination was made. The second part of the theory comes from the study of electronic communications. It provides a means of calculating for simple signals, such as tones and lights, the best discrimination that can be attained. The prediction is based upon physical measurements of the signals and their interfering noise.

This opportunity to compare the sensitivity of human observers with the sensitivity of an “ideal observer” for a variety of signals is of considerable usefulness, and of growing interest, in sensory psychology. Signal detection theory has been applied to several topics in experimental psychology in which separation of intrinsic discriminability from decision factors is desirable. Included are attention, imagery, learning, conceptual judgment, personality, reaction time, manual control, and speech.

The analytical apparatus of the theory has been of value in the evaluation of the performance of systems that make decisions based on uncertain information. Such systems may involve only people, or people and machines together, or only machines, Examples come from medical diagnosis, where clinicians may base diagnostic decisions on a physical examination, or on an x-ray image, or where machines make diagnoses, perhaps by counting blood cells of various types.