The Theory of Signal Detection
The discovery that expectancy and payoff have such a dramatic influence upon detection behavior was incorporated into a new theoretical conception of the detection situation. Tanner and Swets ( 1954) proposed that statistical decision theory and certain ideas about electronic signal-detecting devices might be used to build a model closely approximating how people actually behave in detection situations. The model is called the theory of signal detection (TSD) and is described in detail by Green and Swets ( 1966). Fundamental to TSD is the concept of noise.
Signals (stimuli) are always detected-whether by electronic devices or by humans--against a background of activity. The level of this background activity, called noise, is assumed to vary randomly and may be either external to the detecting device or caused by the device itself (e.g., physiological noise caused by spontaneous activity of the nervous system). In the detection situation, the observer must therefore first make an observation (x) and then make a decision about the observation. On each trial, the observer must decide whether x is due to a signal added to the noise background or to the noise alone. When a weak signal is applied, the decision becomes difficult, and errors are frequent. One factor contributing to the difficulty of the problem is the random variation of background noise. On some trials, the noise level may be so high as to be mistaken for a signal, and on other trials it may be so low that the addition of a weak signal is mistaken for noise. This state of affairs can