Signal Detection Theory

Signal detection theory (SDT) is used to analyze data coming from experiments in which the task is to categorize ambiguous stimuli that can either be generated by a known process (in which case the stimuli are called the signal) or be obtained by chance (in which case they are called the noise in the SDT framework). For example, radar operators must decide whether what they see on a radar screen indicates the presence of a plane (the signal) or the presence of parasites (the noise). This type of application was the original framework of SDT (see the founding work by Green & Swets). But the notion of signal and noise can be somewhat metaphorical is some experimental contexts. For example, in a memory recognition experiment, participants have to decide whether the stimulus they currently see was presented before. Here the signal corresponds to a familiarity feeling generated by a memorized stimulus whereas the noise corresponds to a familiarity feeling generated by a new stimulus.

The goal of detection theory is to estimate two main parameters from the experimental data. The first parameter, called d′, indicates the strength of the signal (relative to the noise). The second parameter, called C (a variant of which is called β), reflects the strategy of response of the participant (e.g., saying easily Yes rather than No). SDT is used in disparate domains, from psychology (psychophysics, perception, memory) to medical diagnostics (do the symptoms match a known diagnostic or can they be dismissed as irrelevant?) to statistical decision (do the data indicate that the experiment has an effect or not?).