Signal Detection Theory (SDT)

Signal Detection Theory (often abridged as SDT) is used to analyze data coming from experiments where the task is to categorize ambiguous stimuli which can be generated either by a known process (called the signal) or be obtained by chance (called the noise in the SDT framework). For example a radar operator must decide if what she sees on the radar screen indicates the presence of a plane (the signal) or the presence of parasites (the noise). This type of applications was the original framework of SDT (see the founding work of Green & Swets, 1966) 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 if 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 it is called β), reflects the strategy