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 signal detection theory is to estimate two main parameters
from the experimental data. The ¯rst parameter, called d0, indicates the
strength of the signal (relative to the noise). The second parameter called
C (a variant of it is called ¯), re°ects the strategy of response of the par-
ticipant of being more willing to say (e.g., yes rather than no). Sdt is used
in very di®erent domains from psychology (psychophysics, perception, mem-
ory), medical diagnostics (do the symptoms match a known diagnostic or can
they be dismissed are irrelevant), to statistical decision (do the data indicate
that the experiment has an eดดect or not).