The Generalized Signal Detection Theory

In English Signal detection theory (SDT) and the Dual Process SDT
(Yonelinas, 1994) are currently the most influential accounts
of item variability in recognition memory. However,
neither provides a sufficient account of differences in the
familiarity distributions. Instead, this phenomenon is
accounted for by the idea of encoding variability (Wixted,
2007) or an additional retrieval process (Yonelinas, 2001).
We present the Generalized Signal Detection Theory (the
GSDT), in which the familiarity distribution are a sum of
signals described by a sigmoidal non-linear activation function.
The GSDT accounts for a higher variability in the old
item distribution by emphasizing the non-linarites, but also
for equal variability in the new and old item distributions by
attenuating the non-linearites. The GSDT also extends the
interpretation of the new to old item variability, indexed by
the slope of the z-ROC.