The Gaussian model that is used to extrapolate the empirical ROC curve and to then compute the area beneath it looks much like the signal detection model of memory depicted in Fig. 2 (e.g., two Gaussian distributions separated by d').
The signal detection model of memory biases described by Gaitan and Wixted (2000) was considered to have some strength in providing a parsimonious explanation for the current within-subject findings.
The superior complexity of the proposed equalizer allows it to equalize signals with hundreds of memory elements at a fraction of the computational cost of conventional optimal equalizer, which has complexity linear in the data block length but exponential in die channel memory length.
It has been shown that mTOR signals, AMPK-α1 signals, and IL-7 signals support the development of memory CD8+ T cells (Rolf et al., 2013; Cui et al., 2015).
If a signal is synthetised of sub-signals having different lengths of memory, changing between a minimum value, N min, and a maximum one, N max, the time-varying signal model coefficients can be estimated by using Eqs.
First, the large bandwidth of the OFDM signal requires the introduction of memory effects in the PD model.
We combined the hits to studied proverbs and false alarms to the unstudied, but related proverbs into signal detection theoretic measures of memory performance (d' and C; Green & Swets, 1966).
But a theory of lineup memory would make a specific prediction about the first latent variable (the distribution of memory signals) without necessarily making any prediction about the second (criterion variability).
There is (1) some probability of observing a particular memory strength of the target, x1, (2) some probability that x1 will be the highest (MAX) memory strength of the lineup members and (3) some probability, f(x), that the decision variable will exceed the decision criterion (where x represents the vector of memory signals associated with the faces in the lineup).
