Bayesian Estimation of Signal Detection Models, Part 1

We begin by calculating the maximum likelihood estimates of the EVSDT parameters, separately for each participant in the data set. Before doing so, I note that this data processing is only required for manual calculation of the point estimates; the modeling methods described below take the raw data and therefore don't require this annoying step. First, we'll compute for each trial whether the participant's response was a hit, false alarm, correct rejection, or a miss. We'll do this by creating a new variable, type: Then we can simply count the numbers of these four types of trials for each participant, and put the counts on one row per participant. For a single subject, d' can be calculated as the difference of the standardized hit and false alarm rates (Stanislaw and Todorov 1999): Its inverse, \(\Phi {-1}\), converts a proportion (such as a hit rate or false alarm rate) into a z score.