A signal detection model applied to the stimulus: Understanding covariances in face recognition experiments in the context of face sampling distributions

We provide a description and interpretation of signal detection theory as a pplied to the analysis of an individual stimulus in a recognition experimen t. Despite the common use of signal detection theory in this context, espec ially in the face recognition literature, the assumptions of the model have rarely been made explicit. In a series of simulations, we first varied the stability of d' and Cin face sampling distributions and report the pattern of correlations between the hit and false alarm rate components of the mod el across the simulated experiments. These kinds of correlation measures ha ve been reported in recent face recognition papers and have been considered to be theoretically important. The simulation data we report revealed wide ly different correlation expectations as a function of the parameters of th e face sampling distribution, making claims of theoretical importance for a ny particular correlation questionable. Next, we report simulations aimed a t exploring the effects of face sampling distribution parameters on correla tions between individual components of the signal detection model (i.e. hit and false alarm rates), and other facial measures such as typicality ratin gs. These data indicated that valid interpretations of such correlations ne ed to make reference to the parameters of the relevant face sampling distri bution.