Abstract:

Signal detection theory (SDT) has been widely applied to explain the decision-making process in different types of cognitive tasks. However, one important limitation of classical SDT is that it is difficult to illustrate the mental mechanisms underlying the setting of response criterion. The current article discusses the decision rule in signal detection tasks from the perspective of Bayesian decision theory (BDT). I first introduce the basic idea of BDT based on the Bayes’ theorem. Next, I discuss how BDT explains the decision rule of ideal observer, and characterizes the deviation between actual participants and ideal observer in empirical signal detection tasks. I then discuss the difference between classical SDT and BDT in unequal variance signal detection model. Finally, I briefly introduce the empirical research evidence supporting BDT.

Key words: signal detection theory, Bayesian decision theory, prior probability, likelihood function, response criterion