Comparison of Fuzzy Signal Detection and Traditional Signal Detection Theory: Approaches to Performance Measurement

Abstract

A recent advance upon Signal Detection Theory (SDT) promises to enhance measurement of performance in complex real world domains where stimuli do not fall into discrete, mutually exclusive categories. This development, Fuzzy Signal Detection Theory (FSDT) combines traditional SDT with Fuzzy Set Theory (FST) to extend signal detection analysis beyond the traditional crisp, categorical decision making model. FSDT allows for events to simultaneously be in more than one state category (e.g. signal and non-signal), so that stimulus and response dimensions can be continuous rather than categorical. This study compared the differences in methods of analyses from FSDT and traditional SDT using the same data set. Data suggests that FSDT analysis and traditional SDT provide different vistas into signal detection performance. FSDT provided a better description of the effects of stimulus uncertainty on observers' response bias and sensitivity. This is because the FSDT model explicitly captures this uncertainty and can provide insight into system performance in domains in which stimulus categories vary along a continuum.