Comparing Traditional and Fuzzy Signal Detection Analysis for Classification Data

Abstract

This paper provides a comparative analysis of the results of applying traditional signal detection theory (tSDT) and fuzzy signal detection theory (fSDT) methods to classification data. Fuzzy signal detection theory generalizes tSDT by allowing for non-binary assignment of events and responses in order to capture the uncertainty inherent in detection judgments. Because traditional signal detection has been used extensively in classification research, the use of fSDT may ultimately provide new insights in this domain. Results were analyzed from a classification learning experiment in which category discriminability, base-rates and payoffs varied across conditions. Several quantitative and qualitative differences were observed across theoretical perspectives. The difference between derived hit and false alarm rates was smaller in the fSDT analysis, even though the data were the same. Differences between accounts of the base-rate and payoff conditions, as well as between category discriminability levels, were actually reversed in some cases, depending on whether tSDT or fSDT were used. The implications of these differences for interpreting classification data are considered.