Abstract
Fuzzy Signal Detection Theory (FSDT) has the potential of enhancing performance measures in detection tasks in which precision of Signal Detection Theory (SDT) analysis is limited by discrete mutually exclusive categorization of the state of the world and/or responses available to the observer. While there have been empirical efforts to demonstrate the benefits and tenability of FSDT, the question still remains whether traditional SDT computational procedures for measures of sensitivity and bias can be used with FSDT procedures. Through the use of Monte Carlo simulation and ROC analysis, the current study examined whether data analyzed by FSDT met the assumptions of traditional SDT on which sensitivity and bias measures are predicated. The results indicated that FSDT does in fact meet the normality and equal variance assumptions of SDT. However, the results also indicated that further theoretical elaboration of ‘fuzzy criterion setting’ is necessary.
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