Abstract
A recent advance on Signal Detection Theory (SDT) promises to enhance measurement of performance in complex real world domains. This development, Fuzzy Signal Detection Theory (FSDT), combines traditional SDT with Fuzzy Set Theory to extend signal detection analysis beyond the traditional crisp, categorical model. FSDT permits events to simultaneously be in more than one state category (e.g. signal and non-signal), so that the stimulus and response dimensions can be continuous rather than categorical. Consequently, FSDT can be employed in settings where the degree to which an event is a signal for detection may vary. This study is an initial test of application of FSDT to vigilance, a domain in which SDT has been widely applied. Results indicate that manipulations of stimulus probability impacts response bias in a fuzzy vigilance task, but that these effects differ somewhat from tasks employing traditional signal detection.
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