A Signal Improvement to Signal Detection Analysis: Fuzzy SDT on the ROCs

    Authors

    J. L. Szalma;P. A. Hancock

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    J. Exp. Psychol.-Hum. Percept. Perform.

    Keywords

    Signal Detection Theory; Fuzzy Signal Detection Theory; ROC analysis; SEQUENTIAL LINEUP ADVANTAGE; MACHINE PERFORMANCE; DECISION; Psychology; Psychology, Experimental

    Abstract

    Fuzzy Signal Detection Theory (FSDT) combines traditional Signal Detection Theory (SDT) with Fuzzy Set Theory to generalize signal detection analysis beyond the traditional categorical decision-making model. This advance upon SDT promises to improve measurement of performance in domains in which stimuli do not fall into discrete, mutually exclusive categories; a situation which characterizes many detection problems in real-world operational contexts. FSDT allows for events to simultaneously be in more than one state category (e. g., both signal and nonsignal). The present study derived FSDT Receiver Operating Characteristic (ROC) functions to test whether application of FSDT meets the Gaussian and equal variance assumptions of traditional SDT and, therefore, whether the standard representation of the SDT decision space can be extended to the broader case of FSDT. Results supported the contention that FSDT does meet these traditional SDT assumptions, and further, that it yields higher sensitivity scores than traditional SDT when the category membership of events is ambiguous. ROC analyses indicate that use of traditional SDT formulas with fuzzy hit and false alarm rates is thus justified. The implications of this advance to both theoretical and practical domains are adumbrated.

    Journal Title

    Journal of Experimental Psychology-Human Perception and Performance

    Volume

    39

    Issue/Number

    6

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    1741

    Last Page

    1762

    WOS Identifier

    WOS:000327885400020

    ISSN

    0096-1523

    Share

    COinS