1. Comparison of Fuzzy and Signal Detection Theory L.L. Murphy, J.L. Szalma, and P.A. Hancock Department of Psychology Institute of Simulation and Training University of Central Florida

2. Signal Detection Theory (SDT) SDT has provided an useful analytical tool SDT has provided an useful analytical tool the state of the world is divided into mutually exclusive categories the state of the world is divided into mutually exclusive categories which are not always evident in an uncertain world which are not always evident in an uncertain world

3. Fuzzy Signal Detection Theory (FSDT) FSDT combines SDT and Fuzzy Set Theory FSDT combines SDT and Fuzzy Set Theory category membership is not mutually exclusive category membership is not mutually exclusive

4. Objectives This is the first ever direct contrast between FSDT and SDT This is the first ever direct contrast between FSDT and SDT The purpose was to investigate the degree to which the FSDT model was sensitive to the classical SDT manipulations. The purpose was to investigate the degree to which the FSDT model was sensitive to the classical SDT manipulations. The same set of data from a discrimination task was analyzed using both FSDT and SDT methods of analysis The same set of data from a discrimination task was analyzed using both FSDT and SDT methods of analysis

10. FSDT and SDT Formulas d’ perceptual sensitivity and c response bias (same for both methods) d’ perceptual sensitivity and c response bias (same for both methods) Hit and false alarm rate formulae Hit and false alarm rate formulae FSDT FSDT Parasuraman et al. (2000) Parasuraman et al. (2000) SDT SDT McMillan and Creelman (1991) McMillan and Creelman (1991)

17. Discussion The surprise is in the very different results The surprise is in the very different results Evidence suggests that the methods provide different insights into system performance Evidence suggests that the methods provide different insights into system performance Further work is needed to investigate d’ and the FSDT model Further work is needed to investigate d’ and the FSDT model

18. References Hancock, P.A., Masalonis, A.J., & Parasuraman, R. (2000). On the theory of fuzzy signal detection: Theoretical and practical considerations. Theoretical Issues in Ergonomic Science, 1, 207-230. Hancock, P.A., Masalonis, A.J., & Parasuraman, R. (2000). On the theory of fuzzy signal detection: Theoretical and practical considerations. Theoretical Issues in Ergonomic Science, 1, 207-230. Macmillan, N.A., & Creelman, C.D. (1991). Detection theory: A user’s guide. New York: Cambridge University Press. Macmillan, N.A., & Creelman, C.D. (1991). Detection theory: A user’s guide. New York: Cambridge University Press. Parasuraman, R., Masalonis, A.J., & Hancock, P.A. (2000). Fuzzy signal detection theory: Basic postulates and formulas for analyzing human and machine performance. Human Factors, 42, 636-659. Parasuraman, R., Masalonis, A.J., & Hancock, P.A. (2000). Fuzzy signal detection theory: Basic postulates and formulas for analyzing human and machine performance. Human Factors, 42, 636-659.

19. Acknowledgements This work was facilitated by the Department of Defense Multidisciplinary University Research Initiative (MURI) program administered by the Army Research Office under Grant DAAD19-01-1- 0621, Dr. P.A. Hancock, Principal Investigator. The assistance of the Department of Psychology at University of Central Florida is also gratefully acknowledged. The views expressed in this work are those of the authors and do not necessarily reflect official Army policy. This work was facilitated by the Department of Defense Multidisciplinary University Research Initiative (MURI) program administered by the Army Research Office under Grant DAAD19-01-1- 0621, Dr. P.A. Hancock, Principal Investigator. The assistance of the Department of Psychology at University of Central Florida is also gratefully acknowledged. The views expressed in this work are those of the authors and do not necessarily reflect official Army policy.