APPLICATION OF FUZZY SIGNAL DETECTION THEORY TO THE DISCRIMINATION OF MORPHED TANK IMAGES

The effect of response set size on performance on a detection task was evaluated using both fuzzy and traditional signal detection theory. Fuzzy categories of stimuli were created using morphing software to blend profile images of American (M1A1) and Iraqi (T55) tanks to different degrees. These combinations were used to create static images varying from 100 % T55 to 0 % T55 (100% MIA1). Participants were asked to indicate the degree to which each image did not resemble an American tank. Consistent with previous research, results indicated that the FSDT model conforms to the normality assumption of traditional SDT. In addition, forcing observers to make binary decisions impaired performance relative to multi-category response sets in the FSDT analysis but not the traditional analysis. However, there were more model convergence failures in the FSDT analysis relative to the traditional analysis, mostly associated with conditions in which there were 100 response categories