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Both Traditional and Fuzzy Signal Detection Theory (SDT) methods of analyses were compared using a duration discrimination task of brief light flashes. Both difficulty and response bias (by instruction) were manipulated. The two methods clearly provided different estimates of SDT parameters. The Fuzzy SDT analyses inflated performance while the traditional SDT analyses underestimated performance. In some cases performance on the ‘easier’ condition was worse than on the more ‘difficult’ condition. This may be due to differences in the range of durations discriminated, which was narrower in the ‘easier’ condition. Range of stimuli used can impact performance, although it or maybe an artifact of the procedure for the crisp analysis. Fuzzy SDT met the assumptions of traditional SDT, the assumption of normally distributed noise and signal plus noise distribution holds. In addition, the equal variance assumption holds for the more difficult discrimination for all participants.

A recent advance upon Signal Detection Theory (SDT) promises to enhance measurement of performance in complex real world domains where stimuli do not fall into discrete, mutually exclusive categories. This development, Fuzzy Signal Detection Theory (FSDT) combines traditional SDT with Fuzzy Set Theory (FST) to extend signal detection analysis beyond the traditional crisp, categorical decision making model. FSDT allows for events to simultaneously be in more than one state category (e.g. signal and non-signal), so that stimulus and response dimensions can be continuous rather than categorical. This study compared the differences in methods of analyses from FSDT and traditional SDT using the same data set. Data suggests that FSDT analysis and traditional SDT provide different vistas into signal detection performance. FSDT provided a better description of the effects of stimulus uncertainty on observers' response bias and sensitivity. This is because the FSDT model explicitly captures this uncertainty and can provide insight into system performance in domains in which stimulus categories vary along a continuum.

We evaluated the use of color in a human-computer interface to investigate how affective cues influence usability judgments. Yellow and green, were found to enhance performance on a cognitive flexibility task as compared to white and gray. Such an outcome suggests that color promotes positive affect. The aesthetically designed interface that promoted positive affect was also found to enhance perceptions of interface usability. This relationship, however, was only found when underlying usability was effective. When usability was poor, aesthetics made users more cognizant of the usability weaknesses. The results suggest that designers consider incorporating color into designs to help elicit positive emotion in users but that they must be aware that incorporation of aesthetics comes with the potential cost of disposing users to be more discerning of usability.