Signal detection theory (SDT) assumes a division of objective truths or "states of the world" into the nonoverlapping categories of signal and noise. The definition of a signal in many real settings, however, varies with context and over time. In the terminology of fuzzy logic, a real-world signal has a value that falls in a range between unequivocal presence and unequivocal absence. The definition of a response can also be nonbinary. Accordingly the methods of fuzzy logic can be combined with SDT, yielding fuzzy SDT. We describe the basic postulates of fuzzy SDT and provide formulas for fuzzy analysis of detection performance, based on four steps: (a) selection of mapping functions for signal and response; (b) use of mixed-implication functions to assign degrees of membership in hits, false alarms, misses, and correct rejections; (c) computation of fuzzy hit, false alarm, miss, and correct rejection rates; and (d) computation of fuzzy sensitivity and bias measures. Fuzzy SDT can considerably extend the range and utility of SDT by handling the contextual and temporal variability of most real-world signals. Actual or potential applications of fuzzy SDT include evaluation of the performance of human, machine, and human-machine detectors in real systems.

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Traffic flow management (TFM) in the U.S. is the process by which the Federal Aviation Administration (FAA), with the participation of airspace users, seeks to balance the capacity of airspace and airport resources with the demand for these resources. This is a difficult process, complicated by the presence of severe weather or unusually high demand. TFM in en-route airspace is concerned with managing airspace demand, specifically the number of flights handled by air traffic control (ATC) sectors; a sector is the volume of airspace managed by an air traffic controller or controller team. Therefore, effective decision-making requires accurate sector demand predictions. While it is commonly accepted that the sector demand predictions used by current and proposed TFM decision support systems contain significant uncertainty, this uncertainty is typically not quantified or taken into account in any meaningful way. The work described here is focused on measuring the uncertainty in sector demand predictions under current operational conditions, and on applying those measurements towards improving the performance and human factors of TFM decision support systems.

This paper applies fuzzy SDT (signal detection theory) techniques, which combine fuzzy logic and conventional SDT, to empirical data. Two studies involving detection of aircraft conflicts in air traffic control (ATC) were analysed using both conventional and fuzzy SDT. Study 1 used data from a preliminary field evaluation of an automated conflict probe system, the User Request Evaluation Tool (URET). The second study used data from a laboratory controller-in-the-loop simulation of Free Flight conditions. Instead of assigning each potential conflict event as a signal (conflict) or non-signal, each event was defined as a signal (conflict) to some fuzzy degree between 0 and 1 by mapping distance into the range [0, 1]. Each event was also given a fuzzy membership, [0, 1], in the set 'response', based on the perceived probability of a conflict or on the colour-coded alert severity. Fuzzy SDT generally reduced the computed false alarm rate for both the human and machine conflict systems, partly because conflicts just outside the conflict criterion used in conventional SDT, were defined by fuzzy SDT as a signal worthy of some attention. The results illustrate the potential of fuzzy SDT to provide, especially in exploratory data analysis, a more complete picture of performance in aircraft conflict detection and many other applications. Alternative analytic methods also using fuzzy SDT concepts are discussed.

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As complex automated aids proliferate in transportation and manufacturing domains, examining human users' trust in such systems gains importance. We review some of the growing literature on trust in automated systems, and outline a program for future studies and theoretical developments. Trust is an intervening variable between automation reliability and use, among other factors. Consistent reliable machine performance can increase trust, and discrete errors can decrease trust. Trust tends to resist change over time. The association between trust and subsequent usage is positive but not clear-cut, and may be mediated by risk and self-confidence. The place of trust in the overall picture of human-automation system performance must be established. The suggested research program accomplishes this by investigating training issues and individual differences, employing new measures, and examining dynamics of trust and usage in automation possessing different reliability in different situations, automation with multiple modes, and adaptive automation.

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