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Vigilance pp 705-718 | Cite as

Signal Detection Theory Applied to Vigilance

  • John A. Swets
Part of the NATO Conference Series book series (NATOCS, volume 3)

Abstract

Some 30 articles in the 1960s reported studies of vigilance in which the analytical techniques of signal detection theory were used to obtain separate, presumably independent, measures of sensitivity and the decision criterion. According to three reviews at the end of the decade, most studies showed a change in the decision criterion over time, but no change in sensitivity, and were thus inconsistent with the earlier interpretation of vigilance experiments as exhibiting a decrement in sensitivity. A few experiments did show a sensitivity decrement, however, usually in addition to a criterion change, and some of the later articles provided a preliminary description of the different stimulus conditions producing the different effects.

The present paper provides another look at the application of signal detection theory to vigilance, some 6 years and 30 articles after the previous reviews. The main effect observed in the 1960s appears again, but the differences between stimulus conditions that do and do not produce a sensitivity decrement are now less distinct. Several studies show that the decision criterion varies appropriately with changes in signal probability, and a few studies suggest that the criterion varies less reliably with changes in the payoff matrix. An evaluative review is given here of the often conflicting discussions by several authors of the role that signal detection theory can and should play in the study of vigilance, and of the theory’s strengths and weaknesses in that role. This latter part of the paper, especially, draws upon the recent but extensive application of the theory in the context of industrial inspection.

Keywords

Event Rate True Detection Signal Probability False Detection Payoff Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1977

Authors and Affiliations

  • John A. Swets
    • 1
  1. 1.Bolt Beranek and Newman Inc.CambridgeUSA

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