frequency with which this occurred is unknown, because the data were pooled without regard to their source. Future applications of the detection approach to the role of heuristics in judgments of occupations or related problems should be constructed so that subjects make mutually exclusive choices on each item.
There is an element of arbitrariness in the foregoing analysis, in that it would have been equally possible to treat the descriptions as stimuli to be discriminated, and the gender of the person described as the source of bias. (This arbitrariness is evident in common usage, where sex discriminatiol and sex bias have the same meaning.) I chose to treat gender as the discrimination variable because there is information on the relative frequencies of males and females in various occupations, but the data treatment and the general conclusions would have been much the same if gender hac been treated as a source of response bias. There is a lesson here: The discrimination and bias terms are interchangeable in a detection analysis that adopts a behavioral approach, and thus treats signals, stimulus contexts, payoffs, instructions, and indeed all aspects of the detection situation as sources of stimulus or response bias. Nevin ( 1981) suggested the interchangeability of stimulus and reinforcement terms in the analysis of detection performance, and Alsop and Davison (this volume) propose models of choice that give parallel treatment to stimulus and response terms in detection situations. These developments may lead us to a truly integrative and broadly applicable approach to the joint control of behavior by stimuli and consequences.
This chapter has demonstrated the applicability of signal-detection analyses to problems where the stimuli are not readily defined in physical terms (e.g., the occupation stereotype descriptions) and where responses cannot in principle be defined as correct or incorrect (as in both the illusion and gender-occupation problems). These applications were suggested by a behavioral model of signal detection in which the signals, as well as contextual factors and outcomes, are treated as determiners of stimulus or response bias.
The research described here was designed to be illustrative rather that definitive, to encourage application of the signal-detection approach to more problems outside its normal scope. In the language of this chapter, my purpose is to bias researchers toward the signal-detection approach when they choose a research strategy. If socially significant problems such as the discrimination of sane vs. insane patients are approached in this way, erroneous interpretations may be avoided and effective solutions can be promoted.
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Publication information: Book title: Signal Detection:Mechanisms, Models, and Applications. Contributors: Michael L. Commons - Editor, John A. Nevin - Editor, Michael C. Davison - Editor, Sheila M. McDonald - Editor. Publisher: Lawrence Erlbaum Associates. Place of publication: Hillsdale, NJ. Publication year: 1991. Page number: 272.
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