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Is Le Mot Juste? The Contexualization of Words by Expert Lie Detectors

  • Maureen O’Sullivan
Chapter

Abstract

Some of the chapters in this book discuss the ways in which language samples can be analyzed to determine credibility (e.g., Colwell, Hiscock-Anisman, & Fede, this volume; Griesel, Ternes, Schraml, Cooper, & Yuille, this volume). This chapter illustrates how expert lie detectors use information from a single word in discerning the truthfulness of others. These illustrations were obtained from in-depth interviews with highly accurate lie detectors (O’Sullivan & Ekman, 2004) who received scores of 80 % or more on at least two of three different lie detection tests. The three tests were not easy, since average scores on the measures are close to 50 %. Although the base rate occurrence of such expert lie detectors varies from group to group, the expert lie detectors in this analysis are at least two standard deviations above the mean in their lie detection abilities. For example, using the criterion described, no expert lie detector has been found among college students, although thousands have been examined. Although there are now a sufficient number of experts (n = 50) to aggregate their responses and compare them with their matched controls, another value of the project is the opportunity to compare the description of the lie detection enterprise that results from the efforts of a single expert with the contributions to knowledge made by scores of scientists using a wide variety of methodologies. A brief review of these methodologies is offered in order to situate the kind of information obtained from individual interviews in the broader research endeavor. This review is, of necessity, cursory. Many subtle distinctions are disregarded in the effort to describe briefly each approach.

Keywords

Facial Expression Police Officer Nonverbal Behavior Truth Teller Empathic Accuracy 
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

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Maureen O’Sullivan
    • 1
  1. 1.University of San FranciscoSan FranciscoUSA

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