Asking the right questions: An indirect strategy for improving lie detection
Despite our ability, innate and learned, to perform many tasks, we are, on average, only 54% accurate at detecting lies; this rate of performance is only marginally better than a lucky guess (Bond & DePaulo, 2006). However, research by ten Brinke et al. (2016) suggests that people may be able to detect deception indirectly. Our team, used suggestions of future research from ten Brinke et al. (2016) to study indirect detection of deception by participants watching the video interview of a person suspected of lying. The first hypothesis for this research was that better deception detection would result from questions that primed implicit associations of dishonesty from an observer than from direct questioning of the observer about the dishonesty of a person in a video. A second hypothesis was that the type of question asked of an observer would produce more correct identifications of dishonesty when the questions (a) evoked biases about the person in the video (e.g., stereotypes that individuals in some professions are more or less honest than others), (b) expectations of behavior of the person in the video (e.g., would the person in the video cheat), and (c) probed about nonverbal characteristics indicating the dishonesty of the person in the video (e.g., fidgeting and looking around instead of at the interviewer). A control group of observers was directly questioned about whether the person in the video was lying. Data collection is in progress and our expectation is that indirect questioning will lead to more accurate deception detection than will direct questioning. We also expect that of the three types of indirect questions, those that evoke biases and expected behaviors will produce better deception detection than will questions about the personal characteristics of the person in the video.
Susan T Davis, Mark A Matthews
Primary Advisor's Department
Stander Symposium poster
"Asking the right questions: An indirect strategy for improving lie detection" (2018). Stander Symposium Posters. 1433.