The state of deception detection research: two perspectives used to uncover deception detection methods

Abstract

People are sometimes deceptive, meaning that they “intentionally, knowingly, or purposefully mislead another person” (Levine, 2014, p. 37), despite potential negative relational consequences (McCornack & Levine, 1990; Millar & Tesser, 1988) or harsh societal condemnation (Kleinmuntz & Szucko, 1984). In fact, people encounter deception on a daily basis (Serota, Levine, & Boster 2010). Sometimes this deception is especially destructive (Van Swol, Braun, & Malhotra, 2012). Therefore, many law enforcement agencies and academic disciplines are invested in the study of deception. Much of this research is conducted in order to uncover ways to detect deceptive messages and distinguish them from truthful ones. This deception detection research consistently yields the following three findings. First, that people are notoriously bad at distinguishing truthful messages from deceptive ones (Bond & DePaulo, 2006). Second, that people often over-estimate their ability to detect deception accurately (Burgoon & Levine, 2010). Lastly, people are more likely to judge a message as truthful rather than deceptive regardless of the message’s veracity (Levine, Kim, Park, & Hughs, 2006). However, despite these three consistent findings, deception detection research is primarily bifurcated into two different perspectives researchers take when examining the phenomenon of deception (Burgoon & Levine, 2010) which this report labels the dominant and new perspectives. These two perspectives greatly affect the results and implications of the deception detection research being conducted. Therefore, this report examines and discusses each perspective as well as their divergent and sometimes intersecting research streams. Following this discussion, some of the most notable implications for each perspective are listed. Then some remarkable applications of each perspective are also discussed. Finally, some future research directions are suggested. Such discussions lead to an enhanced understanding of deception detection research.