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A lively debate between Bond and Uysal (2007, Law and Human Behavior, 31, 109-115) and O'Sullivan (2007, Law and Human Behavior, 31, 117-123) concerns whether there are experts in deception detection. Two experiments sought to (a) identify expert(s) in detection and assess them twice with four tests, and (b) study their detection behavior using eye tracking. Paroled felons produced videotaped statements that were presented to students and law enforcement personnel. Two experts were identified, both female Native American BIA correctional officers. Experts were over 80% accurate in the first assessment, and scored at 90% accuracy in the second assessment. In Signal Detection analyses, experts showed high discrimination, and did not evidence biased responding. They exploited nonverbal cues to make fast, accurate decisions. These highly-accurate individuals can be characterized as experts in deception detection.

Media reports frequently depict older adults as victims of deception. The public perceives these stories as particularly salient because older adults are seen as fragile victims taken advantage of because of trusting behaviors. This developmental investigation of deception detection examines older and younger adults interacting in 2 contexts, prison and the "free world," to discover whether older adults are vulnerable to deception. Younger prisoners were found to be lie biased. Older adults were better able to discriminate lies than younger adults, and this effect was localized primarily to older female adults. Findings indicate that discriminability strongly increases from younger to older age for women, whereas men do not show an improvement, as age increases, in making decisions about statement veracity.

The 2020 U.S. Presidential election was a campaign that could be characterized as 'one of the nastiest presidential campaigns in recent memory,' partly because the general election debates were highly contentious and featured frequent interruptions and several insults and invectives between candidates. This research compared the language used in the debates to fact-checked truths and lies using a Reality Monitoring (RM) deception detection algorithm in Linguistic Inquiry and Word Count (LIWC) to investigate the veracity of real-life high-stakes verbal messages in the political context. We found that overall RM scores were lower and not significantly different between debate language and fact-checked lies, and RM scores were significantly higher in fact-checked truth statements, indicating that most debate language uttered was deceptive. This result supports the finding that the RM algorithm in LIWC distinguishes truth from lies and debate language in the context of politics. The 60.7% classification rate in this study may reflect a problem with the relatively short word counts of fact-checked lie and truth statements, but most probably reflects individual candidates' deviations in RM features used in their statements. Each individual has a style that they use in communication—'the way people talk and write have been recognized as stamps of individual identity.' Even with a corpus of many statements from the same individual candidates, they probably regularly amplify certain features of RM and diminish other features of RM in their truthful and deceptive messages. This is a fruitful area of research that could be explored in future studies. [ABSTRACT FROM AUTHOR]

Language in the high-stakes 2016 US presidential primary campaign was contentious, filled with name-calling, personal attacks, and insults. Language in debates served at least three political functions: for image making, to imagine potential realities currently not in practice, and to disavow facts. In past research, the reality monitoring (RM) framework has discriminated accurately between truthful and deceptive accounts (~70% classification). Truthful accounts show greater sensory, time and space, and affective information, with little evidence of cognitive operations. An RM algorithm was used with Linguistic Inquiry and Word Count software to code candidates' language. RM scores were significantly higher in fact-checked truth statements than in lies, and debate language in the 2016 primaries was as deceptive as fact-checked lies. In a binary logistic regression model, one RM criterion, cognitive processes, predicted veracity using computerized RM, classifying 87% of fact-checked truth statements but only 28% of fact-checked lie statements (63% classification overall).Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Reliability and validity of the Generalized Communication Suspicion scale (GCS; Levine & McCornack, 1991) was tested in this research with prisoners who participated in three studies (N=207, ages 18–84). Convergent validity was assessed using Interpersonal Trust (Rotter, 1967) and Locus of Control scales (Paulhus & Van Selst, 1990). Divergent validity was tested using Rosenberg’s Self-Esteem scale (1965), and a hypothetically-unrelated prisoner’s self-rating of overall health. Trichotomized GCS on lie-bias did not substantiate predictive utility of the scale. Further studies could include a design that incorporates multiple levels of prisoner custody to better measure the predictive utility of the scale. [Copyright &y& Elsevier]

Linguistic Inquiry and Word Count (LIWC) software was used to code truthful and deceptive words in prisoners' natural language. Reality Monitoring (RM) and Newman, Pennebaker, Berry, & Richards' (NP, 2003) models were used. NP indicates that lies contain fewer self-references, other references, and exclusive words, and higher numbers of negative emotion and motion words. Higher sensory, spatial, temporal and affective RM terms were predicted for truths, and more cognitive mechanism words were predicted for lies. The RM model's hit rate was 71.1% and discriminability was 1.11 without spatial words, which were surprisingly higher in lies than in truth statements, and the NP model was 69.7%, d′ = 0.99. The software models were contrasted with humans' hit rates, and younger prisoners had 71% hits, d′ = 0.22, but older prisoners had 50% hits, d′ = 0.90. The software set unbiased criteria (β), but younger prisoners were biased in setting their criteria when judging statement veracity, β = -0.34. Without other references, found to be higher in truths than in lies, NP classified 59% of statements correctly. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

If a listener becomes suspicious during a conversation, and asks questions (probes) of a speaker, the listener tends to judge the speaker's message as honest. This result has been termed the probing effect (McCornack, Levine, Aleman, Oetzel, & Miller, 1991). This study hypothesized that an untested decision-making phenomenon, an opposite probing effect, or a post-probe tendency to judge a message as deceptive, might occur when lie-biased individuals judge statement veracity. Prison inmates and non-inmates participated in dyads as judges and speakers. Speakers watched a video, and then lied or told the truth to judges. Judges covertly showed thumbs up or down before asking questions, and subsequently made post-probe judgments. Findings indicate that inmates use heuristic processing to a greater extent than non-inmates, and that inmates, surprisingly, exhibit a probing effect, and not an opposite probing effect, when heuristic processing is employed to decide message veracity. [ABSTRACT FROM AUTHOR]