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 ¼ 0:99. The software models were contrasted with humans' hit rates, and younger prisoners had 71% hits, d 0 ¼ 0:22, but older prisoners had 50% hits, d 0 ¼ 0:90. The software set unbiased criteria (), but younger prisoners were biased in setting their criteria when judging statement veracity, ¼ À0.34. Without other references, found to be higher in truths than in lies, NP classified 59% of statements correctly.

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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.

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).