A cognitive approach to lie detection: A meta‐analysis

Abstract

Introduction This article provides a meta‐analysis of a new, cognitive approach to (non‐)verbal lie detection. This cognitive lie detection approach consists of three techniques: (1) imposing cognitive load, (2) encouraging interviewees to say more, and (3) asking unexpected questions. Method A meta‐analysis was carried out on studies using the cognitive approach, 14 of which directly compared the cognitive approach to a standard approach. Results The cognitive lie detection approach produced superior accuracy results in truth detection (67%), lie detection (67%), and total detection (truth and lie detection combined, 71%) compared to a traditional standard approach (truth detection: 57%; lie detection: 47%; total detection: 56%). Conclusions Practitioners may find it useful to use a cognitive lie detection approach in their daily practice.

Using unexpected questions to elicit information and cues to deceit in interpreter‐based interviews

“…Therefore, around a decade ago, researchers started to examine whether verbal differences between truth tellers and liars could be elicited or enhanced through theory-based interview methods . One strand of this research is called cognitive lie detection and involves three key elements: imposing cognitive load, encouraging interviewees to say more, and asking unexpected questions (Vrij, Fisher, & Blank, 2017;Vrij, Leal, Mann, Vernham, & Brankaert, 2015). Cognitive lie detection appears to elicit cues to deception (Vrij, Fisher, Blank, Leal, & Mann, 2016) and to facilitate lie detection (Vrij, Fisher, et al, 2017).…”

The first direct replication on using verbal credibility assessment for the detection of deceptive intentions

“…Although it is not clear what caused the lengthier answers in the replication, it is possible that the differences mentioned above played a role so that, for example, participants were more talkative because they already made the trip. Importantly, however, that difference in answer length should not have lowered that chance for replication as lengthier statements are typically better suited for verbal deception detection than shorter ones (Vrij et al, 2015) and several methods are specifically designed to elicit lengthier and richer verbal accounts (e.g., the model statement technique, Harvey, Vrij, Leal, Lafferty, & Nahari, 2017). …”

“…In the challenge to tell truth‐tellers from liars, verbal deception detection has emerged as one of the more promising approaches (Oberlader et al, 2016; Vrij, Fisher, & Blank, 2015). Verbal deception detection sets out to identify verbal indicators of deception in statements made about an event.…”

“…Verbal deception detection sets out to identify verbal indicators of deception in statements made about an event. Based on the notion that liars will have more difficulty providing a convincing and hence detailed account of a fabricated event than truth‐tellers, the cognitive approach to deception postulates that the differences in difficulty are represented in, for example, the richness of the verbal account about the event (Vrij et al, 2015). Similarly, the theory of Reality Monitoring poses that the content of a statement about a genuinely experienced event can be recalled in more detail than the content of a fabricated event (Johnson, Bush, & Mitchell, 1998).…”

“…Both the cognitive approach and Reality Monitoring agree on the prediction that truthful statements are richer in detail than deceptive statements. There is a body of research on the verbal deception detection approach with meta‐analytical findings suggesting that detail richness can identify liars and truth‐tellers better than chance (Masip, Sporer, Garrido, & Herrero, 2005; Oberlader et al, 2016; Vrij et al, 2015). However, meta‐analyses rely on the quality of the original studies and cannot ascertain whether the individual effects reported in studies are reliable (van Elk et al, 2015).…”