Using the verifiability of details as a test of deception: A conceptual framework for the automation of the verifiability approach

The Verifiability Approach (VA) is a promising new approach for deception detection. It extends existing verbal credibility assessment tools by asking interviewees to provide statements rich in verifiable detail. Details that i) have been experienced with an identifiable person, ii) have been witnessed by an identifiable person, or iii) have been recorded through technology, are labelled as verifiable. With only minimal modifications of information-gathering interviews this approach has yielded remarkable classification accuracies. Currently, the VA relies on extensive manual annotation by human coders. Aiming to extend the VA’s applicability, we present a work in progress on automated VA scoring. We provide a conceptual outline of two automation approaches: one being based on the Linguistic Inquiry and Word Count software and the other on rule-based shallow parsing and named entity recognition. Differences between both approaches and possible future steps for an automated VA are discussed. 1 Cognitive deception detection Based on the rationale that the default setting in human communication is honesty (Levine, 2014; Verschuere & Shalvi, 2014), the cognitive approach to deception (e.g. Zuckerman et al., 1981) postulates that the act of lying requires extra mental effort compared to telling the truth (e.g. trying to fabricate a convincing lie; Vrij, 2014). The general idea of lying being correlated to increased mental effort has been corroborated by a great body of research, using self-reports, behavioral, autonomic, electrophysiological, and neural measures (Ganis et al., 2003; Verschuere et al., 2011). To further increase cognitive differences between lying and truth telling, the cognitive approach advocates applying minimal interventions in information-gathering interviewing situations that enlarge the differences between the truth tellers and liars (e.g. asking unexpected questions, asking to recall a story in reverse order; Vrij et al., 2015, Meissner et al., 2012). A recent metaanalysis indicates that cognitive techniques outperform standard interviews (Vrij et al., 2015). This body of work has also found the most reliable differences between truths and lies to be manifested in verbal rather than nonverbal behavior. Ormerod and Dando (2014), for instance, applied cognitive interviewing techniques on mock-passengers and found verbal detection methods to by far outperform its behavioral counterparts (e.g. spotting suspicious behavior). Also, objective judgments (i.e., algorithmic scoring such as discriminant analysis) outperformed human judgments (truths: 60% vs. 80%; lies: 64% vs. 73%, for human vs. objective judgments, respectively, Vrij et al., 2015). The superiority of objective criteria might be explained by the sheer amount of information for humans to take into account to derive a binary truth versus lie judgment (e.g. Rubin & Conroy, 2012).