Lie Detection

European Psychologist, 2014

Law and Human Behavior, 2009

We examined whether individuals' ability to detect deception remained stable over time. In two sessions, held one week apart, university students viewed video clips of individuals and attempted to differentiate between the lie-tellers and truth-tellers. Overall, participants had difficulty detecting all types of deception. When viewing children answering yes-no questions about a transgression (Experiments 1 and 5), participants' performance was highly reliable. However, rating adults who provided truthful or fabricated accounts did not produce a significant alternate forms correlation (Experiment 2). This lack of reliability was not due to the types of deceivers (i.e., children versus adults) or interviews (i.e., closed-ended questions versus extended accounts) (Experiment 3). Finally, the type of deceptive scenario (naturalistic vs. experimentally-manipulated) could not account for differences in reliability (Experiment 4). Theoretical and legal implications are discussed.

Deception in computer-mediated communication is defined as a message knowingly and intentionally transmitted by a sender to foster a false belief or conclusion by the perceiver. Stated beliefs about deception and deceptive messages or incidents are content analyzed in a sample of 324 computer-mediated communications. Relevant stated beliefs are obtained through systematic sampling and querying of the blogosphere based on 80 English words commonly used to describe deceptive incidents. Deception is conceptualized broader than lying and includes a variety of deceptive strategies: falsification, concealment (omitting material facts) and equivocation (dodging or skirting issues). The stated beliefs are argued to be valuable toward the creation of a unified multi-faceted ontology of deception, stratified along several classificatory facets such as (1) contextual domain (e.g., personal relations, politics, finances & insurance), (2) deception content (e.g., events, time, place, abstract notions), (3) message format (e.g., a complaint: they lied to us, a victim story: I was lied to or tricked, or a direct accusation: you're lying), and (4) deception variety, each tied to particular verbal cues (e.g., misinforming, scheming, misrepresenting, or cheating). The paper positions automated deception detection within the field of library and information science (LIS), as a feasible natural language processing (NLP) task. Key findings and important constructs in deception research from interpersonal communication, psychology, criminology, and language technology studies are synthesized into an overview. Deception research is juxtaposed to several benevolent constructs in LIS research: trust, credibility, certainty, and authority. "If falsehood, like truth, had only one face, we would be in a better shape. For we would take as certain the opposite of what the liar said. But the reverse of truth has a hundred thousand shapes and a limitless field." (Of Liars, p.24).

2010

A promising method to detect deception is the Guilty Knowledge Test (GKT) which assesses whether an individual possesses knowledge about a particular crime. Specifically, the GKT involves a series of questions with multiple answers where one answer is relevant to the crime and ...

Recent improvements in effectiveness and accuracy of the emerging field of automated deception detection and the associated potential of language technologies have triggered increased interest in mass media and general public. Computational tools capable of alerting users to potentially deceptive content in computer-mediated messages are invaluable for supporting undisrupted, computermediated communication and information practices, credibility assessment and decision-making. The goal of this ongoing research is to inform creation of such automated capabilities. In this study we elicit a sample of 90 computer-mediated personal stories with varying levels of deception. Each story has 10 associated human deception level judgments, confidence scores, and explanations. In total, 990 unique respondents participated in the study. Three approaches are taken to the data analysis of the sample: human judges, linguistic detection cues, and machine learning. Comparable to previous research results, human judgments achieve 50-63 percent success rates, depending on what is considered deceptive. Actual deception levels negatively correlate with their confident judgment as being deceptive (r = -0.35, df = 88, ρ = 0.008). The highest-performing machine learning algorithms reach 65 percent accuracy. Linguistic cues are extracted, calculated, and modeled with logistic regression, but are found not to be significant predictors of deception level, confidence score, or an authors' ability to fool a reader. We address the associated challenges with error analysis. The respondents' stories and explanations are manually content-analyzed and result in a faceted deception classification (theme, centrality, realism, essence, self-distancing) and a stated perceived cue typology. Deception detection remains novel, challenging, and important in natural language processing, machine learning, and the broader library information science and technology community.

Detecting Deception is part of the Wiley Series Psychology of Crime, Policing and Law. The purpose of this series is to inform practitioners involved in the many aspects of the judicial process about the latest research ndings which may have implications for real world policy and practice in crime investigation, detection, policing, and Law. Detecting Deception is eectively an update on two previous books in the series, Detecting Lies and Deceit: The Psychology of Lying and the Implications for Professional Practice (2000) and Detecting Lies and Deceit: Pitfalls and Opportunities (2008) by Aldert Vrij, Professor of Applied Social Psychology at the University of Portsmouth. However, this latest publication is an edited book, with fourteen chapters written by leading psychology researchers in the field, providing readers with the most comprehensive review of the state of deception detection research, theory, and practice at the present time.

IRJET, 2022

A claim that is thought to be untrue and is often made with the intent to deceive someone is called a lie. The lie is challenging to distinguish from the truth as the variations between true and false claims are so negligible. Lying requires more cognitive effort than stating the truth because the liar must work hard to close all the gaps in the lie. When feeling fear, anxiety, or extreme excitement, a person's oxygen consumption rate, BP, galvanic skin resistance, etc. will significantly increase. This is the basis of lie detection. Lies can be detected psychologically by probing for details, asking unexpected questions, and exerting cognitive strain on the subject. Recently, deception detection has advanced beyond polygraphs to include electroencephalography, eye blink patterns, voice signals, etc. This paper presents the various advanced lie detection techniques and their comparison.

Frontiers in Psychology, 2022

There is agreement among researchers that no simple verbal cues to deception detectable by humans have been demonstrated. This paper examines the evidence for the most prominent current methods, critically considers the prevailing research strategy, proposes a taxonomy of lie detection methods and concludes that two common types of approach are unlikely to succeed. An approach to lie detection is advocated that derives both from psychological science and common sense: When an interviewee produces a statement that contradicts either a previous statement by the same person or other information the authorities have, it will in many cases be obvious to interviewer and interviewee that at least one of the statements is a lie and at the very least the credibility of the witness is reduced. The literature on Strategic Use of Evidence shows that features of interviews that foster such revelatory and self-trapping situations have been established to be a free account and the introduction of independent information late and gradually into the proceedings, and tactics based on these characteristics constitute the best current general advice for practitioners. If any other approach 1 day challenges this status quo, it is likely to be highly efficient automated systems.

Psychological Science in the Public Interest, 2010

Group Decision and Negotiation, 2010