Deception Judgments Research Articles

Training in the Strategic Use of Evidence technique: Improving deception detection accuracy of American law enforcement officers

The Strategic Use of Evidence (SUE) approach is a framework for planning and executing suspect interviews with the aim of facilitating judgments of truth and deception. US law enforcement officers (N = 59) either received training in the SUE approach or did not. Each officer interviewed a mock suspect (N = 59) who had either committed a simulated security breach or had completed a benign task. The officers who received SUE training interviewed in line with the training: They questioned the suspect systematically, withheld the evidence and critical case information until after questioning, and relied on statement-evidence inconsistency to detect deceit. Consequently, SUE-trained interviewers achieved a higher deception detection accuracy rate (65%) compared to untrained interviewers (43%).

You must be lying because I don't understand you: Language proficiency and lie detection.

We examined the impact of interviewees' language proficiencies on observers' lie detection performance. Observers (N = 132) were randomly assigned to make deception judgments about interviewees (N = 56) from Four proficiency groups (i.e., native, advanced, intermediate, and beginner English speakers). Discrimination between lie- and truth-tellers was poorest when observers judged beginner English speakers compared to interviewees from any other proficiency group. Observers were also less likely to exhibit a truth-bias toward nonnative than native English speakers. These results suggest that interviewing individuals in their nonnative languages can create inequalities in the justice system. (PsycINFO Database Record

Using reality monitoring to improve deception detection in the context of the cognitive interview for suspects.

Research has found that deception detection accuracy in the context of suspect interrogation hovers around chance levels. Geiselman (2012) adapted the cognitive interview (typically used for witnesses) for use with suspects (CIS) and found that judgments of deception were more accurate than previous interrogation techniques. The current study attempted to use the CIS to improve deception detection with Reality Monitoring (RM: Vrij et al., 2008), which has already been validated in the context of witness statements. One hundred sixty-six undergraduate students were randomly assigned to 2 conditions. In the Truthful condition, participants played a game with a confederate, whereas in the Deceptive condition, participants rehearsed (but did not experience) a synopsis of the game scenario. Participants in the Deceptive condition were also instructed to steal $10 from a confederate's wallet. In both conditions, $10 was purported to be missing and a researcher blind to condition conducted a CIS. Statement veracity was coded using 6 of the RM criteria advanced by Vrij et al. (frequency of visual, auditory, spatial, temporal, cognitive, and affective details). According to results from a MANOVA, truthful and deceptive statements differed significantly on all RM criteria, with the exception of affective details, validating the importance for evaluation of statement veracity (p ≤ .01). Further, a binary logistic regression found that combining the RM criteria together correctly classified 86.6% of statements, χ(²)(6) = 114.4, p < .001, with excellent sensitivity and specificity (.899 and .833, respectively). As well, Visual, Auditory, and Cognitive details uniquely predicted condition. Findings support using RM criteria to detect deception in interviews conducted with the CIS.

Rejoinder to Levine, Clare et al.'s Comparison of the Park-Levine Probability Model Versus Interpersonal Deception Theory: Application to Deception Detection

Levine, Clare, Green, Serota and Park (2014) present studies intended to demonstrate that the Park–Levine probability model (PLM) better accounts for accuracy in detecting interactive deception than interpersonal deception theory (IDT). This rejoinder makes 6 points: (a) the PLM is a description not an explanation; (b) IDT and its empirical support are seriously mischaracterized; (c) application of the PLM to interactive deception is based on a faulty understanding of what constitutes interactive deception; (d) the test pitting IDT against the PLM is invalid; (e) IDT offers a rival explanation for the pattern of results; and (f) empirical data show that deception judgments covary dynamically with deceptiveness of the messages being produced. Other misstatements are also addressed.

Producing deception detection expertise

Purpose– To function effectively, police must separate lies from truth. Police, ideally, would be experts at this task, yet there is debate surrounding whether expertise in detecting deception is possible. Drawing upon literature outside of deception detection, the purpose of this paper is to explore whether subjects making deception judgments can improve their performance.Design/methodology/approach– The sample was 19 students from two graduate-level classes. Subjects viewed six sets of videos over eight weeks. The first five sets displayed individuals reporting whether they cheated on an exam. The sixth set displayed individuals reporting whether they had committed a (mock) robbery. After each video, subjects judged whether the videoed individual was truthful, and then the actual status was revealed.Findings– Subjects’ accuracy improved consistently over the first five sets of videos; from about 69 percent accuracy to about 89 percent accuracy. However, the accuracy for the sixth set of videos dropped dramatically to 46 percent. The results indicate that expertise in deception detection may be possible, but is likely to be limited in terms of generalizability.Research limitations/implications– The actual environment of police investigations is more variable than the study’s setting. Future studies should integrate inaccurate and incomplete feedback, which are realistic characteristics of investigations.Practical implications– The findings suggest that, like other areas of expertise, it may be possible to develop expertise in detecting deception through the use of deliberate practice with accurate feedback.Originality/value– This study combines three literature-substantiated criteria for developing deception detection expertise. The study environment involved regularity. Subjects were deliberately practicing, and subjects received accurate feedback.

Establishment of Confidence Thresholds for Interactive Voice Response Systems Using ROC Analysis

INTRODUCTION An Interactive Voice Response (IVR) System is a platform for man-machine interaction by the use of voice or keypad. Examples abound. Whenever one calls most large organizations, their initial encounter is with a machine that will prompt the caller for their intent. Usually, such machines will give you options to choose from (Directed Dialog), or it may ask for your input (Open Dialog). In the case of Open Dialog, there is the risk that the machine does not understand a caller input. This is an area where a lot of investigation takes place to deduce why this is the case. The technology for recognizing keyed input is not as challenging as speech technology because each key on the keypad corresponds to a specific sound frequency that cannot be confounded with another key. This technology is called Dual Tone Multi Frequency (DTMF.(i)); and it is a mature technology due to the fact that there is little or no variability in the tone emitted by a particular key. This is not the case with speech. In the case of speech technology, there are several variables that come into play. These include whether a caller barges-into a prompt, whether there is a lot of background noise that may be of similar frequency as the spoken utterance, whether the user is using a cell phone, a speaker phone, or a computer. These, and several other factors, affect the way an IVR system recognizes the caller input. This paper is an attempt to establish guidelines for determining the best settings under which an IVR system should accept a caller input using ROC analysis. REVIEW OF LITERATURE Receiver Operating Characteristics (ROC) analysis has been used in medical imaging to measure diagnostic accuracy (Metz, 2008; Pepe, 2000; Griner, Mayewski, Mushlin, & Greenland, 1981). To diagnose diseases, (McClish, 1989) used this technique to analyze the accuracy of the diagnosis. He preferred this technique because it provided the investigator with all possible combinations of sensitivity and specificity. ROC analysis has been used in the field of radiology (Metz & Obuchowski, 2003). ROC analysis was applied to biomedical informatics, (Lasko, Bhagwat, Zou, & Ohno-Machado, 2005; Brown & Davis, 2006; Hand, & Till, 2001), Signal Detection Theory (Green & Swets, 1966); it provides a precise language and graphic notation for analyzing decision-making in the presence of uncertainty. ROC curves are used extensively in epidemiology and medical research and are frequently mentioned in conjunction with evidence- based medicine (Zweig & Campbell, 1993). Bond and DePaulo (2006) used ROC analysis to study the accuracy of Deception judgments by studying over 20,000 judgments, and came to the conclusion that such analysis correlated strongly with other methods of analysis. In the field of Artificial Intelligence (Fogarty, Baker, & Hudson, 2005), ROC curves have proved useful for the evaluation of machine learning techniques (Flach, 2004; Fawcett, 2006). The approach used in this paper is to extend the use of ROC analysis to Speech Recognition. If an utterance is clearly understood (with high/medium confidence) the caller will be led further down the rest of the call flow. If, however, the IVR engine is not certain what the caller input is, it would be compelled to re-prompt the caller so as to confirm that the original intent was correctly identified. After the second attempt at recognition, for caller inputs that are still not clearly understood by the IVR engine, the caller will be transferred to a live agent. This is what the IVR engine is designed for - to minimize (and possibly eliminate) the cost of transferring to a live agent. THE ENVIRONMENT The Interactive Voice Response (IVR) environment consists of a platform for collecting and analyzing caller utterances using a voice recognizer. The quality of the categorization varies with the parameter settings of the recognizer. The two main parameters of the recognizer are: the energy floor and the confidence threshold. …

Registered report: measuring unconscious deception detection by skin temperature.

Findings from the deception detection literature suggest that although people are not skilled in consciously detecting a liar, they may intuit that something about the person telling a lie is off. In the current proposal, we argue that observing a liar influences the observer’s physiology even though the observer may not be consciously aware of being lied to (i.e., the observers’ direct deception judgment does not accurately differentiate between liars and truth-tellers). To test this hypothesis, participants’ finger temperature will be measured while they watch videos of persons who are either honest or dishonest about their identity. We hypothesize that skin temperature will be lower when observing a liar than when observing a truth-teller. Additionally, we test whether perceiving a liar influences finger skin temperature differently when an individual is, or is not, alerted to the possibility of deceit. We do this by varying participants’ awareness of the fact that they might be lied to. Next to measuring physiological responses to liars and truth-tellers, self-reported direct and indirect veracity judgments (i.e., trustworthiness and liking) of the target persons will be assessed. We hypothesize that indirect veracity judgments will better distinguish between liars and truth-tellers than direct veracity judgments.

The Accuracy of Auditors' and Layered Voice Analysis (LVA) Operators' Judgments of Truth and Deception During Police Questioning*

The purpose of this study was to determine if auditors could identify truthful and deceptive persons in a sample (n=74) of audio recordings used to assess the effectiveness of layered voice analysis (LVA). The LVA employs an automated algorithm to detect deception, but it was not effective here. There were 31 truthful and 43 deceptive persons in the sample and two LVA operators averaged 48% correct decisions on truth-tellers and 25% on deceivers. Subsequent to the LVA analysis the recordings were audited by three interviewers, each independently rendering a decision of truthful or deceptive and indicating their confidence. Auditors' judgments averaged 68% correct decisions on truth-tellers and 71% on deceivers. Auditors' detection rates, generally, exceeded chance and there was significantly (p<0.05) greater confidence on correct than incorrect judgments of deceivers but not on truth-tellers. These results suggest that the success reported for LVA analysis may be due to operator's judgment.

Preventing Cross-Cultural Bias in Deception Judgments

The expectancy violation model proposes that people infer deception when the communicator violates social norms without obvious cause. However, social norms are culture specific. Therefore, discrepant norms between a communicator and an observer in a cross-cultural interaction might increase the likelihood of inferring deception and thus result in bias. The present study investigated whether informing people about cultural differences in nonverbal behavior could counteract cross-cultural bias in deception judgments. Sixty-nine Australian students were randomly assigned to receive no information, general information, or specific information about culture-specific behavioral norms prior to making credibility judgments of 10 video clips (5 norm consistent and 5 norm inconsistent). The results suggest that cross-cultural biases in deception judgments can occur but may also be prevented by providing appropriate information. These findings require further investigation but have potentially significant implications in law enforcement, customs, immigration, and broader societal interactions.

Investigating the "Deceiver Stereotype": Do Older Adults Associate Averted Gaze With Deception?

Research indicates that the most commonly held belief about deception is that people avert their gaze when lying. The present study assessed adult age-related differences in both the association between averted gaze and judgments of deception and the strength of the "deceiver stereotype." In Study 1, younger and older adult participants were required to decide if individuals displaying direct gaze or differing degrees of gaze aversion were lying or telling the truth. In Study 2, a group of younger and older adults were explicitly asked about their beliefs concerning how different behaviors related to deception. Findings revealed that, compared with younger participants, when asked to decide whether individuals were lying, older adults were less likely to associate direct gaze with honesty and averted gaze with deception. This effect was not due to age-related differences in the strength of the deceiver stereotype, as when explicitly asked, both younger and older participants associated averted gaze with lying. These findings provide further evidence of age-related differences in the ability to extract socially relevant information from the eye region, which might relate to changes in visual scanning of facial features with age.

Why do lie-catchers fail? A lens model meta-analysis of human lie judgments.

Decades of research has shown that people are poor at detecting lies. Two explanations for this finding have been proposed. First, it has been suggested that lie detection is inaccurate because people rely on invalid cues when judging deception. Second, it has been suggested that lack of valid cues to deception limits accuracy. A series of 4 meta-analyses tested these hypotheses with the framework of Brunswik's (1952) lens model. Meta-Analysis 1 investigated perceived cues to deception by correlating 66 behavioral cues in 153 samples with deception judgments. People strongly associate deception with impressions of incompetence (r = .59) and ambivalence (r = .49). Contrary to self-reports, eye contact is only weakly correlated with deception judgments (r = -.15). Cues to perceived deception were then compared with cues to actual deception. The results show a substantial covariation between the 2 sets of cues (r = .59 in Meta-Analysis 2, r = .72 in Meta-Analysis 3). Finally, in Meta-Analysis 4, a lens model analysis revealed a very strong matching between behaviorally based predictions of deception and behaviorally based predictions of perceived deception. In conclusion, contrary to previous assumptions, people rarely rely on the wrong cues. Instead, limitations in lie detection accuracy are mainly attributable to weaknesses in behavioral cues to deception. The results suggest that intuitive notions about deception are more accurate than explicit knowledge and that lie detection is more readily improved by increasing behavioral differences between liars and truth tellers than by informing lie-catchers of valid cues to deception.

Interviewing suspects: Practice, science, and future directions

Crime suspects in the USA are typically questioned in a two‐step process aimed, first, at behavioural lie detection during a pre‐interrogation interview, followed by the elicitation of a confession during the interrogation itself (in Great Britain, the practice of investigative interviewing does not make this sharp distinction). Research conducted on the first step shows that police investigators often target innocent people for interrogation because of erroneous but confident judgments of deception. Research on the second step shows that innocent people are sometimes induced to confess to crimes they did not commit as a function of certain dispositional vulnerabilities or the use of overly persuasive interrogation tactics. Citing recent studies, this paper proposes that laboratory paradigms be used to help build more diagnostic models of interrogation. Substantively, we suggest that the British PEACE approach to investigative interviewing may provide a potentially effective alternative to the classic American interrogation. As a matter of policy, we suggest that the videotaping of entire interrogations from a balanced camera perspective is necessary to improve the fact finding accuracy of judges and juries who must evaluate confession evidence in court.

Can intuition improve deception detection performance?

Two studies examined the role of processing style (intuitive vs. deliberative processing) in a deception detection task. In the first experiment, a thin slicing manipulation was used to demonstrate that intuitive processing can lead to more accurate judgments of deception when compared with traditional deliberative forms of processing. In the second experiment, participants who engaged in a secondary (concurrent) task performed more accurately in a deception detection task than participants who were asked to provide a verbal rationale for each decision and those in a control condition. Overall, the results converge to suggest that intuitive processing can significantly improve deception detection performance.

The Psychology of Confessions

Despite the potency of confession evidence in criminal law, recent DNA exonerations indicate that false confessions are a contributing factor in numerous wrongful convictions. After distinguishing between voluntary, compliant, and internalized false confessions, this article reviews research implicating a sequence of three processes responsible for false confessions and the adverse consequences of these confessions. First, police often target innocent people for interrogation because of erroneous judgments of truth and deception made during preinterrogation interviews. Second, innocent people are sometimes induced to confess as a function of certain police interrogation tactics, dispositional suspect vulnerabilities, and naive mental state that accompanies innocence. Third, people cannot readily distinguish between true and false confessions and often fail to discount those confessions they perceive to be coerced. At present, researchers are seeking ways to improve the accuracy of confession evidence and its evaluation in the courtroom.

Training to detect what? The biasing effects of training on veracity judgments

AbstractResearch has failed to show that training to detect deception substantially increases accuracy. Instead, training yields a stronger tendency to make judgments of deceptiveness. Normally, training programmes place a strong emphasis on deception and deception cues. This may lead observers to engage in a biased information seeking process wherein only deception cues are searched for, and any suggestion that the person is being truthful is neglected. Two experiments were conducted in which participants made veracity judgments before and after being ostensibly trained to (a) detect deception (traditional training group or TRAD‐GR), (b) detect truthfulness (alternative training group or ALT‐GR) or (c) not being trained (control group or CONT‐GR). Deception judgments increased for the TRAD‐GR, but decreased for the ALT‐GR, and did not change for the CONT‐GR. Judgmental confidence significantly increased in both training groups, but not in the CONT‐GR. These results indicate that traditional training programmes to detect deception bias the trainees' judgments towards deception. An emphasis on truthfulness cues could compensate for this tendency, as well as for the professionals' inclination to judge other people's statements as deceptive. However, the poor diagnostic value of deception cues makes it difficult to design good training programmes. Copyright © 2008 John Wiley &amp; Sons, Ltd.

False Confessions

Despite the commonsense belief that people do not confess to crimes they did not commit, 20 to 25% of all DNA exonerations involve innocent prisoners who confessed. After distinguishing between voluntary, compliant, and internalized false confessions, this article suggests that a sequence of three processes is responsible for false confessions and their adverse consequences. First, police sometimes target innocent people for interrogation because of erroneous judgments of truth and deception. Second, innocent people sometimes confess as a function of certain interrogation tactics, dispositional suspect vulnerabilities, and the phenomenology of innocence. Third, jurors fail to discount even those confessions they see as coerced. At present, researchers are seeking ways to improve the accuracy of confession evidence and its evaluation in the courtroom.

Individual differences in judging deception: Accuracy and bias.

The authors report a meta-analysis of individual differences in detecting deception, confining attention to occasions when people judge strangers' veracity in real-time with no special aids. The authors have developed a statistical technique to correct nominal individual differences for differences introduced by random measurement error. Although researchers have suggested that people differ in the ability to detect lies, psychometric analyses of 247 samples reveal that these ability differences are minute. In terms of the percentage of lies detected, measurement-corrected standard deviations in judge ability are less than 1%. In accuracy, judges range no more widely than would be expected by chance, and the best judges are no more accurate than a stochastic mechanism would produce. When judging deception, people differ less in ability than in the inclination to regard others' statements as truthful. People also differ from one another as lie- and truth-tellers. They vary in the detectability of their lies. Moreover, some people are more credible than others whether lying or truth-telling. Results reveal that the outcome of a deception judgment depends more on the liar's credibility than any other individual difference.

Audit Inquiries and Deception Detection: Standards, Research, and Guidance

During each phase of the audit members of the engagement team pose numerous questions, the answers to which may affect both the course and outcome of the audit. The authors posit that an understanding of the psychology of lying may provide auditors with an additional tool with which to evaluate verbal responses to audit inquiries and thereby lessen detection risk. A large body of literature suggests that people are only about 4% better than chance at identifying deception. Additionally, most people hold stereotypical views about the indicators of deception, such as a lack of eye contact indicates deception. Unfortunately, research has found many of these stereotypical views to be invalid indicators of deception. The paper concludes by exploring 6 guidelines that may assist auditors in making more accurate judgments of truth or deception.

Accuracy of Deception Judgments

We analyze the accuracy of deception judgments, synthesizing research results from 206 documents and 24,483 judges. In relevant studies, people attempt to discriminate lies from truths in real time with no special aids or training. In these circumstances, people achieve an average of 54% correct lie-truth judgments, correctly classifying 47% of lies as deceptive and 61% of truths as nondeceptive. Relative to cross-judge differences in accuracy, mean lie-truth discrimination abilities are nontrivial, with a mean accuracy d of roughly .40. This produces an effect that is at roughly the 60th percentile in size, relative to others that have been meta-analyzed by social psychologists. Alternative indexes of lie-truth discrimination accuracy correlate highly with percentage correct, and rates of lie detection vary little from study to study. Our meta-analyses reveal that people are more accurate in judging audible than visible lies, that people appear deceptive when motivated to be believed, and that individuals regard their interaction partners as honest. We propose that people judge others' deceptions more harshly than their own and that this double standard in evaluating deceit can explain much of the accumulated literature.

Accuracy of Deception Judgments: Appendix A

Accuracy of Deception Judgments: Appendix A