Hidden information is the key to many security issues. If there is a reliable method to determine whether someone withholds information, many issues of this type can be resolved. However, until now, no method has proven to be reliable, but technical discoveries in the field of neuroimaging have caused a surge of new research in this area. Many neuroimaging techniques can be used, but functional magnetic resonance is the newest method, and its use in extracting and evaluating information from subjects could be the most significant, given that it records brain states in parallel with current mental activity/behavior, enabling the establishment of correlational links between them. Because the brain state displayed during fMRI imaging is the dependent variable measured during stimulus/task condition manipulation, it is necessary to use fMRI data in combination with complementary criminal interrogation techniques to gather information. This could be particularly important when standard interrogational techniques are not enough in order to preserve the common good, especially in \"ticking bomb\" situations. In this study, we review aspects of the possibility of utilizing advanced neuroimaging in combination with criminal interrogation in cases of serious criminal acts that threaten public safety.

In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to be more reliable. This study introduces a new model for detecting lies using electroencephalogram (EEG) signals. An EEG database of 20 study participants was created to accomplish this goal. This study also used a six-layer graph convolutional network and type 2 fuzzy (TF-2) sets for feature selection/extraction and automatic classification. The classification results show that the proposed deep model effectively distinguishes between truths and lies. As a result, even in a noisy environment (SNR = 0 dB), the classification accuracy remains above 90%. The proposed strategy outperforms current research and algorithms. Its superior performance makes it suitable for a wide range of practical applications.

In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals\' performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

Verbal content analyses to differentiate truthful and fabricated statements, such as the Criteria-Based Content Analysis (CBCA), are used in lie detection research as well as in practice to assess the credibility of statements in criminal court proceedings. Meta-analyses demonstrate validity of verbal content analyses above chance, but the traditional research paradigms usually lack either ecological or internal validity. The authors discuss the usage of immersive virtual reality scenarios to solve this dilemma, as both types of validity can be increased by this approach. In this integrative review of existing literature on the current use of virtual scenarios in forensic and victimology research, the authors extract strengths and limitations for possible VR studies in the context of verbal content analysis. Furthermore, novel ethical challenges involved are summarized and implications for future studies proposed. Overall, we argue in favor of using virtual reality scenarios to validate methods for verbal content analysis, but also urge to consider ethical limitations regarding unwanted short- and long-term aftereffects.

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Previous literature on lie detection abilities bears an interesting paradox. On the group level, people detect others\' lies at guessing level. However, when asked to evaluate their own abilities, people report being able to detect lies (i.e., self-reported lie detection). Understanding this paradox is important because decisions which rely on credibility assessment and deception detection can have serious implications (e.g., trust in others, legal issues). In two online studies, we tested whether individual differences account for variance in self-reported lie detection abilities. We assessed personality traits (Big-Six personality traits, Dark Triad), empathy, emotional intelligence, cultural values, trust level, social desirability, and belief in one\'s own lie detection abilities. In both studies, mean self-reported lie detection abilities were above chance level. Then, lower out-group trust and higher social desirability levels predicted higher self-reported lie detection abilities. These results suggest that social trust and norms shape our beliefs about our own lie detection abilities.

Classical polygraph screenings are routinely used by critical businesses such as banking, law enforcement agencies, and federal governments. A major concern of scientific communities is that screenings are prone to errors. However, screening errors are not only due to the method, but also due to human (polygraph examiner) error. Here we show application of machine learning (ML) to detect examiner errors. From an ML perspective, we trained an error detection model in the absence of labeled errors. From a practical perspective, we devised and tested successfully a second-opinion tool to find human errors in examiners\' conclusions, thus reducing subjectivity of polygraph screenings. We report novel features that uplift the model\'s accuracy, and experimental results on whether people lie differently on different topics. We anticipate our results to be a step towards rethinking classical polygraph practices.

Decades of research have shown that people are poor at detecting deception. Understandably, people struggle with integrating the many putative cues to deception into an accurate veracity judgement. Heuristics simplify difficult decisions by ignoring most of the information and relying instead only on the most diagnostic cues. Here we conducted nine studies in which people evaluated honest and deceptive handwritten statements, video transcripts, videotaped interviews or live interviews. Participants performed at the chance level when they made intuitive judgements, free to use any possible cue. But when instructed to rely only on the best available cue (detailedness), they were consistently able to discriminate lies from truths. Our findings challenge the notion that people lack the potential to detect deception. The simplicity and accuracy of the use-the-best heuristic provides a promising new avenue for deception research.

During the COVID-19 pandemic, defendants and witnesses (as well as the prosecution and defense counsel) may wear medical face masks to prevent the spread of the virus. Alternatively, courtrooms proceedings may take place virtually. In this article, we discuss how these deviations from normal procedures may affect jurors\' lie detection ability and decision-making. Although research addressing this specific question does not exist, we are able to formulate an informed view based on the extensive deception literature. Since nonverbal signs of deception in the face and body are virtually absent, we conclude that medical face mask-wearing or virtual courtroom proceedings will not hamper jurors\' lie detection abilities. If jurors can hear the speech well, they may become better at detecting deception if they pay more attention to speech content, which may occur as a result of mask-wearing in the courtroom.

We conducted (I) 18 event-related potential (ERP) field tests to detect concealed information regarding major terrorist crimes and other real-world crimes and (II) 5 ERP tests regarding participation in a classified counterterrorism operation. This study is a test of the brain fingerprinting scientific standards hypothesis: that a specific set of methods for event-related potential (ERP) concealed information tests (CIT) known as the brain fingerprinting scientific standards provide the sufficient conditions to produce less than 1% error rate and greater than 95% median statistical confidence for individual determinations of whether the tested information is stored in each subject\'s brain. All previous published results in all laboratories are compatible with this hypothesis. We recorded P300 and P300-MERMER ERP responses to visual text stimuli of three types: targets contain known information, irrelevants contain unknown/irrelevant information, and probes contain the situation-relevant information to be tested, known only to the perpetrator and investigators. Classification CIT produced significantly better results than comparison CIT, independent of classification criteria. Classification CIT had 0% error rate; comparison CIT had 6% error rate. As in previous studies, classification-CIT median statistical confidences were approximately 99%, whereas comparison CIT statistical confidences were no better than chance for information-absent (IA) subjects (who did not know the tested information). Over half of the comparison-CIT IA determinations were invalid due to a less-than-chance computed probability of being correct. Experiment (I) results for median statistical confidence: Classification CIT, IA subjects: 98.6%; information-present (IP) subjects (who know the tested information): 99.9%; comparison CIT, IA subjects: 48.7%; IP subjects: 99.5%. Experiment (II) results (Classification CIT): error rate 0%, median statistical confidence 96.6%. Countermeasures had no effect on the classification CIT. These results, like all previous results in our laboratory and all others, support the brain fingerprinting scientific standards hypothesis and indicate that the classification CIT is a necessary condition for a reliable, accurate, and valid brainwave-based CIT. The comparison CIT, by contrast, produces high error rates and IA statistical confidences no better than chance.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11571-022-09795-1.