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Integrating artificial intelligence into border control systems may help to strengthen security and make operations more efficient. For example, the emerging application of artificial intelligence for lie detection when inspecting passengers presents significant opportunities for future implementation. However, as it makes use of technology that is associated with artificial intelligence, the system is classified as high risk, in accordance with the EU AI Act and, therefore, must adhere to rigorous regulatory requirements to mitigate potential risks. This manuscript distinctly amalgamates the technical, ethical, and legal aspects, thereby offering an extensive examination of the AI-based lie detection systems utilized in border security. This academic paper is uniquely set apart from others because it undertakes a thorough investigation into the categorization of these emerging technologies in terms of the regulatory framework established by the EU AI Act, which classifies them as high risk. It further makes an assessment of practical case studies, including notable examples such as iBorderCtrl and AVATAR. This in-depth analysis seeks to emphasize not only the enormous challenges ahead for practitioners but also the progress made in this emerging field of study. Furthermore, it seeks to investigate threats, vulnerabilities, and privacy concerns associated with AI, while providing security controls to address difficulties related to lie detection. Finally, we propose a framework that encompasses the EU AI Act's principles and serves as a foundation for future approaches and research projects. By analyzing current methodologies and considering future directions, the paper aims to provide a comprehensive understanding of the viability and consequences of deploying AI lie detection capabilities in border control. [ABSTRACT FROM AUTHOR]

Deception is a universal human behavior. Yet longstanding skepticism about the validity of measures used to characterize the biological mechanisms underlying deceptive behavior has relegated such studies to the scientific periphery. Here, we address these fundamental questions by applying machine learning methods and functional magnetic resonance imaging (fMRI) to signaling games capturing motivated deception in human participants. First, we develop an approach to test for the presence of confounding processes and validate past skepticism by showing that much of the predictive power of neural predictors trained on deception data comes from processes other than deception. Specifically, we demonstrate that discriminant validity is compromised by the predictor's ability to predict behavior in a control task that does not involve deception. Second, we show that the presence of confounding signals need not be fatal and that the validity of the neural predictor can be improved by removing confounding signals while retaining those associated with the task of interest. To this end, we develop a "dual-goal tuning" approach in which, beyond the typical goal of predicting the behavior of interest, the predictor also incorporates a second compulsory goal that enforces chance performance in the control task. Together, these findings provide a firmer scientific foundation for understanding the neural basis of a neglected class of behavior, and they suggest an approach for improving validity of neural predictors. [ABSTRACT FROM AUTHOR]

Polygraph has had more than 30 years of extensive history in Lithuania. This paper offers an overview rather than in-depth research into how polygraph developed in Lithuania in last three decades. There are similar articles about legal and practical aspects of using polygraph in Lithuania published in Lithuanian, with one of them having been published in European Polygraph in 2007 (Kraujalis et al 2007) nevertheless, the author evaluates current situation of the polygraph in Lithuania. [ABSTRACT FROM AUTHOR]

This article suggests a theory-driven approach to address the managerial problem of distinguishing between real and fake reviews. Building on memory research and linguistics, we predict that when recollecting an authentic experience in a product review, people rely to a greater extent on episodic memory. By contrast, when writing a fictitious review, people do not have episodic memory available to them. Therefore, they must rely to a greater extent on semantic memory. We suggest that reliance on these different memory types is reflected in the language used in authentic and fictitious reviews. We develop predictions about five linguistic features characterizing authentic versus fictitious reviews. We test our predictions via a multi-method approach, combining computational linguistics, experimental design, and machine learning. We employ a large-scale experiment to derive a dataset of reviews, as well as two datasets containing reviews from online platforms. We also test whether an algorithm relying on our theory-driven linguistic features is context independent, relative to other benchmark algorithms, and shows better cross-domain performance when tested across datasets. By developing a theory that extends memory and psycholinguistics research to the realm of word of mouth, this work contributes to our understanding of how authentic and fictitious reviews are created. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

People are poor lie detectors, partly because they hold false beliefs about nonverbal cues to deception. Here, we investigated if guiding people to rely only on a message's detailedness ("take‐the‐best") boosts their lie detection and to what extent such heuristic judgments are immune to nonverbal information. In three studies (Ns = 109, 88 and 144), participants made detailedness‐based veracity judgements, of text versus video statements (Study 1), or of statements without or with biasing nonverbal behavior (truth tellers diverting, liars maintaining gaze; Studies 2 and 3). Compared to unguided judgements, participants using the heuristic method achieved higher deception detection accuracy throughout. Mere access to nonverbal behavior did not deteriorate performance (Study 1), but the heuristic was not fully immune to biasing nonverbal behavior (Studies 2, 3). Our findings challenge the lay notion that access to nonverbal behavior benefits deception detection and suggest that only focusing on diagnostic cues improves lie detection. [ABSTRACT FROM AUTHOR]

Amnesia is a frequent claim in major crimes, and it is estimated that the complete or partial absence of memory following a crime ranges from 25% to 50% of total cases. Although some cases may constitute a genuine form of amnesia, due to organic‐neurological defects or psychological causes, and possibly combined with a dissociative or repressive coping style after an extreme experience, malingering is still fairly common in offenders. Therefore, one of the main goals in medico‐legal proceedings is to find methods to determine the credibility of crime‐related amnesia. At present, a number of lie and memory detection techniques can assist the forensic assessment of the reliability of declarative proof, and have been devised and improved over the past century: for example, modern polygraphs, event‐related potentials, thermal imaging, functional magnetic resonance imaging, kinematic, and facial analysis. Other ad hoc psychological tests, such as the so‐called Symptom Validity Test (SVT) and Performance Validity Test (PVT), as well as the autobiographical Implicit Association Test (aIAT), can also be used. To date, however, there is little evidence or case reports that document their real usefulness in forensic practice. Here, we report two cases of crime‐related amnesia, whereby both defendants, who were found guilty of homicide, appeared to exhibit dissociative amnesia but where the application of SVTs, PVTs, and aIAT detected a malingered amnesia. One of the main goals in the context of crime‐related amnesia is to find methods to distinguish whether it is due to an organic‐neurological origin, psychological factors or malingering. Here, we report two homicide cases in which the application of two memory detection techniques, the forced‐choice test and the autobiographical implicit association test (aIAT), strongly suggested a malingered amnesia. [ABSTRACT FROM AUTHOR]

We examined the Devil's Advocate lie detection method which is aimed at detecting lying about opinions. In this approach, participants give reasons for why they hold an opinion in the eliciting-opinion question and counter-arguments to their opinion in a devil's advocate question. Truth tellers (n = 55) reported their true opinion about protestor actions, whereas lie tellers (n = 55) reported the opposite of their true opinion. Answers were coded for number of arguments and plausibility, immediacy, clarity and scriptedness. Data were analysed with analyses of variance with veracity being the sole factor. Supporting the hypothesis, truth tellers provided more pro-arguments than lie tellers and to all eliciting-opinion questions their answers sounded more plausible, immediate and clear than lie tellers' answers. The opposite pattern was predicted for the devil's advocate question but not found, likely caused by the simplification of the question. Neither was being scripted a diagnostic veracity indicator. [ABSTRACT FROM AUTHOR]

SCAN (Scientific Content Analysis) is a verbal credibility assessment (VCA) tool that claims to detect deception in written statements. Although the validity of SCAN is contested in literature, various (law enforcement) agencies across the globe are trained in using SCAN. To date it remains unknown how SCAN is perceived, and to what extent it is used in practice. Based on a scoping review and qualitative survey, we identified practitioners' and scholars' perceptions on the use(fulness) of SCAN. Data were collected from 48 participants (35 practitioners and 13 scholars). Key findings illuminate (1) that practitioners apply an incomplete, personalized version of SCAN, (2) that SCAN practitioners are reluctant to abandon SCAN, and (3) that SCAN is considered incompatible with (Belgian) legislation on police questioning. Based on practitioners' expressed needs and concerns, we present several alternatives for SCAN, as well as recommendations on how a shift to other techniques can be facilitated. [ABSTRACT FROM AUTHOR]

Research has shown that complications are more common in truth tellers' accounts than in lie tellers' accounts, but there is currently no experiment that has examined the accuracy of observers' veracity judgments when looking at complications. A total of 87 participants were asked to judge 10 transcripts (five truthful and five false) derived from a set of 59 transcripts generated in a previous experiment by Deeb et al. Approximately half of the participants were trained to detect complications (Trained), and the other half did not receive training (Untrained). Trained participants were more likely to look for complications, but they did not detect them accurately, and thus their veracity judgments did not improve beyond Untrained participants' judgments. We discuss that the training may have been too brief or not sensitive enough to enhance decision-making. [ABSTRACT FROM AUTHOR]

Ben‐Shakhar and Verschuere (2024) commented on Brennen and Magnussen's (2023) review of the literature on lie detection. They argued that the review's conclusion that such methods are not ready for forensic use is too pessimistic and that they already are in use worldwide. While we agree that many jurisdictions do in fact use such methods, we see this as cause for alarm rather than confirmation of their utility. It is documented that several lie detection methods distinguish to a statistically significant degree between deceitful and truthful statements, but they also have substantial error rates. This means that applying them at the level of the individual statement will frequently be misleading, making them unsuited to guiding police investigations. [ABSTRACT FROM AUTHOR]

When people tell lies, they often exhibit tension and emotional fluctuations, reflecting a complex psychological state. However, the scarcity of labeled data in datasets and the complexity of deception information pose significant challenges in extracting effective lie features, which severely restrict the accuracy of lie detection systems. To address this, this paper proposes a semi-supervised lie detection algorithm based on integrating multiple speech emotional features. Firstly, Long Short-Term Memory (LSTM) and Auto Encoder (AE) network process log Mel spectrogram features and acoustic statistical features, respectively, to capture the contextual links between similar features. Secondly, the joint attention model is used to learn the complementary relationship among different features to obtain feature representations with richer details. Lastly, the model combines the unsupervised loss Local Maximum Mean Discrepancy (LMMD) and supervised loss Jefferys multi-loss optimization to enhance the classification performance. Experimental results show that the algorithm proposed in this paper achieves better performance. [ABSTRACT FROM AUTHOR]

Copyright of European Journal of Psychology Applied to Legal Context is the property of Sociedad Espanola de Psicologia Juridica y Forense and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Detecting deception has significant implications in fields like law enforcement and security. This research aims to develop an effective lie detection system using Electroencephalography (EEG), which measures the brain's electrical activity to capture neural patterns associated with deceptive behavior. Using the Muse II headband, we obtained EEG data across 5 channels from 34 participants aged 16-25, comprising 32 males and 2 females, with backgrounds as high school students, undergraduates, and employees. EEG data collection took place in a suitable environment, characterized by a comfortable and interference-free setting optimized for interviews. The research contribution is the creation of a lie detection dataset and the development of an autoencoder model for feature extraction and a deep neural network for classification. Data preparation involved several pre-processing steps: converting microvolts to volts, filtering with a band-pass filter (3-30Hz), STFT transformation with a 256 data window and 128 overlap, data normalization using z-score, and generating spectrograms from power density spectra below 60Hz. Feature extraction was performed using an autoencoder, followed by classification with a deep neural network. Methods included testing three autoencoder models with varying latent space sizes and two types of classifiers: three new deep neural network models, including LSTM, and six models using pre-trained ResNet50 and EfficientNetV2-S, some with attention layers. Data was split into 75% for training, 10% for validation, and 15% for testing. Results showed that the best model, using autoencoder with latent space size of 64x10x51 and classifier using the pre-trained EfficientNetV2-S, achieved 97% accuracy on the training set, 72% on the validation set, and 71% on the testing set. Testing data resulted in an F1-score of 0.73, accuracy of 0.71, precision of 0.68, and recall of 0.78. The novelty of this research includes the use of a cost-effective EEG reader with minimal electrodes, exploration of single and 3-dimensional autoencoders, and both non-pretrained classifiers (LSTM, 2D convolution, and fully connected layers) and pretrained models incorporating attention layers. [ABSTRACT FROM AUTHOR]

The article titled "Aspects of the Application of the Polygraph to Crime Investigation. The Particulars of the Pre-Test Stage" aims to highlight the complexity and finesse of the procedures associated with using the polygraph in the process of crime investigation. This work attempts to build a bridge of knowledge between the norms and protocols in force in the Republic of Moldova and Romania, focusing on a detailed analysis of the pre-test stage, one of the crucial phases of the entire polygraph examination process. The first section of the article delves into the norms and practices in the Republic of Moldova, exploring the legislative and procedural mechanisms that govern the use of the polygraph in criminal investigations. A comparative analysis with the regulatory framework and practices in Romania will be conducted to highlight the similarities and differences between the two jurisdictions. The most significant focus is placed on a detailed analysis of the pre-test stage, a defining step that can significantly influence the results of the polygraph test. The article will explore the various procedures and strategies applied in this stage, as well as the impact they can have on the final results of the polygraph examination. Through this work, an attempt is made not only to highlight the complex procedures and protocols surrounding the use of the polygraph in investigations but also to call for a deeper understanding of the importance of the pre-test stage in obtaining reliable and conclusive results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep learning methods. EEG signals were acquired from 27 individuals using a wearable EEG device called Emotiv Insight with 5 channels (AF3, T7, Pz, T8, AF4). Each person took part in two trials: one where they were honest and another where they were deceitful. During each experiment, participants evaluated beads they saw before the experiment and stole from them in front of a video clip. This study consisted of four stages. In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. Later, the overlapping sliding window (OSW) method was used for data augmentation. In the third stage, feature extraction was performed. To achieve this, EEG signals were analyzed by combining discrete wavelet transform (DWT) and fast Fourier transform (FFT) including statistical methods (SM). In the last stage, each obtained feature vector was classified separately using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNNLSTM hybrid algorithms. At the study's conclusion, the most accurate result, achieving a 99.88% accuracy score, was produced using the LSTM and DWT techniques. With this study, a new data set was introduced to the literature, and it was aimed to eliminate the deficiencies in this field with this data set. Evaluation results obtained from the data set have shown that this data set can be effective in this field. [ABSTRACT FROM AUTHOR]

The exponential growth of crime, violence, and wrongdoing is linked to dysfunction in restraining aggressive impulses, leading to neglect and disrespect of others' feelings, rights, and needs. Two basic forms of communication, verbal and nonverbal, serve individuals to interact and exchange ideas and attitudes with other people in everyday life situations, including within the framework of criminal investigations and criminal justice. The purpose of this study was to draw an analogy between verbal communication (oral speech, written speech) and nonverbal communication (body language, gestures, and silence), with the tendency to explore different components of nonverbal communication and place them in the context of criminal profiling as a method for detecting lies in nonverbal communication. The aim of this study was to emphasize the importance of nonverbal communication as an equal part of the communication process, and often a predominant one in this domain, which helps us demystify suspects, perpetrators, and criminal acts. [ABSTRACT FROM AUTHOR]

Lying has become an element of human nature. People lie intentionally or unintentionally at any point in their lives. Human beings can deceive by lying to justify themselves about something or to get rid of a wrongdoing. This lie can result in various consequences, including health deterioration, loss of life, a sense of insecurity, criminal behaviors, and more. Such situations are more common especially in daily life, security, and criminology. In these cases, lie detection is of vital importance. With the development of technology, lie detection becomes a more important issue. People can manipulate others and provide information by lying. This situation has led researchers to turn to more alternative ways and the importance of EEG signals has increased. Since EEG signals are difficult to manipulate, there has been an increase in their use and analysis in lie detection studies. In this study, lie detection was performed with EEG signals and the importance of EEG signals was demonstrated. Within the scope of this study, a novel hybrid deep learning method was designed on the Bag-of-Lies dataset, which was created using different methods, and lie detection was carried out. The study consisted of four stages. In the first stage, EEG data were obtained from the Bag-of-Lies dataset. In the second stage, the data were decomposed into sub-signals by DWT method. These signals, which were separated in the third stage, were classified with the designed novel hybrid deep learning model. At the last stage of the study, the performance of the classifier was determined by accuracy, precision, recall, F1-score, and AUC score. At the conclusion of the research, an accuracy score of 97.88% was achieved, demonstrating the significance of EEG signals in this domain. [ABSTRACT FROM AUTHOR]

Lie detection is a crucial aspect of human interactions that affects everyone in their daily lives. Individuals often rely on various cues, such as verbal and nonverbal communication, particularly facial expressions, to determine if someone is truthful. While automated lie detection systems can assist in identifying these cues, current approaches are limited due to a lack of suitable datasets for testing their performance in real-world scenarios. Despite ongoing research efforts to develop effective and reliable lie detection methods, this remains a work in progress. The polygraph, voice stress analysis, and pupil dilation analysis are some of the methods currently used for this task. In this study, we propose a new detection algorithm based on an Enhanced Recurrent Neural Network (ERNN) with Explainable AI capabilities. The ERNN, based on long short-term memory (LSTM) architecture, was optimized using fuzzy logic to determine the hyperparameters. The LSTM model was then created and trained using a dataset of audio recordings from interviews with a randomly selected group. The proposed ERNN achieved an accuracy of 97.3%, which is statistically significant for the problem of voice stress analysis. These results suggest that it is possible to detect patterns in the voices of individuals experiencing stress in an explainable manner. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Researchers have often claimed that the interviewer's nonverbal behavior such as nodding facilitates rapport building, the number of recalled details, and verbal veracity cues. However, there is no experiment to-date that isolated the effects of nodding in information gathering interviews. We thus examined the effects of interviewer's nodding behavior on rapport perceptions and on the number and accuracy of total details provided by truth tellers and lie tellers. Participants (N = 150) watched a video recording and then reported it truthfully or falsely to an interviewer. The interviewer showed demeanor that was either supportive with nodding, supportive without nodding, or neutral. Truth tellers reported more total details than lie tellers and these effects were similar across demeanor conditions. No significant effects emerged for rapport perceptions and accuracy of total details. These results suggest that the interviewer's nodding behavior does not affect rapport perceptions and details provided by truth tellers and lie tellers. [ABSTRACT FROM AUTHOR]

In this article we propose to make an analysis and evaluation of some aspects related to the application of the polygraph to the investigation of crimes. Attention will be paid to the professional skills and competences of the polygraph examiner, as well as the need to know the special procedures that can be applied during the testing. We will establish criteria for classifying the profile of the criminal under psycho-criminological and criminological aspects.The circumstances to be elucidated by applying the polygraph to the investigation of acts of terrorism. We will analyze the psycho-criminological profile of the victim, witnesses and other categories of people to be tested by applying the polygraph. We will focus on the manner and methodology of conducting the polygraph examination and make recommendations for further research in the field of polygraph application [ABSTRACT FROM AUTHOR]

Copyright of European Journal of Psychology Applied to Legal Context is the property of Sociedad Espanola de Psicologia Juridica y Forense and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acción Psicológica is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social Defense Theory (SDT) states that anxious attachment reflects an adaptive sentinel strategy, whereby anxious people should be better able to detect lies than secure people. Existing research on this issue, however, has not been able to evaluate whether heightened lie detection among anxious individuals is due to an actual ability or a bias to assume that others are lying (one that pays off when others are, in fact, lying). We addressed this issue in a study in which 254 adults had to determine whether people in videos were lying or telling the truth about their experiences. Contrary to the predictions of SDT, highly anxious people did not have a heightened ability to separate lies from truths, but were biased to assume that others were lying regardless of the authenticity of their statements. [ABSTRACT FROM AUTHOR]

In human-computer interaction, much empirical research exists. Online questionnaires increasingly play an important role. Here the quality of the results depend strongly on the quality of the given answers, and it is essential to distinguish truthful from deceptive answers. There exist elegant single modalities for deception detection in the literature, such as mouse tracking and eye tracking (in this paper, respectively, measuring the pupil diameter). Yet, no combination of these two modalities is available. This paper presents a combined approach of two cognitive-load-based lie detection approaches. We address study administrators who conduct questionnaires in the HCI, wanting to improve the validity of questionnaires. [ABSTRACT FROM AUTHOR]

Previous research indicated that drawing is a strategic tool in investigations. However, little research has been conducted to determine how to introduce a sketch instruction into an investigative interview. The current experiment compared sketching and speaking separately (sketch‐speak and speak‐sketch conditions) with sketching and speaking synchronously to determine which recall method gathered the most information and elicited the most cues to deceit. Participants (N = 180) watched a video, half were asked to tell the truth (to a friendly agent), whereas the other half were asked to lie (to a corrupt agent) about the video in an interview. Participants were asked either to (i) sketch then speak, (ii) speak then sketch, or (iii) speak and sketch synchronously. Verbal statements and sketches were coded for total details, number of people, number of self‐handicapping strategies and plausibility. The camera perspective of sketches was also examined. Truth tellers' sketches and verbal statements were rated as more plausible than those of lie tellers. This difference was found in all three recall methods with the largest effect emerging in the speaking and sketching synchronously condition. Truth tellers included more details than lie tellers in their verbal statements but not in their sketches. Conversely, truth tellers included more people than lie tellers in their sketches but not in their verbal responses. No veracity differences emerged in sketches nor verbal statements for self‐handicapping strategies. However, participants in the speaking and sketching synchronously recall method included more sketch self‐handicapping strategies than those in the other recall conditions. Additionally, participants in the speak and sketch synchronously method reported more verbal details than those who spoke and then sketched. Effects were not large enough between speaking in isolation of sketching and also sketching and speaking synchronously to determine which recall method is best to detect deceit. However, sketching and speaking synchronously emerged as the most beneficial method for information‐gathering. [ABSTRACT FROM AUTHOR]

The present work documents our investigation of proximal (i.e., immediate) effects of existential threat on the process of lie detection. Specifically, we hypothesized that lie detection accuracy will be higher in a mortality salience (MS) condition compared with the control condition. In two lab-based studies (N = 120; N = 109) and one internet study (N = 1294), we did not find any evidence for this hypothesis, that is, MS effects on lie detection accuracy were constantly not significant. However, these null findings should not be overstated. Instead, the present contribution aims to reveal the theoretical and methodological challenges in properly testing proximal MS effects on lie detection accuracy. First, we make transparent that our theoretical assumptions regarding the underlying mechanisms changed during the research process from MS-induced vigilance (Studies 1 and 2) to MS-induced negative affect (Study 3) and remain speculative. Moreover, we show how and why we adapted the operationalization from study to study to optimize adequate testing of the idea. In sum, this work aims to be informative for conducting future research rather than to provide conclusive evidence against or in favor of the investigated idea. [ABSTRACT FROM AUTHOR]

Five experiments (total number of judging participants = 1309, four different kinds of stimulus materials with a total of 464 messages, total number of judgements = 19,634) investigated the influence of personal uncertainty on the process of lie detection in social relationships. Building on and extending basic assumptions of uncertainty management models, we reasoned that uncertainty about themselves motivates people to evaluate the quality of their relationships. A crucial aspect of the quality of relationships with other people is the truthfulness with which they communicate verbally with you and anyone else. We proposed that if these assumptions are valid, reminding people of their personal uncertainties should lead them to use valid verbal cues in veracity judgements more. This enhanced usage of valid verbal cues should result in better accuracy in deception detection. An internal meta‐analysis of the five experiments reveals only a small, not significant, overall effect of uncertainty salience on detection accuracy with larger effect sizes for experiments conducted in the laboratory than for those conducted online. Hence, if personal uncertainty plays a role in the process of deception detection, it seems to be subject to moderators such as methodological or motivational factors. [ABSTRACT FROM AUTHOR]

In contrast to other Western-European countries, polygraph testing is being used in police investigations in Belgium for about twenty years now. However, until recently, its use was not legally regulated. On 1 January 2021 an amendment to the law came into force which officially regulates the use of the polygraph in the Belgian Code of Criminal Procedure. Yet, in light of findings from academic research polygraph testing in criminal proceedings is controversial. Therefore, these Belgian practices and developments could be considered to be an outlier. Against this background, this article examines the recent legal regulation on polygraph testing and its use in practice from both a legal and criminological-psychological perspective. As such, the article offers important insights for law and practice on polygraph testing and the use of comparable other techniques across Europe and beyond. [ABSTRACT FROM AUTHOR]

The article describes the current legal status of the polygraph examination method in the context of expert evidence in criminal proceedings in the Czech Republic, England, and Wales, with a focus on the method of language interpretation. The aim of the article is to provide a description of the existing knowledge as well as the valid and effective legislation and case law in the field of expert proof in criminal cases. The basic research design is to use the language interpretation method to describe the current law and the case law of the Polygraph Examination Method, considering the Guideline for Evaluative Reporting in Forensic Science (2015) and guidelines for best practice in forensic science and the European Network of Forensian Science Institutes (ENFSI) for expert assessment of forensic sciences. [ABSTRACT FROM AUTHOR]

Robots destined to tasks like teaching or caregiving have to build a long-lasting social rapport with their human partners. This requires, from the robot side, to be capable of assessing whether the partner is trustworthy. To this aim a robot should be able to assess whether someone is lying or not, while preserving the pleasantness of the social interaction. We present an approach to promptly detect lies based on the pupil dilation, as intrinsic marker of the lie-associated cognitive load that can be applied in an ecological human–robot interaction, autonomously led by a robot. We demonstrated the validity of the approach with an experiment, in which the iCub humanoid robot engages the human partner by playing the role of a magician in a card game and detects in real-time the partner deceptive behavior. On top of that, we show how the robot can leverage on the gained knowledge about the deceptive behavior of each human partner, to better detect subsequent lies of that individual. Also, we explore whether machine learning models could improve lie detection performances for both known individuals (within-participants) over multiple interaction with the same partner, and with novel partners (between-participant). The proposed setup, interaction and models enable iCub to understand when its partners are lying, which is a fundamental skill for evaluating their trustworthiness and hence improving social human–robot interaction. [ABSTRACT FROM AUTHOR]

In this study, partial mutual information at the source level was used to construct brain functional networks in order to examine differences in brain functions between lying and honest responses. The study used independent component analysis and clustering methods to computationally generate source signals from EEG signals recorded from subjects who were lying and those who were being honest. Partial mutual information was calculated between regions of interest (ROIs), and used to construct a functional brain network with ROIs as nodes and partial mutual information values as connections between them. The partial mutual information connections that showed significant differences between the two groups of people were selected as the feature set and classified using a functional connectivity network (FCN) classifier, resulting in an accuracy of 88.5%. Analysis of the brain networks of the lying and honest groups showed that, in the lying state, there was increased informational exchange between the frontal lobe and temporal lobe, and the language motor center of the frontal lobe exchanged more information with other brain regions, suggesting increased working and episodic memory load and the mobilization of more cognitive resources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Reaction Time-Based Concealed Information Test (RT-CIT) was designed to detect familiarity with crime-related information. However, RT-CIT results can be manipulated by preparing innocent-looking responses for these probes. We developed a new paradigm allowing us to assess such response preparation processes. In each trial of the task, a crime-related prime question was presented, followed by a test item which was either a publicly known item, an irrelevant item, or a probe. The test item could either match the question in terms of content or not, and a key had to be pressed if the answer was not publicly known (Go-Nogo task). In two experiments (total N = 138), we found evidence for both familiarity (slower reactions toward crime-related than unrelated information) and response preparation effects (less errors for probes matching the prime question) in guilty participants, indicating that the new paradigm can help to overcome problems of strategic response preparation. [ABSTRACT FROM AUTHOR]

Lie detection test is highly significant task due to its impact on criminology and society. Computerized lie detection test model using electroencephalogram (EEG) signals is studied in literature. In this paper we studied deep learning framework in lie detection test paradigm. First, we apply a preprocessing technique to utilize only a small fragment of the EEG image instead of the whole image. Our model describes a temporal feature map of the EEG signals measured during the lie detection test. A deep learning attention model (V-TAM) extracts the temporal map vector during the learning process. This technique reduces computational time and lessens the overfitting in Deep Learning architectures. We propose a Cascading attention model with a deep learning convolutional neural network (CNN). V-TAM model extracts local features and global features in separate paths spatial and temporal. Also, to enhance the EEG segmentation precision, a novel Visual-Temporal AttentionModel (V-TAM) is proposed. The accuracy was evaluated using data measured from a sensor from a public dataset of 9512 subjects during fifteen minutes lie detection task. We compared our model with three recent published models. Our proposed model attained the highest performance of (98.5%) with (p<0.01). The visual-temporal model of the proposed platform shows an optimized balance between prediction accuracy and time efficiency. Validation investigationwere performed to prove the correctness and reliability of the proposed method through sizing of the input data, proving its effectiveness in attaining satisfactory performance by using only a smaller size input data. [ABSTRACT FROM AUTHOR]

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The current study aimed at testing the impact of the cognitive interview for suspects (CIS) used by trained custom officers on the quantity of gathered details, compared to a control standard interview (SI) used by untrained officers. Forty‐five mock‐suspects were required to perform a series of actions and each was interviewed by a pair of customs officers. Participants had to give statements containing truthful parts and deceptive parts. The CIS elicited significantly more details than the SI. Truthful parts of the statements contained more details than deceptive parts. An interaction effect revealed that the CIS elicited a higher number of action details in truthful parts. It is worthwhile for professionals in the field to adopt the CIS, which provides valuable benefits for information gathering. Moreover, the increase in action details raise the question of considering it as a possible lie detection tool. [ABSTRACT FROM AUTHOR]

Sketching while narrating involves describing an event while sketching on a blank paper (self‐generated sketch) or on a printed map. We compared the effects of self‐generated sketches and printed maps on information elicitation and lie detection. Participants (N = 211) carried out a mock mission and were instructed to tell the truth or to lie about it in an online interview. In the first phase of the interview, all participants provided a free recall. In the second phase, participants provided another free recall or verbally described the mission while sketching on a blank paper or on a printed map. Truth tellers provided richer accounts than lie tellers. Larger effect sizes emerged for the self‐generated sketch condition than for the printed map and free recall conditions. This suggests that self‐generated sketches are more effective lie detection tools when information on routes and locations is sought. [ABSTRACT FROM AUTHOR]

Background: Sketching while narrating is an effective interview technique for eliciting information and cues to deceit. The current research examined the effects of introducing a Model Sketch in investigative interviews andis pre-registered on https://osf.io/kz9mc (accessed on 18 January 2022). Methods: Participants (N = 163) completed a mock mission and were asked to tell the truth or to lie about it in an interview. In Phase 1 of the interview, participants provided either a free recall (control condition), sketched and narrated with exposure to a Model Sketch (Model Sketch-present condition), or sketched and narrated without exposure to a Model Sketch (Model Sketch-absent condition). In Phase 2, all participants provided a free recall without sketching. Results: Truth tellers reported significantly more information than lie tellers. The Model Sketch elicited more location details than a Free recall in Phase 1 and more veracity differences than the other Modality conditions in Phase 2. Conclusion: The Model Sketch seems to enhance the elicitation of information and to have carryover veracity effects in a follow-up free recall. [ABSTRACT FROM AUTHOR]

Summary: The Devil's Advocate protocol has been developed to assist making veracity assessments when someone discusses their opinion. The present experiment focused on protester actions rather than controversial issues and also included an adapted version of the Verifiability Approach. Participants told the truth or lied about protester actions and the participants' answers to the eliciting opinion and Devil's Advocate questions were compared. The Devil's Advocate approach predicts the difference in answers (residue scores) to be more pronounced in truth tellers than in lie tellers in terms of quantity of the answers (number of words, details and arguments) and quality of the answers (plausibility, immediacy and clarity). The hypothesis was supported but only in terms of quality: Truth tellers' answers sounded more plausible and immediate and somewhat clearer than lie tellers' answers. Truth tellers also reported more digital verifiable sources than lie tellers. [ABSTRACT FROM AUTHOR]

This article provides an overview of over 100 years of social science research on deception and lying. The aim is to raise awareness on the full scope of research findings on deception and lying to help the scientific community to communicate these research findings to practitioners who assess the veracity of individuals statements, further future research, better understand the research field of deception and lying, and bridge gaps that are relevant to scholars and practitioners interested in deception and lying. To begin, Web of Science is introduced, and the steps undertaken to build our database are described. Then, the yearly evolution of research findings on deception and lying is presented. Finally, the journals and the research areas, as well as the authors, the institutions and the countries that contributed the most to the deception and lying literature are highlighted, as well as the most used keywords and cited articles. [ABSTRACT FROM AUTHOR]

The feedback concealed information test (fCIT) is a novel form of the CIT, providing participants with feedback regarding their memory concealment performance. The fCIT utilizes event‐related potentials (recognition‐P300 and feedback‐related event‐related potentials) and has been shown to provide high efficiency in detecting information concealment. However, it is unclear how well the fCIT performs in the presence of mental countermeasures. To address this question, participants were trained to use countermeasures during fCIT. Results showed that the recognition‐P300 efficiency decreased when participants used countermeasures. However, the efficiencies of feedback‐related negativity and feedback‐P300 were unchanged, with feedback‐P300 still showing a high detection efficiency (AUC = 0.86) during countermeasures. These findings demonstrate the potential of fCIT for subverting countermeasures. [ABSTRACT FROM AUTHOR]

We consider six myths regarding mistaken beliefs about psychological processes (perception, memory, judgment) shaping legal evidence, and we offer research to debunk each. These myths include the idea that identification evidence provided by police officers is more reliable than similar evidence provided by civilians, that officers can reliably detect deception, that officers' memories are not compromised by stress, and the claim that providing one sleep cycle after a use-of-force incident improves officers' memories. We also consider cognitive mechanisms that contribute to the maintenance of these myths. In debunking the myths, we take on the more general issue of why myths related to cognitive processing in the real world should be addressed and suggest multiple vehicles for doing so. We consider both the benefits and the obstacles for each path, and end by suggesting a novel resource for debunking myths in the legal system. [ABSTRACT FROM AUTHOR]

Previous research has produced mixed results on the question of whether confidence in ad hoc veracity judgments can be used as an indicator of judgment accuracy. These studies have used a variety of measures to analyze the confidence‐accuracy relationship; however, they have rarely explicitly addressed why a particular measure was chosen and what its properties are. We theoretically and empirically examined previously used measures of metacognition in lie detection and report the results these measures yielded in re‐analyses of 12 lie detection studies (total N = 2817 participants). Regardless of the measure, none of the studies found a confidence‐accuracy relationship. Discrepancies between the measures are likely due to conceptual differences between them, emphasizing the importance of carefully selecting appropriate measures for the research question at hand. More work on the underlying processes of confidence judgments in lie detection is needed to improve the assessment of confidence and the selection of appropriate measures. [ABSTRACT FROM AUTHOR]

Research has consistently shown people predominantly rely on undiagnostic nonverbal cues when detecting deceit, whereas verbal cues are more accurate. In three experiments, we investigated whether the simple instruction not to focus on nonverbal cues would make people focus more on diagnostic verbal cues and hence more accurate in detecting lies. Participants judged the veracity of true and deceptive statements and either received (1) no instruction, (2) the instruction to ignore nonverbal cues, or (3) to ignore nonverbal cues and focus on verbal cues instead. In the second and third experiments, condition 3 was changed to an audio condition in which visual cues were inaccessible. Results showed no effect of instruction on lie detection performance. Overall, we found no evidence that the simple instruction not to focus on nonverbal cues while judging veracity is an effective strategy to make people focus more on verbal cues or to improve lie detection. [ABSTRACT FROM AUTHOR]

Different forms of deception can be applied to hide one's true intentions, this faking can take various forms, depending on the deceiver's aim(s): faking bad or faking good. Since 25-45% of behaviors are deceptive in a legal setting, detecting these efforts has become a priority. With this aim, the development of objective and effective tools has increased considerably. Starting with the first polygraph created by Vittorio Benussi in 1914 to nowadays through the implementation of Artificial Intelligence, we will explore the lie detection techniques that have followed over the years. [ABSTRACT FROM AUTHOR]

Although it is true that past thinkers developed relevant taxonomies of the phenomenon of memory at the behavioral level. only in the last century has our scientific understanding of the underlying brain mechanisms of memory progressed remarkably. New acquisitions include that memory is not just a functional or physiological process, but a structural or anatomical one as well, that conceiving of memory as merely cognitive or centered on cognition is misleading, or that memory does not require any consciousness or intentionality. On the applicative side, recent technological advances offered opportunities of modifying memory with biological means, and detecting more effectively whether someone is remembering or lying. The current issue of Medicina nei Secoli is dedicated to Memon in contemporary biomedicine: cross-disciplinary scenarios. It hosts seven expert contributions to the field covering different areas of medical inquiry (i.e., immunology, neuroscience, gerontology) and humanistic-social perspectives (history, epistemology, ethics, and law). [ABSTRACT FROM AUTHOR]