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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]

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.

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]

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.)

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]

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]

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]

Background/Objective: Witness’ interviews within a judicial investigation represent a complex procedure because human memory is subjected to suggestions and personal re-elaboration processes. Therefore, it is necessary to use an interview method that guarantees the accuracy of the testimony. Method: This research focused on the development a modified version of the Cognitive Interview based on the use of the five Ws (who, what, where, when and why), commonly used in journalism, which we named the 5Ws-CI. The goal of the present study is to compare this method with two interrogation techniques already present in the literature, the Enhanced Cognitive Interview (ECI) and the Cognitive Interview with the Category Clustering Recall (CCR) and to test the moderating role of Machiavellianism. Seventy-two subjects individually watched a video-clip of a robbery and were randomly assigned to one of the three interview groups (ECI, CCR-CI, or 5Ws-CI) and the veracity condition (truth tellers vs. lie tellers). Results: Subjects in the 5Ws-CI and CCR groups remembered significantly more information than subjects in the ECI condition, without impacting on accuracy. Also, truth tellers reported more details than lie tellers. No significant difference was found between the 5Ws-CI and CI-CCR interview groups. Conclusions: In conclusion, the results indicate that the 5Ws-CI and CCR techniques might be effective methods and outperform the ECI.

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]

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]

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 four experiments, we examined whether pairs of truth tellers could be distinguished from pairs of lie tellers by taking advantage of the fact that only pairs of truth tellers can refer to shared events by using brief expressions (high-context communication style). In Experiments 1 and 2, pairs of friends and pairs of strangers pretending to be friends answered (i) questions they likely had expected to be asked (e.g., ‘How did you first meet’?) and (ii) unexpected questions (e.g., ‘First, describe a shared event in a few words. Then elaborate on it’). Pairs were interviewed individually (Experiment 1, N = 134 individuals) or collectively (Experiment 2, N = 130 individuals). Transcripts were coded for the verbal cues details, complications, plausibility, predictability, and overlap (Experiment 1 only) or repetitions (Experiment 2 only). In two lie detection experiments observers read the individual transcripts in Experiment 3 (N = 146) or the collective transcripts in Experiment 4 (N = 138). The verbal cues were more diagnostic of veracity and observers were better at distinguishing between truths and lies in the unexpected than in the expected questions condition, but only when the pair members were interviewed individually.

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.

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]

Background/Aim: has shown that sketching while narrating facilitates the elicitation of information and verbal veracity cues in single interviews. We examined if these effects are retained when suspects are shown their sketch after one week in a repeated interview. Method: Participants (N = 173) completed a mock mission and then told the truth or lied about it in an immediate interview (interview 1). Participants either verbally reported the mission (Free recall condition) or sketched it while describing what they were sketching (sketch condition). After one week, all participants were asked for a free recall without sketching (interview 2). Half of the participants in the Sketch condition had access to their sketch while they verbally reported the event whereas the remaining half did not access the sketch. Results: Truth tellers provided more information than lie tellers in both interviews, and sketching elicited more information than a free recall but only in Interview 1. Participants who had access to their sketch in interview 2 repeated more information than those who did not have access, but accessing the sketch did not have an effect on veracity cues. Conclusions: Thus, sketching enhanced the elicitation of information in Interview 1 and access to the sketch in interview 2 seemed helpful for recalling previously reported information.

Rapid advancements in artificial intelligence (AI) have driven interest in its potential application for lie detection. Unfortunately, the current approaches have primarily focused on technical aspects at the expense of a solid methodological and theoretical foundation. We discuss the implications thereof and offer recommendations for the development and regulation of AI-based deception detection. [ABSTRACT FROM AUTHOR]

Advances in medical imaging in the last several years have paved the way for new applications outside healthcare, such as in fields such as lie detection and people screening. However, these applications outside of therapeutic settings raise ethical concerns regarding privacy, permission, and potential abuse. The current limitations of medical imaging in various contexts are examined in this paper, which highlights the need for robust research protocols, regulatory frameworks, and informed consent processes.1 Neuro-ethical concerns are tackled to ensure the development and use of new neuro-technologies in a way that respects people's autonomy and is ethical. Lie Detection and Ethical Concerns False positive detection using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) during interrogations pertaining to national security and criminal investigations has attracted a lot of interest.2 In their view, these methods could improve security and help solve crimes by tracking brain activity patterns linked with telling the truth or lying. 3 However, owing to factors such as small sample sizes, controlled laboratory settings, and individual heterogeneity in brain activity patterns, the scientific validity of imaging-based lie detection is still in dispute. Further, major ethical concerns arise when people are subjected to intrusive brain scans without their knowledge or agreement. Involuntary subjecting of detainees or suspects to such testing raises concerns about potential violations of informed consent and civil liberties, as well as the disproportionate impact on marginalized communities and the widening of existing justice system gaps. Because the technology is still in its early stages, there is a great chance of misunderstandings and false allegations. Pre-employment Screening and Ethical Concerns The use of medical imaging for the purpose of screening potential employees is another non-clinical application. Scans, according to proponents, could reveal personality attributes—like the ability to manage one's impulses or emotions—that are important for a certain job. Most studies depend on small samples and group averages instead of individual forecasts, hence the scientific evidence supporting this assertion is weak.4 In addition, the human aspect is lost when people are reduced to biological statistics, which ignores the intricacies of human behavior. Members of already-vulnerable populations may feel further pressure to submit to sexually explicit brain scans for hiring reasons, even when they have not given their informed consent. Due to the present constraints, the predictive efficacy and ethical acceptability of screening with medical imaging are seriously doubted. Informed Consent and Privacy Concerns In non-clinical uses of medical imaging, getting really informed permission is a major concern.5 Subjects under coercion may feel forced to submit to scans during interrogations or when their participation is crucial to a hiring decision. It is possible that even in study settings, participants do not completely understand the consequences of their agreement, especially when it comes to the prospect of their brain data being accessed and examined in the future due to technological advancements.6 Strong privacy protections are required because sensitive medical information, if obtained, could be misused or disclosed without authorization. Neuroimaging data must be protected against possible misuse and abuse by implementing stringent governance and oversight structures. 7 Although there are interesting possibilities for medical imaging outside of healthcare, rushing into adoption could severely compromise people's freedoms and independence. What is scientifically valid today can be proven wrong tomorrow because the technology is continually evolving.8 Current methodological constraints in neuroscience must be addressed through stringent research standards incorporating large representative samples before non-clinical or commercial utilization.9 Robust governance mechanisms should be put in place to prevent misuse, and consent processes should be tightened to make sure people are making really informed decisions. Responsible development and application of developing neuro-technologies requires open, interdisciplinary dialogue between neuro-ethicists, legal specialists, legislators, and society at large. We must seriously address the ethical obligations that accompany the ever-increasing capabilities of medical imaging. Particularly for uses outside of direct treatment, safeguarding individual privacy and consent ought to take precedence. Respecting human autonomy and societal well-being is essential for responsibly developing and applying medical imaging technologies. Only then can we fully utilize their promise.

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.

Purpose: Differences in social cognition between autistic and nonautistic people may put autistic people at greater risk of being deceived. To inform communication interventions related to deception, the purposes of this mixed-methods study were to examine the deception detection strategies used by young adults with varying levels of autistic traits and to explore whether those strategies differed between groups or in terms of accuracy. Methods: Fifty-one young adults with varying levels of autistic traits watched a series of videos. For each video, the participant judged whether the recorded speaker was truthful and gave the reasoning for their judgment. Inductive thematic analysis was used to identify themes in participants’ stated reasons, which were used to quantitatively examine (1) between-group differences based on self-reported autistic traits and (2) theme-based differences in accuracy. Results: Thematic analysis of the open-ended responses yielded four major themes: subjective descriptions of the person, nonverbal communication, observable features of the response, and nebulous reasons. Statistical analyses indicated no significant group differences in frequency of use of the four themes. When the four themes were compared with each other, observable response features yielded significantly more accurate judgments than nonverbal communication or subjective descriptions. Discussion: Findings are discussed within the framework of speech–language pathologists’ role in helping communicators determine a speaker’s truthfulness, with the ultimate goal of avoiding deception and/or manipulation. Suggestions for incorporating research findings into the design of communication interventions are included. [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]

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Purpose Methods Results Conclusions The reverse order recall technique has been suggested as tool to improve deception detection accuracy. We conducted a registered replication and extension of Vrij et al., 2012's two experiments, testing whether the reverse order technique increases cues to deception in liars and accuracy in lie detection.Following Vrij et al., 2012, in Experiment 1, participants were interviewed twice—once lying and once telling the truth—about a mission they completed. In both interviews, participants recounted their experience in chronological and reverse order. We coded interviews for cues to deception according to a cognitive approach: those included in Vrij et al., 2012 (replication) and others included in research on the cognitive load approach (extension). In Experiment 2, participants read two transcripts (replication) or viewed two videos (extension) from Experiment 1 and decided whether senders were lying or telling the truth in continuous (replication) and dichotomous judgements (extension).In Experiment 1, truth tellers were more detailed and plausible than liars. However, we failed to find the interaction between veracity and route recall reported by the original research on the replication or extensions cues. In Experiment 2, we only found an interaction between veracity and route recall for senders telling the truth on dichotomous lie detection judgement. However, this was not supported when examining overall accuracy.These findings do not provide support for the reverse order technique as a tool to improve deception detection. We suggest further theoretical development before this technique is trained to practitioners. [ABSTRACT FROM AUTHOR]

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]

Background and objective: In both the general and specialized population, certain beliefs are held that relate nonverbal communication (NVC) to the probability that a person is telling the truth or lying. However, the evidence indicates that there is no nonverbal indicator or marker that can accurately and reliably discriminate between honest and dishonest testimony, warning of the pseudoscientificity of these techniques. Despite this, the use of NVC indicators is widely used in the fields of security and justice. Therefore, the present work aims to analyze the errors, practical utility and inadequate uses underlying the current practice of NVC in the Spanish legal-forensic context. Method: We made a review of the available evidence regarding NVC, as well as of the legal process and scientific criteria in the technical forensic field of the application of these techniques. Conclusions: The evidence on the use of NVC in the legal-forensic context is extremely limited, especially in the Spanish population, and does not meet the Daubert standards of admissibility of scientific evidence, i.e., judicially it is pseudoscientific evidence. This applies not only to its use in the detection of lying or deception, but also in all the practices in which the discipline has taken refuge, such as in processes of credibility of testimony in any type of crime, or in the assessment of emotional coherence or congruence. [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.

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.

A reliable lie-detection method would be extremely useful in many situations but especially in forensic contexts. This review describes and evaluates the range of methods that have been studied. Humans are barely able to pick up lies on the basis of nonverbal cues; they do so more successfully with systematic methodologies that analyze verbal cues and with physiological and neuroscientific methods. However, the rates at which people are able to detect lies are still well below the legal standard of "beyond a reasonable doubt." This means that the utmost caution must be exercised when such methods are employed. In investigations where independent evidence exists, there is emerging evidence that interviews based on a free account followed by the gradual introduction of the evidence by investigators can reveal inconsistencies in a guilty interviewee's account. Automated machine-learning methods also hold some promise. [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]

Traditional polygraph techniques mostly rely on the changes of an individual’s physiological indicators, such as electrodermal activity, heart rate, breath, eye movement and function of neural signals and other indicators. They are easily affected by individual physical conditions, counter-tests, external environment and other aspects, and it is difficult to conduct large-scale screening tests based on the traditional polygraph techniques. The application of keystroke dynamics to polygraph can overcome the shortcomings of the traditional polygraph techniques to a large extend, increase the reliability of polygraph results and promote the validity of legal evidence of polygraph results in forensic practice. This paper introduces keystroke dynamics and its application in deception research. Compared with the traditional polygraph techniques, keystroke dynamics can be used with a relatively wider application range, not only for deception research but also for identity identification, network screening and other large-scale tests. At the same time, the development direction of keystroke dynamics in the field of polygraph is prospected.

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]