Deceptive Behavior Research Articles

Domain‐general or specific: How is children's understanding of deception socialized?

AbstractThe current study investigated parenting influences on children's understanding of lie‐telling in eight different social situations. These social situations clustered into two broad categories that have been assumed in the literature: first, self‐oriented lies that were generally told to benefit the self (e.g., to avoid punishment or gain status); and second, socio‐culturally‐oriented lies that were told for more social reasons (e.g., to create positive affect, maintain modesty or politeness). Two types of parenting variables were also measured. The first concerned more general parenting practices and have been studied in the literature; while the second was about more specific parental deceptive behaviors like lying to your child for their compliance. Participants included 141 Turkish parent‐child dyads aged 7, 9, and 11. All children judged the self‐oriented and the socio‐cultural lies as inappropriate but more so for the self‐oriented ones; further, the socio‐cultural lies were judged less negatively with age. While general parenting practices did not predict children's judgments, for parental deceptive practices, there were strong negative relationships between parents’ use of threatening lies (e.g., “come with me or I'll leave you here”) and children's judgments for both self‐ and socio‐cultural lie types. The two main conclusions are that specific parental deceptive practices are more relevant than general parenting for understanding children's judgments about lie‐telling situations. Second, the assumed categorization of lies into “self‐” and “social‐” seems generally valid when using a plurality of situations. Finally, some issues related to the morality of lie‐telling are discussed.

Socio-cognitive correlates of primary school children's deceptive behavior toward peers in competitive settings

Competing for limited resources with peers is common among children from an early age, illustrating their propensity to use deceptive strategies to win. We focused on how primary school-age (6–8 years old) children's strategic deception toward peers is associated with their socio-cognitive development (theory of mind and executive functions). In a novel computerized competitive hide-and-seek game, we manipulated the peer opponents' familiarity (familiar vs. unfamiliar) and actions (following vs. not following children's indications), as well as the stimuli likability (liked vs. disliked cards). Our findings demonstrated that children deceived the familiar opponent less than the unfamiliar one, indicating their determination to preserve positive peer interactions. We showed that theory of mind and executive functions significantly predicted children's willingness to deceive. Notably, second-order false belief understanding and visuospatial working memory positively predicted children's use of truths to deceive, whereas inhibitory control and cognitive flexibility efficacy scores were negatively related to their deceptive performance when using the same strategy. Implications for children's competitive behavior toward peers involving lie-telling are discussed.

Did You Commit a Crime There? Investigating the Visual Exploration Patterns of Guilty, Innocent, Honest, and Dishonest Subjects When Viewing a Complex Mock Crime Scene

Previous studies with the eye-tracking technology have predominantly tracked eye parameters in response to a single simple stimulus, and have generated interesting - sometimes inconsistent - results in research on deceptive behavior. The present study analyzed visual patterns in response to a complex image, to investigate potential differences in eye fixation between guilty versus innocent, and honest versus dishonest participants.One hundred and sixty participants were assigned to one of four experimental groups, defined by the parameters of honesty (dishonesty) and guilt (innocence), and asked to complete a computer-based task, looking at neutral and target images (i.e., images of the mock crime scene). RealEye software was used to capture participants’ eye movements when viewing the images.The findings revealed significant differences in eye movements between the four experimental groups in the pictures in which the area where the crime took place was clearly visible. Dishonest and guilty participants recorded fewer and shorter fixations in the area of the image where the crime took place than those who entered the crime scene but did not commit the crime. No differences between groups emerged in the visual patterns in response to neutral images, confirming that the number and duration of fixations in response to the target images may be attributed to the experimental condition.

A Hybrid Approach Adopted for Credit Card Fraud Detection Based on Deep Neural Networks and Attention Mechanism

Over the past few years, credit card fraud has become a serious problem as more individuals rely on credit cards for purchases. The significant increase in fraudulent activities can be attributed to advancements in technology and the prevalence of online transactions, leading to significant financial losses. To address this issue, an effective fraud detection system needs to be developed and put into practice. Machine learning techniques are commonly used to automatically detect credit card fraud, but they do not consider deceptive behaviour or behavioural issues that could lead to false alarms. The objective of this research is to determine how to identify instances of credit card fraud. This paper aimed to create a model using deep learning and SMOTE oversampling technique to anticipate credit card fraud. A Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) and an attention mechanism is suggested for detecting fraud. This model is known to be effective for processing sequential data with complex relationships between vectors. The performance of RNN-LSTM is compared to XGBoost, Random Forest, Naive Bayes, SVM, and ANN classifiers, and the experiments indicate that our proposed model achieves high accuracy of 99.4% and produces strong results. The suggested model has the potential to decrease financial losses worldwide by identifying instances of credit card scams or frauds.

Detecting Deceptive Behaviours through Facial Cues from Videos: A Systematic Review

Interest in detecting deceptive behaviours by various application fields, such as security systems, political debates, advanced intelligent user interfaces, etc., makes automatic deception detection an active research topic. This interest has stimulated the development of many deception-detection methods in the literature in recent years. This work systematically reviews the literature focused on facial cues of deception. The most relevant methods applied in the literature of the last decade have been surveyed and classified according to the main steps of the facial-deception-detection process (video pre-processing, facial feature extraction, and decision making). Moreover, datasets used for the evaluation and future research directions have also been analysed.

Ownership of the affected leg is further reduced following deceptive behaviors in body integrity dysphoria

Although predicted by the notion of embodied morality, it remains unknown whether a reduced sense of body ownership (SoO) is associated with increased or decreased dishonesty. To clarify this issue, we tested patients with body integrity dysphoria (BID), a clinical condition characterized by chronic reductions of SoO toward one leg that patients persistently desire to have amputated. Participants with BID played a card game in which they could voluntarily tell the truth or cheat an opponent, and thus either steal or give them money. To assess whether SoO toward the effector limb influences (im)moral decisions, responses were communicated with the affected or the unaffected leg. We found that a higher number of self-gain lies was followed by further reductions of SoO toward the affected leg. Our result supports the idea that reductions of SoO may follow immoral behaviors to distance from unwanted characteristics of the self, like one's own dishonesty.

Fenomena Perilaku Moral Siswa Selama Daring di SMA Negeri 2 Solok Selatan

This article aims to elucidate the various forms of student moral behavior observed during online learning, as well as to identify the underlying factors influencing such behavior both in the context of online learning and during the transition back to traditional classroom settings. The study explores diverse manifestations of student moral conduct, encompassing levels of honesty, discipline, and politeness. Employing a qualitative research design with a descriptive approach, data was collected at SMA N 2 Solok Selatan using purposive sampling for informant selection. The data gathering techniques encompassed interviews, documentation studies, and post-observation analysis. To ensure data validity, triangulation of sources was performed, and data was subsequently analyzed through collection, reduction, presentation, and conclusion stages. Findings revealed instances of student dishonesty during online learning, including plagiarism in friend's assignments, copying answers from the internet, and deceptive behavior towards parents. Additionally, students exhibited reduced politeness in their communication with teachers. The research also highlighted influential factors contributing to student moral behavior, attributing challenges in adjusting to the online learning system to both parents and teachers. The impact of this adaptation was observed in students' moral conduct during the transition back to conventional learning, manifesting as difficulty in completing assignments and a lack of self-confidence in offline learning settings.

Competition and the Regulation of Fictitious Pricing

Fifty years ago, the Federal Trade Commission stopped enforcing its fictitious reference pricing guidelines, emphasizing the search qualities of price and the belief that competition would drive out deceptive behavior. Yet this practice of posting false, inflated comparison prices alongside sale prices has proliferated. Building on prior analytic work and the documented effects of reference pricing on search behavior and consumer choice, the authors develop a descriptive model explaining why fictitious reference pricing has spread instead of being extinguished by competition. After summarizing the model with a series of generalizations, the authors consider potential regulatory solutions to the problem. They propose that disclosure of the true normal price charged may be the only solution that could plausibly influence both consumer and firm behavior. Then, based on the existing literature, interviews with practitioners and regulators, an analysis of other nations’ approaches, and the results of a laboratory study of 900 consumers to examine how they integrate “true normal price” information into deal evaluations, the authors develop a set of propositions about the likely effects of requiring firms to disclose recent selling prices for goods, identifying both potential benefits and challenges associated with such a requirement.

An Identity Recognition Model Based on RF-RFE: Utilizing Eye-Movement Data.

Can eyes tell the truth? Can the analysis of human eye-movement data reveal psychological activities and uncover hidden information? Lying is a prevalent phenomenon in human society, but research has shown that people's accuracy in identifying deceptive behavior is not significantly higher than chance-level probability. In this paper, simulated crime experiments were carried out to extract the eye-movement features of 83 participants while viewing crime-related pictures using an eye tracker, and the importance of eye-movement features through interpretable machine learning was analyzed. In the experiment, the participants were independently selected into three groups: innocent group, informed group, and crime group. In the test, the eye tracker was used to extract a total of five categories of eye-movement indexes within the area of interest (AOI), including the fixation time, fixation count, pupil diameter, saccade frequency, and blink frequency, and the differences in these indexes were analyzed. Building upon interpretable learning algorithms, further investigation was conducted to assess the contribution of these metrics. As a result, the RF-RFE suspect identification model was constructed, achieving a maximum accuracy rate of 91.7%. The experimental results further support the feasibility of utilizing eye-movement features to reveal inner psychological activities.

Psychometric Properties of the Revised Trolling Questionnaire in Argentinean Adults

Trolling with new technologies is a disruptive, deceptive, and destructive behavior, with great psychosocial, legal, and economical relevance. However, there are no studies in the scientific literature that have examined the properties of questionnaires to assess them. Therefore, the purpose of the present study was to observe the psychometric properties of the Revised Trolling Questionnaire by Buckels et al.’s in a sample of Argentinean adults. An intentional sample of 837 participants from Argentina (mean age = 28.4 years; 61% female) was formed. They completed the Buckels et al.’s Revised Trolling Questionnaire, measures of dark personality, Internet addiction, cyberstalking, and socio-demographic questions. Exploratory and confirmatory factorial analyses indicated a one-factor structure with correct fit statistics. Internal consistency was adequate: Cronbach’s α = .80 and Omega coefficient (ω) = .81. Concurrent validity of the trolling questionnaire was observed with dark personality (Machiavellianism, narcissism and psychopathy), problematic Internet use (symptomatology and disfunctionality), and cyberstalking. Machiavellianism and disfunctionality were the most significant variables associated with trolling. Therefore, the Argentinean Spanish version of this test showed evidence of adequate psychometric properties.

Integrating MLOps and EEG Techniques for Enhanced Crime Detection and Prevention

The use of machine learning and artificial intelligence in crime detection has gained significant attention in recent years. This research paper explores the potential of MLOps techniques in identifying criminal behavior through the analysis of vehicle plate numbers, person detection, and object behavior detection. In this paper, we present a literature review of studies that have investigated the use of MLOps in crime detection and highlight the potential of these techniques to be used in criminal investigations. Specifically, we discuss the use of machine learning models for vehicle plate number recognition, person detection through image and video analysis, and object behavior detection through image and video analysis. We also present a framework for integrating MLOps techniques into criminal investigations, which involves a combination of data acquisition, data preprocessing, model development, model training and testing, and deployment. Additionally, we discuss the ethical implications of using MLOps techniques in criminal investigations and highlight the need for transparency and fairness in model development and deployment. The detection and prevention of criminal behavior is a critical issue for society. In recent years, there has been growing interest in the use of electroencephalogram (EEG) techniques to detect and predict criminal behavior. This research paper explores the potential of EEG techniques as a means of detecting crime by analyzing brainwave activity. Specifically, we examine the use of alpha and beta waves in identifying deceptive or abnormal behavior. We present a literature review of studies that have investigated the relationship between EEG signals and criminal behavior and highlight the potential of these techniques to be used in criminal investigations. Furthermore, we propose a framework for integrating EEG techniques into criminal investigations, which involves a combination of data acquisition, analysis, and interpretation. Finally, we discuss the ethical implications of using EEG techniques in criminal investigations and highlight the need for further research in this area.

World wide web of lies: Personality and online deception

The deliberate act of falsifying aspects of a person's identity is rampant online and across social networking sites. Although personality traits have been found to be related to deception in traditional contexts, less is known about how this relationship translates to an online setting. The current study investigates the relationships between the dark tetrad traits (Machiavellianism, psychopathy, narcissism, and sadism), self-esteem, and online identity-based deception. A combined student and community sample (N = 342, 18 – 72 years old, 70.2% female) was asked if they had previously engaged in eight common identity-based deceptive behaviours on social networking sites (e.g., used a fake name, created a fake profile). Personality traits were measured using the Short Dark Triad, the Short Sadistic Impulse Scale, and the Rosenburg Self-Esteem Scale. Overall, engagement in identity-based deception was found to be common on social networking sites. Machiavellianism emerged as a unique predictor of using a fake name and altering one's age with higher Machiavellianism also associated with a greater frequency of lying about one's name, age, appearance, and relationship status. Psychopathy, however, failed to uniquely predict any of the online-deceptive behaviours examined when controlling for other personality variables. Together, these findings suggest that online identity-based deception is a calculated endeavour.

Big-Five Personality: Prediction of Fear of Missing Out, Problematic Social Media Use, and Deceptive Behavior

Very rapid development in information technology, one of which lies in social networking media. The use of social media continued to increase from 2013 (42%) to 2015 (47%). The extensive use of social media causes adverse effects such as deceptive behavior (in social media), constant urge to go online, problematic social media use, and checking social media (fear of missing out). This study examines how Big-Five personality affects deceptive behavior, FoMO, and problematic social media use. The sampling technique used was convenience sampling, which 121 students in DKI Jakarta selected. Multiple linear regression and multivariate multiple regression were used to examine the data. As a result, there is a strong correlation between the Big-Five personality in predicting FoMO, deceptive behavior, and problematic social media use. This finding has implications for universities, especially for lecturers with a background in psychology, to direct and provide knowledge to students to avoid deceptive behavior, problematic social media use, and fear of missing out.

Examining the “normal” in Relation to Mental Illness: The Case of Jane Eyre

How do we understand what is normal social behavior outside the parameters that are used to define “mental illness”? Terminological clarity is important in ensuring that our perception of mental illness is not just a theoretical exercise but is invariably connected to our day-to-day lives. A person could appear completely normal while displaying abnormal traits in his or her character. Something like that does not necessarily make them social misfits because they could still be perceived as “normal” for all practical purposes. For an instance of the normalization of the abnormal, I selected the novel Jane Eyre. Far from being a simple, captivating romance, Jane Eyre is not just about a girl struggling for self-respect but also someone who is trapped in a struggle for “normalcy” in the face of potential mental illness. The latter part of my paper revolves around two aspects: one is that we cannot conceive of the Realist novel without seeing that the genre of the novel itself is a product of class and gender-based contradictions. Another factor, specific to Jane Eyre, is that in their desperation to be “normal,” the characters display morbid symptoms of a controlled and by extension deceptive behavior that would qualify for an argument that the novel is a study of mental illness, rather than a picture of a society at harmony with itself. The paper is a critique of normalcy in the face of the fear of falling mentally ill – a fear experienced by the narrator and communicated to the reader. In the process, the paper uses some of the literary aspects of the text to talk about characters in a manner that brings them as close as possible to reality. The characters are not just imaginary beings in the author’s mind, but individuals who could be situated in a social and a historical context. In attempting to examine mental illness in relation to Jane Eyre, the paper pays attention to some of the other characters in the novel such as Helen Burns, Brocklehurst, and St. John Rivers. The colonial dimension of mental illness in the colonizer’s world is also explored.

Trust and deception with high stakes: Evidence from the friend or foe dataset

Many social interactions rely on the premise of mutual trust, but deception violates trust and poses risk. Empirically examining trust and deception, particularly in high-stakes situations, is challenging but essential for improving the research realism and generalizability. To address this difficulty, we study trusting and deceptive behaviors in a high-stakes situation by using a novel dataset created from an American game show, Friend or Foe (FoF). In the show, a contestant's reward was determined through a trust game modified from the prisoner's dilemma. We explore how numerous human behaviors including facial expressions, gaze, head pose, body motion, language, and socio-demographic attributes, were related to a contestant's trusting or deceptive decision. Using a data-driven approach, we find that the deceivers' (contestants who chose Foe) behavior featured a neutralized face, negative facial emotions, enhanced upper body motion, and language with a lower sense of immediacy and agreeableness. The contestants who chose to trust (chose Friend) exhibited opposite behavioral patterns. Socio-demographic factors such as age, height, and facial attractiveness were also associated with a contestant's choice. Combining multimodal information, machine learning classifiers could predict the contestant's choice with an accuracy about 25% greater than earlier reported human accuracy. We contribute to both trust and deception literature by examining the generalizability of trusting and deceptive behaviors to a new high-stakes scenario. We also add to the decision support literature by showing the superior predictive performances of combining behavioral and socio-demographic features. Furthermore, we contribute to the academic community by introducing the FoF dataset.

Deceptive behaviour and coopetition: the role of heterogeneous absorptive capacities and product specialisation

PurposeThis paper aims to study the role of absorptive capacities in coopetitive alliances that involve leakages of sensitive private knowledge regarding firms’ production processes.Design/methodology/approachThis paper uses a game theoretic approach to model a differentiated product market in which two firms asymmetrically informed about the economic value of a business opportunity must cooperate to exploit this opportunity. Under coopetition, firms gain access to their partners’ core knowledge as the result of inevitable leakages of information. Firms differ in their absorptive capacities, which affects their abilities to leverage this new knowledge outside the collaborative activity.FindingsFirms with superior absorptive capacities are more likely to devise alliances whose purpose is to gain access to their partners’ core knowledge. This opportunistic behaviour does not disappear even if firms compensate their partners for the damages caused by this deceptive business practice. This paper also finds that a highly specialised product safeguards firms with limited absorptive capacities against these opportunistic behaviours.Originality/valueThis paper provides a theoretical analysis of the role that absorptive capacities and product specialisation play in influencing the emergence of opportunistic behaviours in coopetitive alliances. The theoretical analysis underscores the extent to which the risk of opportunism associated with the exploitation of a partner’s specific core knowledge outside the scope of the cooperative activity affects not only the nature and intensity of market competition but also the incentives to pursue coopetitive alliances.

Relationship between Secure Decision Points and Arbitrary Polygon Positions in Malicious Models

The problem of determining the position relationship between points and polygons is very extensive in practical applications. However, the existing solutions are only applicable to the case of convex polygons, and no solutions are given for arbitrary polygons. In addition, most of the existing schemes are carried out under the semi-honest model, and cannot resist the active attack of malicious adversaries. In order to solve these problems, this paper uses Paillier's encryption scheme, the idea of zero-knowledge proof to design a decision protocol to protect the position relationship between points and arbitrary polygons, which can not only prevent the deceptive behavior of malicious adversaries, but also extend the application from convex polygons to arbitrary polygons. A simulation example is used to prove that this paper is safe and efficient under the malicious model.

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

In real-time strategy (RTS) games, the ability of recognizing other players' goals is important for creating artifical intelligence (AI) players. However, most current goal recognition methods do not take the player's deceptive behavior into account which often occurs in RTS game scenarios, resulting in poor recognition results. In order to solve this problem, this paper proposes goal recognition for deceptive agent, which is an extended goal recognition method applying the deductive reason method (from general to special) to model the deceptive agent's behavioral strategy. First of all, the general deceptive behavior model is proposed to abstract features of deception, and then these features are applied to construct a behavior strategy that best matches the deceiver's historical behavior data by the inverse reinforcement learning (IRL) method. Final, to interfere with the deceptive behavior implementation, we construct a game model to describe the confrontation scenario and the most effective interference measures.

In the eye of the beholder: A configurational exploration of perceived deceptive supplier behavior in negotiations

AbstractDeceptive behavior in negotiations has been found to be widespread and to have harmful consequences. This study shifts the current research direction on deceptive negotiation behavior by adopting a target's perspective on deception and by using a configurational theorizing approach. Prior studies in supply chain management (SCM) and in other disciplines have studied deceptive negotiation behavior—as one specific form of opportunism—based on correlational approaches. In doing so, they have focused almost exclusively on the actor's (i.e., deceiver's) perspective—for example, investigating actors' motivations for using deception. As a result, a profound understanding of deceptive negotiation behavior from a target's perspective is lacking. In three studies, this research investigates what factors, on both the firm and individual levels, combine to lead purchasing managers (i.e., targets) to perceive supplier deception. The configurational analysis uncovers considerably more combinations of firm‐level and individual‐level factors that lead to perceptions of high supplier deception than combinations that lead to perceptions of low supplier deception. Thus, the contribution is twofold: First, the studies shift the perspective from the deception source to the deception target. Second, they uncover the causally complex nature of perceived deception in negotiations. Managerial implications include that purchasing managers, in their efforts to detect supplier deception, should move beyond paying attention to isolated factors, such as body language, and instead should focus on different combinations of power balances, negotiation stakes, and negotiator proficiencies.

Lightweight authentication scheme for massive MIMO on Internet of Things connectivity

Massive MIMO is an essential technology in developing 5G networks and a concept that may be applied to other wireless systems. However, the advantages of adopting massive MIMO for broadband communication are well-established. Recently researcher has been devoted to building communication systems sustaining high communication rates with security. While massive MIMO for Internet-of-Things (IoT) connectivity is still a developing issue, IoT connectivity has requirements and limitations that differ significantly from broadband connections. Although IoT makes people’s lives easier by allowing physical devices to flow through, the interaction of open wireless channels such as Bluetooth, ZigBee, LoRa, Narrowband-Internet of Things (NBIoT), and WiFi, has produced various security and privacy difficulties. Identity authentication is one of the effective solutions for addressing the Internet of Things security and privacy concerns. The typical point-to-point authentication technique ignores the internet of things’ massive number of nodes and limited node resources. Group authentication is an authentication method that simultaneously confirms the identity of a group of members, offering a novel approach to identity identification for the internet of things nodes. However, existing group authentication systems appropriate for the internet of things scenarios pose security issues. They cannot withstand malicious attacks such as forging and replay and cannot prevent group managers from fooling group members. Most existing group authentication schemes are computationally expensive and cannot be applied to resource-constrained IoT scenarios. At the same time, existing systems based on secret-sharing technology cannot resist forgery attacks and replay attacks. The attacker can forge a legal token by modifying the Lagrangian coefficient in the authentication token to pass the group authentication. This work employs verifiable secret sharing technology to create a lightweight verifiable group authentication method (L-IoT-GS) suitable for Internet of Things situations to resist group managers’ deceptive group behavior. Nodes in the Internet of Things scenario can frequently join and leave the network. Because of this, this article proposes a critical update link based on a verified group authentication system for updating group Member rights. According to security analysis, the suggested L-IoT-GS scheme meets accuracy and confidentiality requirements and can withstand malicious attacks such as replay, forgery, and impersonation. Furthermore, performance study and experimental simulation reveal that the L-IoT-GS technique minimizes group members’ computing costs compared to existing standard IoT group authentication schemes.