Telling lies: the irrepressible truth?

Online dating through lies: the effects of lie fabrication for personal semantic information on predicted and actual memory performance

Research shows that people lie on online dating sites often but might fail to remember this information subsequently. This study investigated participants' predicted and actual memory performance for personal semantic information after telling the truth versus a lie in two experiments in a setup similar to online dating sites. In Experiment 1, participants responded to open-ended questions either truthfully or fabricated lies in a within-subjects design, followed by predictions for remembering their responses. Subsequently, they recalled their responses through free-recall. Using the same design, Experiment 2 also manipulated the type of retrieval task by using a free- or cued-recall test. The results showed that participants consistently had higher memory predictions for truthful than deceptive responses. However, the actual memory performance did not always produce similar results to their predictions. The results suggest that the difficulties during lie fabrication, measured through response latencies, partially mediated the relationship between lying and memory predictions. The study has important applied implications for lying about personal semantic information in online dating contexts.

Detecting faking-good response style in personality questionnaires with four choice alternatives

Deliberate attempts to portray oneself in an unrealistic manner are commonly encountered in the administration of personality questionnaires. The main aim of the present study was to explore whether mouse tracking temporal indicators and machine learning models could improve the detection of subjects implementing a faking-good response style when answering personality inventories with four choice alternatives, with and without time pressure. A total of 120 volunteers were randomly assigned to one of four experimental groups and asked to respond to the Virtuous Responding (VR) validity scale of the PPI-R and the Positive Impression Management (PIM) validity scale of the PAI via a computer mouse. A mixed design was implemented, and predictive models were calculated. The results showed that, on the PIM scale, faking-good participants were significantly slower in responding than honest respondents. Relative to VR items, PIM items are shorter in length and feature no negations. Accordingly, the PIM scale was found to be more sensitive in distinguishing between honest and faking-good respondents, demonstrating high classification accuracy (80–83%).

False Identity Detection Using Complex Sentences

The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90–95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models.

The Detection of Malingering: A New Tool to Identify Made-Up Depression

Major depression is a high-prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double-choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine-learning algorithms, comprises 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine-learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks compared to both truth-tellers and liars. Machine-learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.

A reverse order interview does not aid deception detection regarding intentions

Promising recent research suggests that more cognitively demanding interviews improve deception detection accuracy. Would these cognitively demanding techniques work in the same way when discriminating between true and false future intentions? In Experiment 1 participants planned to complete a task, but instead were intercepted and interviewed about their intentions. Participants lied or told the truth, and were subjected to high (reverse order) or low (sequential order) cognitive load interviews. Third-party observers watched these interviews and indicated whether they thought the person was lying or telling the truth. Subjecting participants to a reverse compared to sequential interview increased the misidentification rate and the appearance of cognitive load in truth tellers. People lying about false intentions were not better identified. In Experiment 2, a second set of third-party observers rated behavioral cues. Consistent with Experiment 1, truth tellers, but not liars, exhibited more behaviors associated with lying and fewer behaviors associated with truth telling in the reverse than sequential interview. Together these results suggest that certain cognitively demanding interviews may be less useful when interviewing to detect false intentions. Explaining a true intention while under higher cognitive demand places truth tellers at risk of being misclassified. There may be such a thing as too much cognitive load induced by certain techniques

From junior to senior Pinocchio: A cross-sectional lifespan investigation of deception

We present the first study to map deception across the entire lifespan. Specifically, we investigated age-related difference in lying proficiency and lying frequency. A large community sample (n = 1005) aged between 6 and 77 were surveyed on their lying frequency, and performed a reaction-time (RT) based deception task to assess their lying proficiency. Consistent with the inverted U-shaped pattern of age-related changes in inhibitory control that we observed in a stop signal task, we found that lying proficiency improved during childhood (in accuracy, not RTs), excelled in young adulthood (in accuracy and RTs), and worsened throughout adulthood (in accuracy and RTs). Likewise, lying frequency increased in childhood, peaked in adolescence, and decreased during adulthood. In sum, we observed important age-related difference in deception that generally fit with the U-shaped pattern of age-related changes observed in inhibitory control. Theoretical and practical implications are discussed from a cognitive view of deception.

Mentira: Aspectos Sociais e Neurobiológicos

RESUMONo contexto da interação interpessoal, no qual são utilizados recursos comportamentais como gestos, expressões faciais, postura corporal e modulação de voz, destaca-se o fenômeno da mentira, que é caracterizada pela dissimulação de ideias, sentimentos e emoções. No presente trabalho foi realizado um levantamento teórico acerca da mentira, sendo descritos alguns de seus aspectos comportamentais e neurobiológicos e também analisadas as publicações relacionadas ao tema no Brasil. As implicações do uso de técnicas de avaliação da mentira no âmbito judicial, bem como as crenças infundadas utilizadas em julgamentos são discutidas. A revisão identificou poucos estudos sobre a temática no contexto brasileiro, o que indica a necessidade de ampliação desse campo de investigação no país.