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This study applied a countermeasure-resistant version of the Concealed Information Test - the Complex Trial Protocol (CTP) - in an information recognition scenario. We replicated and extended the effects of a novel countermeasure developed by Lukács et al., (2016) on both Semantic and Episodic CTPs. We measured participants' response time and P300 event-related potential to rare, crime-relevant probe stimuli, or frequent, non-crime-relevant irrelevant stimuli in two ways: 1) probe vs the average of all irrelevants (PvIall), and 2) probe vs the maximum irrelevant (PvImax). We hypothesized that countermeasure use would only impair information recognition (as indexed by P300) when participants had practiced the countermeasure beforehand. We further hypothesized that recognition of less salient, Episodic information (i.e., jewelry items from a mock crime) would be impaired by countermeasure use more than the recognition of highly salient, Semantic information (i.e., birthdates). Individual diagnostics based on the area under the receiver operating characteristic curve (Semantic CTP: practice n = 22, non-practice n = 23; Episodic CTP: practice n = 19, non-practice n = 18) revealed that the Semantic CTP was affected by the novel countermeasure, but both PvIall and PvImax analyses remained diagnostically useful. The Episodic CTP's performance, however, was reduced to chance, regardless of practice or analysis type. These results are important for both the field of deception detection and the CTP literature. Research on improvements to the Episodic CTP is required.

In the Concealed Information Test (CIT), differential responses between crime-relevant and crime-irrelevant items are indicative of concealed knowledge of a crime, and are used to classify an individual as either "guilty" or "innocent". However, when crime-relevant items are leaked before the test, an innocent examinee can exhibit enhanced responses to the crime-relevant items, thus causing such examinee to be wrongly classified as guilty. In an attempt to solve this problem, we examined the role of retroactive memory interference (RI) in differentiating informed innocents from guilty participants, using a P300-based CIT. Participants acquired crime-related knowledge either by committing a mock crime (guilty group) or reading a paper that described a mock crime (informed innocent group). Subsequently, the participants within each condition were randomly assigned to either an RI group, where they were exposed to new crime-related details before the CIT, or a control group. We found an interaction between guilty and RI groups: in the guilty group, there was a significant difference in P300 amplitude between the probe and irrelevant items, regardless of RI manipulation, whereas in the informed innocent group, a difference in P300 amplitude between the probe and irrelevant items was significant only in the control group, but not in the RI group. This led to an improved detection rate of the informed innocents (31% for the control group vs. 77% for the RI group). These results suggest that RI manipulation could be used to reduce the false positive outcomes of informed innocents without affecting the detection rate of guilty participants.

Steganographer detection aims to identify guilty users who conceal secret information in a number of images for the purpose of covert communication in social networks. Existing steganographer detection methods focus on designing discriminative features but do not explore relationship between image features or effectively represent users based on features. In these methods, each image is recognized as an equivalent, and each user is regarded as the distribution of all images shared by the corresponding user. However, the nuances of guilty users and innocent users are difficult to recognize with this flattened method. In this paper, the steganographer detection task is formulated as a multiple-instance learning problem in which each user is considered to be a bag, and the shared images are multiple instances in the bag. Specifically, we propose a similarity accumulation graph convolutional network to represent each user as a complete weighted graph, in which each node corresponds to features extracted from an image and the weight of an edge is the similarity between each pair of images. The constructed unit in the network can take advantage of the relationships between instances so that common patterns of positive instances can be enhanced via similarity accumulations. Instead of operating on a fixed original graph, we propose a novel strategy for reconstructing and pooling graphs based on node features to iteratively operate multiple convolutions. This strategy can effectively address oversmoothing problems that render nodes indistinguishable although they share different instance-level labels. Compared with the state-of-the-art method and other representative graph-based models, the proposed framework demonstrates its effectiveness and reliability ability across image domains, even in the context of large-scale social media scenarios. Moreover, the experimental results also indicate that the proposed network can be generalized to other multiple-instance learning problems.

In the pursuit of new methods for concealed memory detection, event-related potential components (ERP) have been placed at the forefront of research. No method, however, is scientifically complete without a theory and the present study therefore aimed to unravel the cognitive processes underlying these ERPs (i.e., orienting and arousal inhibition). This was accomplished by using a Concealed Information Test (CIT) in which participants were once motivated to conceal and once motivated to reveal their identity. The results showed a similarly strong P3 CIT effect in the two motivational conditions, which was enhanced for high salience compared to low salience identity items. Similar results were observed when using a multivariate machine-learning algorithm - suggesting that brain-based concealed memory detection is driven mainly by orientation to salient stimuli, rather than by arousal inhibition. In addition, the algorithm, trained and tested on the ERPs of different identity items, achieved detection rates exceeding those achieved by the P3. This implies that CIT researchers and practitioners could potentially rely on the entire ERP waveform instead of a-priori selecting separate components. Together these results enrich current understanding of the mechanisms underlying neurophysiological responding to concealed information and pave the way for novel and powerful algorithms which could be used in real-life forensic investigations.

The reaction time-based concealed information test (RT CIT) typically uses three types of stimuli: 1) probe (i.e., crime-related item), 2) irrelevant (i.e., crime-unrelated item) and 3) target (i.e., an irrelevant item assigned a unique response so as to ensure attention to the test). Lukacs, et al., (2017) introduced familiarity-related filler items to the RT CIT, enhancing the efficacy of the test for both single and multiple probe (per block) protocols. Our study aims to A) replicate the effects of familiarity-related filler items on the RT CIT, B) use P300 to investigate the mechanisms of the fillers-related enhancement effect on the RT CIT, C) investigate whether these fillers can enhance the efficacy of the typical P300-based CIT, and D) explore the effects of fillers on the semantic and episodic versions of the P300 and RT CITs. We replicated a clear fillers-related enhancement of the RT CIT (N = 38 for Fillers, N = 37 for No Fillers). Semantic and Episodic experiments were enhanced equally-well by filler items. No effect of fillers was observed on P300 amplitude for CIT stimuli, however, an interaction involving P300 latency suggests that fillers facilitated the processing of Semantic information over Episodic information. Our study independently replicates a valuable improvement of the traditional RT CIT, investigates the potential mechanisms of this enhancement effect, and demonstrates important differences between the P300 and RT CITs.

The feedback concealed information test (fCIT) is a novel variant of a CIT, which provides participants with feedback regarding their memory concealment performance during the CIT. Previous studies have found that feedback-related ERPs associated with feedback processing in the fCIT can accurately identify concealed information. However, it is still unknown whether the feedback influences the process and ERP signs of recognition during memory concealment, such as the recognition P300. To address this issue, the present study directly compared the recognition-based P300 in CITs with and without feedback. Results showed that the probe elicited a larger recognition P300 than irrelevants in both CITs with and without feedback, and there were no significant differences in recognition P300 between those two CITs. Moreover, the detection rate for recognition-based P300 in both CITs also had no significant difference. The feedback-related ERPs, especially feedback P300, continued to discriminate guilty and innocent subjects with AUCs well above the chance.

We investigate the effects of feedback on the decision to lie in a sender-receiver deception game with imperfect lie detection. We find evidence of feedback effects through two channels. First, the mere expectation of receiving feedback, including the anticipation of positive feedback and the threat of negative feedback, reduces lying. Second, actually-received feedback affects the subsequent decision to lie, but only in one situation: honest-type people who are being falsely punished with negative feedback become three times as likely to lie as those who are correctly rewarded with positive feedback. Our results indicate that the anticipation effect is the primary deterrent of lying, rather than the experience of receiving negative or positive feedback. Feedback may backfire and should be used with caution: honest-type individuals who are condemned as liars are surprised and react with moral indignation.

Some studies have shown a superiority of visual vs. auditory item presentation in the Complex Trial Protocol (CTP), which is a countermeasure-resistant version of the P300-based Concealed Information Test (CIT). But those studies used elaborately- rehearsed autobiographical information as stimuli, instead of incidentally-acquired crime-related information. Thus, the relative superiority of the visual as opposed to the auditory modality in detecting episodic crime-related information is still unknown. The present study also improved on the usual mock crime scenario by adding a mock disposal task between a mock theft and administration of a CTP test to increase stimulus saliency. In this CTP, the probe and the irrelevant items were presented visually or acoustically on alternating trials, while target and non-target stimuli were simultaneously presented in visual and auditory modalities. The results showed that the P300 amplitude differences of probe minus irrelevant items presented in the visual modality were significantly larger compared to the auditory modality, and the detection rate of the guilty participants was also significantly higher for the visual (14/16) versus auditory modality (5/16). These results suggest a superiority of visual vs. auditory presentation when a CTP is used to detect crime-related information in a mock crime scenario.

We study how well individuals in a face-to-face situation can delude others. We exploit data from a laboratory experiment in which participants were asked to assess video-taped statements as being rather truthful or untruthful. The statements are face-to-face tax declarations. The video clips feature each subject twice making the same declaration: One time the subject is reporting honestly, and the other time willingly dishonestly. This allows us to investigate within-subject differences in perceived truthfulness. Our study provides several novel insights. We find that individuals can cover up their lies successfully. On average, a subject is perceived as more truthful if she deceives than if she reports honestly. In particular, individuals with a genuine dishonest appearance manage to increase their perceived truthfulness by up to 14 percent when lying. Moreover, our results show that a subject appears less truthful if she had previously been caught lying. Being detected as a liar previously appears to impair self-confidence and to thereby lower an individual’s ability to deceive.

Being able to identify if someone is telling the truth or lying is essential in many social situations, for instance in police interrogations or employment interviews. Unfortunately, people are generally poor at lie detection. Some researchers have argued that a small category of individuals are detection wizards who can achieve substantially higher detection accuracy because they have high levels of emotional intelligence (EI) and are better able to identify non-verbal cues to deceit. These propositions have been popularized in the media and are appealing to some practitioners, but are based on very limited empirical evidence. We conducted three experimental studies to test these propositions, relying on different samples and using both trait and ability measures of EI. We measured deception detection using different approaches (in-person and video-based) and contexts (social interaction and job interview). One study measured skepticism, and another used eye-tracking technology to capture participants' reliance on non-verbal information. Results showed that high-EI individuals indeed rely more on non-verbal information. However, EI, skepticism, and the use of non-verbal cues are unrelated to deception detection. We thus argue that detection wizards are likely a myth, and it would be more productive to focus on evidence-based methods to improve deception detection.

The validity of the Concealed Information Test (CIT) to detect recognition of critical details has been demonstrated in hundreds of laboratory studies. These studies, however, lack the factor of deliberate intent to deceive. This disparity between research and practice may affect the generalizability of laboratory based CIT findings.In the current study, 65 out of 174 participants cheated on their own initiative in a trivia quiz. These self-initiated cheaters were compared to 68 participants who were explicitly requested to cheat. Skin conductance, heart rate, and respiration were found to detect concealed information related to cheating. No significant differences emerged between self-initiated and instructed cheaters, supported by Bayesian statistics showing substantial evidence for the null hypothesis. The data demonstrate that the validity of the CIT is not restricted to instructed deception. This finding is encouraging from an ecological validity perspective and may pave the way for further field implementation of memory detection.

Eye tracking has for decades been a powerful tool of inspection for scientists and engineers. Thanks to the development of cheap and compact hardware devices, eye tracking applications are being adopted in many fields, from military to marketing. In recent years, gaze-based biometrics has proven to be a noteworthy alternative technology for authentication, surveillance and so on, although there are still efficiency and privacy issues to be addressed. An emerging application of gaze-based biometrics is in the forensic field. This paper provides an introduction and an extended bibliography to such applications, and briefly presents some relevant case studies about the so-called weapon focus effect, identification in a criminal lineup, and lie detection using eye tracking.

When embedded among a number of plausible irrelevant options, the presentation of critical (e.g., crime-related or autobiographical) information is associated with a marked increase in response time (RT). This RT effect crucially depends on the inclusion of a target/non-target discrimination task with targets being a dedicated set of items that require a unique response (press YES; for all other items press NO). Targets may be essential because they share a feature - familiarity - with the critical items. Whereas irrelevant items have not been encountered before, critical items are known from the event or the facts of the investigation. Target items are usually learned before the test, and thereby made familiar to the participants. Hence, familiarity-based responding needs to be inhibited on the critical items and may therefore explain the RT increase on the critical items. This leads to the hypothesis that the more participants rely on familiarity, the more pronounced the RT increase on critical items may be. We explored two ways to increase familiarity-based responding: (1) Increasing the number of different target items, and (2) using familiar targets. In two web-based studies (n = 357 and n = 499), both the number of different targets and the use of familiar targets facilitated concealed information detection. The effect of the number of different targets was small yet consistent across both studies, the effect of target familiarity was large in both studies. Our results support the role of familiarity-based responding in the Concealed Information Test and point to ways on how to improve validity of the Concealed Information Test.

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

The Concealed Information Test (CIT) aims to detect the presence of crime-related information in memory. In two experiments, we examined the influence of stimulus emotionality on the outcomes of the CIT. In experiment 1, each participant was tested immediately or after one week, on a series of neutral and either negative arousing or negative non-arousing pictures. CIT detection efficiency was unaffected, but physiological and recognition data did not support the manipulation's effectiveness. In experiment 2, each participant was tested after a week on a series of neutral versus negative arousing pictures. Importantly, stimulus arousal was increased and memory ceiling effects were prevented. This time, both memory and CIT detection efficiency using the skin conductance, but not the respiration and heart rate measures, were enhanced for emotional compared to neutral pictures. Taken together, these results indicate that the use of emotional stimuli does not deteriorate and may even improve CIT validity.

The literature on language evolution treats the fact that language allows for lying as a major obstacle to the emergence and development of language, and thus looks for theoretical means to constrain the lie. In this paper, I claim that this general formulation of the issue at hand misses out on the fact that lying made an enormous contribution to the evolution of language. Without the lie, language would not be as complex as it is, linguistic communication would be much simpler, the cognitive requirement of language would not be so heavy, and its role in society would be radically different. The argument is based on Dor's (2015) theory of language as a social communication technology, collectively-designed for the instruction of imagination. The theory re-thinks the essence of lying, and suggests that the emergence of language did more to enhance the human capacity for deception than it did to enhance the human capacity for honest communication. Lying, then, could not be constrained, but language did not collapse. The conception of lying as a threat to language, as it is formulated in the literature, is based on a series of unrealistic assumptions. Most importantly, the cognitive, emotional and social capacities required for lying, lie-detection and moral enforcement are never equally spread within communities: they are highly variable. Lying and language came to be entangled in a never-ending co-evolutionary spiral, which changed the map of communicative relationships within communities, and participated in shaping our languages, societies, cognitions and emotions. We evolved for lying, and because of lying, just as much as we evolved for and because of honest communication.

Throughout history, there has always been a need to find out whether people are telling the truth. Classical deception detection methods, such as polygraph-based techniques, have so far failed to accurately and reliably detect deception, as they are limited in various aspects. Therefore, results are susceptible to manipulation. In the current study, we attempt to improve lie detection using a classical conditioning procedure with startle response as an outcome variable. Thirty-six participants were asked to report true and false sentences (10 each) before the test procedure. We knew the truth value of only 50% of the sentences (the value of the other 50% was revealed after the experiment was over). Aversive conditioning was used, i.e. participants were presented with the unconditioned stimulus (air blast of 5 bar, 50 ms; contingency of 75%) when uttering known lies. There was a significant difference in participants’ startle reaction to false statements compared to truths, both in the known lies category and, more importantly, in the unknown lies category. We recommend further investigation of this phenomenon by changing the conditioning parameters (duration, contingency) in order to optimize this promising method and achieve a higher level of accuracy.

People display facial expressions of fear to communicate danger to others and sometimes to exaggerate danger to manipulate an audience. Here we test whether fear expressions add credibility to a speaker's warnings of danger. Participants played an incentivized lie detection game in which they guess whether a confederate partner is lying or telling the truth. Participants viewed a video of their partner's message, after reading that there was a good chance (75%) their partner was instructed to lie. We manipulated across conditions whether the partner stated the message with a neutral or fearful expression. Experiment 1 finds that participants were more likely to believe the speaker's warning of danger when it was conveyed with a fear expression compared to a neutral expression. Experiment 2 finds that when a speaker instead claimed that a danger was absent, a fearful expression no longer added credibility to their message. These findings provide evidence that fear expressions add credibility to statements of danger, specifically, rather than any claim.

We consider a model of Bayesian persuasion in which the Receiver can detect lies with positive probability. We show that the Sender lies more when the lie detection probability increases. As long as the lie detection probability is sufficiently small the Sender's and the Receiver's equilibrium payoffs are unaffected by the lie detection technology because the Sender simply compensates by lying more. When the lie detection probability is sufficiently high, the Sender's (Receiver's) equilibrium payoff decreases (increases) with the lie detection probability.

Emotional intelligence is an extremely valued aspect of intelligence therefore; machine intelligence needs to encompass emotional intelligence. Intensive research has been carried out in this field to enable machines to understand emotional states of human. Non-verbal communication is major mode of expression among humans and it constitutes of gestures, facial expressions etc. Facial expressions can be deceptive and a person might fake his/her emotional state by the usage of facial expressions. Physiological signals can be used in conjunction with facial expressions to detect a behavioral lie. It is of prime importance to automate this task. In this paper, we survey various techniques which make use of machine learning and physiological signals for detection of behavior lie. Identification of robust physiological signals yielding accurate results which aid in in the identification of emotional state of an individual. Survey also aims at identification of combination of the type of physiological signal and the classification algorithm used for classification of the emotion.

The Verifiability Approach is a lie detection method based on the insight that truth-tellers provide precise details whereas liars sometimes remain vague to avoid being exposed. We provide a-game-theoretic analysis of a speaker who wants to be acquitted and an investigator who prefers to find out the truth. The investigator can verify the speaker’s statement at some cost; verification gets more reliable the more details are provided. If, after a falsified statement, the investigator convicts, an additional obstruction penalty is imposed. We derive all the equilibria of the game and thereby the conditions under which the investigator can infer additional information from the speaker's statement at face value. Strategic information revelation by the speaker and verification by the investigator then necessarily work in tandem. Improvements in reliability result in more valuable (strategic) information transmission, whereas a harsher obstruction penalty does not as soon as a lower limit is met.

A pupillometry is a device that measures diameter of pupil. Measuring diameter can be done for different reasons. However, if person is not fixed to the pupillometry, diameter value could vary depending on distance between subject and device. To overcome this problem radius of iris was taken as reference to pupil as output will be ratio of pupil diameter to iris diameter. In this paper one of cues of deception, pupil dilation was studied, the dilated and non-dilated iris images were image pre-processed and segmentation of pupil and iris was achieved. Images were chosen from MMU Iris database and images with big pupils are assumed to be dilated pupils that are indication of lying and small ones were assumed to be neutral. After pre-processing that follows segmentation of pupil and iris, pupil to iris ratios as 60 samples of 1 element with 10 hidden neurons were fed into neural network for classification by neural network pattern recognition tool. All images were correctly classified.

The tipping point framework of lie detection posits that people can, and do, accurately detect deception. This framework pinpoints three circumstances that aid accuracy: (i) using methods of measurement that circumvent controlled, conscious cognition; (ii) when individual differences or situational factors portend potent risks to lie detection failure, such as in high-stakes or threatening settings; and (iii) when factors diminish concern over the relationship or reputation costs of asserting that someone has lied. We thus depict a psychological system that registers lie detection consistently in nonconscious reactions (e.g., brain based, bodily, indirect social evaluations) and that allows information into consciousness to inform overt assessments of lies when the costs of failing to detect deception exceed those of signaling distrust.

Successful use of wavelet packet analysis to extract P300 from ERP.We propose two new methods for ERP feature extraction and classification.Bootstrapped geometric difference analysis is useful for lie detection.Functional network analysis is first used in lie detection system.Functional brain network in guilty group shows enhanced small world property. Deception is a complex cognition process which involves activities in different brain regions. However, most of the ERP based lie detection systems focus on the features of ERPs from few channels. In this study, we designed a multi-channel ERP based brain computer interface (BCI) system for lie detection. Based on this, two new EEG feature selection approaches, bootstrapped geometric difference (BGD) and network analysis were proposed and applied to feature recognition and classification system. Unlike other methods, our approaches focus on the changes of EEGs from different brain regions and the correlation between them. For the test, we focus on visual and auditory stimuli, two groups of subjects went through the test and their EEGs were recorded. For all subjects, BGD of the P300 for all the scalp electrodes combined with SVM classifier showed the average rate of recognition accuracy was 84.4% and 82.2% for visual and auditory modality respectively. Statistical analysis of network features indicated the difference in the two groups were significant and the average accuracy rate reached 88.7% and 83.5% respectively, and the guilty group showed more obvious small-world property than innocent group. The results suggest the BGD and network analysis based approaches combined with SVM are efficient for ERP based expert and intelligent system for detection and evaluation of deception. The combination of these methods and other feature selection approaches can promote the development and application of ERP based lie detection system.

Honesty is a crucial aspect of a trusting parent-child relationship. Given that close relationships often impair our ability to detect lies and are related to a truth bias, parents may have difficulty with detecting their own children's lies. The current investigation examined the lie detection abilities (accuracy, biases, and confidence) of three groups of participants: non-parent group (undergraduates), parent-other group (parents who evaluated other peoples' children's statements), and parent-own group (parents who evaluated their own children's statements). Participants were presented with videos of 8- to 16-year-olds telling either the truth or a lie about having peeked at the answers to a test and were asked to evaluate the veracity of the statement along with their confidence in their judgment. All groups performed at chance in the accuracy of their veracity judgments. Furthermore, although all groups tended to hold a truth bias for 8- to 16-year-olds, the parent-own group held a much stronger truth bias than the other two groups. All groups were also highly confident in their judgments (70%-76%), but confidence ratings failed to predict accuracy. These findings, taken together, suggest that the close relationship that parents share with their own children may be related to a bias toward believing their children's statements and, hence, a failure to detect their lies.

A precedent for high-stakes deception detection using facial clues.Database consisting of real-world high-stakes deception videos is collected.Methods analyzing facial expressions in unconstrained environments are proposed. During a forensic interview, high-stakes deception is very prevalent notwithstanding the heavy consequences that may result. Studies have shown that most untrained people cannot perform well in discerning liars and truth-tellers.Some psychological studies have stated that certain facial actions are more difficult to inhibit if the associated facial expressions are genuine. Similarly, these facial expressions are equally difficult to fake. This has cast light on the possibility that deception could be detected by analyzing these facial actions. However, to the best knowledge of the authors, there is no computer vision research that has attempted to discriminate high-stakes deception from truth using facial expressions. Therefore, this paper aims to test the validity of facial clues to deception detection in high-stakes situations using computer vision approaches.We also note that only a limited number of the existing databases have been collected specifically for deception detection studies and none of them were obtained from real-world situations. In this paper we present a video database of actual high-stakes situations, which we have created using YouTube.We have adopted 2D appearance-based methods as the methodology to characterize the 3D facial features. Instead of building a 3D head model as is the current trend, we have extracted invariant 2D features that are related to the 3D characteristic from nine separate facial regions by using dynamic facial analysis: eye blink, eyebrow motion, wrinkle occurrence and mouth motion. Then these cues are integrated to form a facial behavior pattern vector. A Random Forest was trained using the collected database and applied to classify the facial patterns into deceptive and truthful categories.Despite the many uncontrolled factors (illumination, head pose and facial occlusion) contained in the videos in our database, we have achieved an accuracy of 76.92% when discriminating liars from truth-tellers using methods based on both micro-expressions and "normal" facial expressions. The results have shown that using facial clues for automated lie detection is very promising from the point of view of practice.

The pandemic caused by COVID-19 led to the distribution of excessive pseudoscientific information and fake news that has confused the general population. In the field of forensic psychiatry, lie detection is essential to determine if the witness is telling the truth with the purpose of making fair and effective decisions. In this research, we present a new approach that uses the pseudoscientific beliefs related to COVID-19 and 4 psychometric scales of the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2) to detect and predict lies. A total of 268 participants were classified into two groups: the control group (n ​= ​132) and the quasi-experimental group (n ​= ​136). The quasi-experimental group participants received instructions to lie as they wished in response to a number of questions on a content exam (called exam 1) based on a short children’s film. The participants had to indicate which and how many questions they had lied on. The quasi-experimental group was only required to lie in exam 1. A second exam (called exam 2) was also administered to assess whether the participants could recognize which news items about COVID-19 were false or true. The control group was not required to lie on any exam. Several multiple regression models were applied. The 4 scales of the MMSI-2 predicted 71.2% of the lies for exam 1 and 41.5% of the lies for exam 2. The control group participants obtained lower average scores on exam 1 than the quasi-experimental group in the “F” and “Si” scales. The theory of signal detection is proposed as a possible explanation of the effectiveness of the MMSI-2 scales in lie detection.

Anomalous phenomena are human experiences that are characterized by challenging the foundations of current scientific ontology (i.e., psi phenomena). The problem lies in the fact that some studies have obtained significant results that support the existential validity of psi phenomena. This fact calls into question the role of psychology -and specifically that of psychological assessment- in scientifically justifying and objectively evaluating this type of behavior. This work examines the construct validity and reliability of the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2), a psychometric test that measures both anomalous phenomena and the main psychological predictive variables that could generate them. The study included 804 participants without psychiatric history. The participants were evenly distributed into two groups: participants who believe in the existence of the paranormal and participants who are non-believers. Confirmatory factor analysis was applied, factorial invariance between both groups was examined, and Cronbach's alpha and Omega reliability coefficients were calculated. The results allowed accepting the ‘strong factorial invariance’ for the internal structure of the MMSI-2. In parallel, latent means analysis indicated that believers had higher scores than non-believers in the 4 latent variables of the test. Regression models indicated that the Clinical Personality Tendencies (CPT), Incoherent Manipulations (IMA) and Altered States of Consciousness (ASC) scales predicted 51.2% of anomalous phenomena. It is concluded that the MMSI-2, with its 174 items and 20 scales, is a valid and reliable psychometric instrument. This research is a continuation of the Escola-Gascon (2020) report, in which the first psychometric properties of the MMSI-2 were published.

The item to be concealed elicits greater physiological arousal than other items. Since high physiological arousal causes an overestimation of time, the display duration of an item is expected to be perceived as longer when people intend to conceal it. After stealing and concealing one item, 36 university students were asked to judge the display duration of an item as shorter than, equal to, or longer than a memorised duration of 2 s. Pictures of three items including the stolen item were presented in the guilty condition, whereas pictures of three items that had not been stolen were presented in the innocent condition. The display of all items in the guilty condition was perceived as longer than in the innocent condition without difference between the concealed and other items. The intention to conceal increases tonic arousal reflected in a higher skin conductance level and leads to a non-specific temporal overestimation.

Researchers started developing interview techniques to enhance deception detection in forensic settings. One of those techniques is the Model Statement, which has been shown to be effective for eliciting information and cues to deception in single interviews. In the current research, we focused on the effect of the Model Statement in multiple interviews. Participants (N = 243) were interviewed three times—each time one week apart—about a genuine (truth tellers) or fabricated (lie tellers) memorable event. They listened to a Model Statement at Time 1, Time 2, Times 1 and 2, or not at all. Hypotheses focused on participants' verbal reports at Time 3 and on unique details provided across the three interviews. In both instances, truth tellers provided more core and total details and complications and fewer common knowledge details and self-handicapping strategies and obtained higher proportion scores of (i) complications and (ii) core details than lie tellers. Complications and proportion of complications were the most diagnostic cues. The Model Statement was effective only when presented at Time 1, resulting in more common knowledge details. No Veracity × Model Statement interaction effects emerged.

Brain computer interface (BCI) provides communication between the computer and the brain. It is the combination of hardware and software which provides non-muscular channel to send the various messages to control the computer. BCI is useful in various medical applications such as patients with neuromuscular injuries, locked-in syndrome (LiS) etc. BCI is not only useful in medical applications, but also useful in lie detection, entertainment, etc. In this paper, spark and rule-KNN based scalable framework has been presented using BCI with the EEG data collected on 20 subjects in which 10 are acted as innocent and 10 are acted as guilty. Using BCI P300, Deceit identification Test (DIT) is performed. To perform DIT, we classify the P300 signals which have a positive peak of 300 ms–1000 ms in one stimulus start. Data processing is performed with band pass filter to cut the frequency ranges and features are extracted using non-parametric weighted feature extraction followed by rule based discriminant classification. For training and testing, the data ratio selected as 80:20 and achieved the accuracy 92.46 %. Proposed framework provides better results in comparison with existing models presented in literature. Hence this model is accurate, scalable and fault tolerant.

Self-report, the most widely used, gold standard measurement of pain, is crucial for pain research, diagnosis, and management. However, there are no accurate, reliable methods for detecting dishonesty in self-reports when there is incentive for pain deception. We introduce a novel approach to detecting pain deception by analyzing performance patterns of honest and dishonest psychophysical pain testing. Warmth sensation threshold (WST) and heat pain threshold (HPT) were measured in healthy individuals (N = 37) under 2 conditions: standard instruction (ie, provide sincere reports) and instructions to simulate intense pain (ie, provide feigned reports) with the intention of deceiving. In the feigned compared with sincere condition, participants had significantly increased WST and decreased HPT. Repeatability and variability indices were indistinguishable between conditions. In a second, separate cohort (N = 24), measurements were repeated with the addition of a sensory interference to influence task performance. When sensory interference during HPT measurement was introduced, feigned pain reports had significantly higher variability and poorer repeatability compared with sincere reports and were distinguishable from sincere reports, with high sensitivity (83%) and specificity (84%). The statistical properties of psychophysical performance under sensory interference provide a method for identifying feigned performance and could be applied to evaluations of pain malingering. Perspective This article introduces a method to detect whether individuals are being dishonest in psychophysical pain testing. The method could help clinicians to detect chronic pain malingering in contexts in which there is incentive to deceive.

The standard view in social science and philosophy is that lying does not require the liar's assertion to be false, only that the liar believes it to be false. We conducted three experiments to test whether lying requires falsity. Overall, the results suggest that it does. We discuss some implications for social scientists working on social judgments, research on lie detection, and public moral discourse.

We conducted a mock crime experiment with 250 paid participants (126 females, Mdn age = 30 years) contrasting the validity of the probable-lie and the directed-lie variants of the comparison question test (CQT) for the detection of deception. Subjects were assigned at random to one of eight conditions in a Guilt (Guilty/Innocent) × Test Type (Probable-Lie/Directed-Lie) × Stimulation (Between Repetition Stimulation/No Stimulation) factorial design. The data were scored by an experienced polygraph examiner who was unaware of subject assignment to conditions and with a computer algorithm known as the Objective Scoring System Version 2 (OSS2). There were substantial main effects of guilt in both the OSS2 computer scores F(1, 241) = 143.82, p < .001, η(p)(2) = 0.371, and in the human scoring, F(1, 242) = 98.92, p

Objective To explore the accuracy rate of the comparison question test (CQT) by Binomial Distribution way on field cases in Chengdu area of China. In detail, the study revealed the accuracy rate of detecting guilty and innocent examinees, and the rate of False Positive and False Negative. Method The study was built on the field cases with only two examinees that held the opposite opinions to the same single case. The original evaluators and four experienced polygraph specialists independently chose and scored 148 field criminal cases into the final study, among which 111 paired-records were concluded as indicative records and the rest, 37 ones, were ruled as non-indicative. Result Judging from all records, the accuracy rate of detecting guilty examinees was 0.836 with a 0.164 False Negative rate. In addition, the rate to detect innocents was 0.822 with a 0.178 False Positive rate. Judging from indicative cases only excluding the non-indicative, the accuracy rate rose to 0.958 for perceiving guilty examinees with a 0.042 False Negative rate. To innocents, the accuracy rate was 0.859 with a 0.141 False Positive rate. Conclusion The study with non-standard methodology was not limited to mock cases and the final confessions. It was able to estimate validity of other lie-detection ways, GKT, POT, etc., with this method being applied. With high accuracy rate and validity, polygraph examinations were able to give better decisions to real-life investigations.

Cheap-talk lies are at risk of being detected. We investigate the implications of this fact in a communication game in which players have no common interests and messages are cheap, but deceit is detectable with positive probability. In particular, when the sender makes a claim that is not true, with positive probability the receiver learns that the claim is a lie. We show that partially informative equilibria exist because lie detection generates an endogenous cost from lying consisting of being confused with lower types in equilibrium. In any informative equilibrium, the moderate and the highest types tell the truth, while the lowest types lie claiming to be the highest types. We show that more information may be revealed if the sender is given an opportunity to prepare a lie in advance and thereby decrease its detectability. We analyze an extension in which the sender may make multiple attempts at convincing the receiver, and show that if lie detectability is high, the receiver may benefit from committing to listening to the sender only once. And finally, we analyze a two-sender version of the model, and show that senders will exaggerate their claims only if the state disadvantages them sufficiently.

To ensure a more effective and applicable emotion recognition system with the use of an electroglottography (EGG) device, we investigated the relationship between pitch (an average frequency value obtained in terms of vibration around the neck), and the emotion felt, and if personality plays a role in the identification of emotions. Some of the motivations of this work include lie detection and paediatric emotional health evaluation. In this study, data obtained from EGG and DISC personality assessment of 25 participants were analysed. We found that EGG can actually serve the function to identify emotions between neutral, happy and sad. Through the use of mixed model ANOVA, the results of happy, neutral and sad are statistically different at p = 0.01. As to whether personality affects emotion expression, a significant main effect for personality was not found and only a weak interaction between pitch and personality was reported. Thus contrary to the hypothesis that extroverts and introverts would convey emotions with different intensity and accuracy, there is no noticeable effect of personality on the emotion recognition system.

We briefly review comments submitted in response to the target article in this series (Palmatier & Rovner, 2015) arguing that a scientifically defensible construct for the instrumental assessment of credibility (i.e. polygraph) may be found in Barry's Preliminary Process Theory (PPT). Our review of the relevant scientific literature discovered a growing body of converging evidence, particularly from the neurosciences that focus not only on deception, but more broadly on memory, emotion, and the orienting response (OR), leading to this conclusion. After reviewing the submitted comments, we are further convinced, especially as applied scientists, that at this time the most viable direction forward is in the context of the PPT. Concurrently, we candidly acknowledge that research must be conducted to address not only commentator concerns but, if warranted, modification of existing theory. Although disagreement continues to exist regarding the order in which questions are asked, the most significant finding, is perhaps that not a single commentator argues against this growing, and vital applied science (i.e., the instrumental assessment of credibility — polygraph).

The term “polygraph test,” particularly in a forensic context, is used generally to describe diagnostic procedures using a polygraph instrument to assess credibility. Polygraph testing has been subject to greater scrutiny, debate, and empirical study than many other forensic techniques. It has been repeatedly demonstrated that, when used properly, the polygraph testing process functions with a high degree of predictive (criterion) validity. However, advocates have failed to address, in a substantive manner, the primary objection often cited by opponents that the polygraph procedure most used in applied day-to-day contexts, that is, Comparison Question Testing (CQT), is atheoretical and lacking construct validity. A review of the available research literature, including that from the neurosciences, psychophysiology, and other relevant disciplines, coupled with an intimate understanding of two commonly used polygraph procedures, the context in which they are used, and the scientific method, strongly suggests that such claims are no longer true, nor warranted. Here, we discuss the interplay of the two most advocated polygraph procedures, the CQT and CIT (Concealed Information Testing), with Preliminary Process Theory (PPT), contemporary writings on memory and other contributions from the research literature relevant to the instrumental assessment of credibility. We conclude that the available scientific evidence not only establishes a plausible theoretical construct that strengthens the practical application of the polygraph process in forensic and other settings, but also concurrently provides directions for future research by scientists interested in the applied assessment of credibility.

Two experiments were conducted in order to examine factors that might influence the motivation of guilty and informed innocent examinees to either cope or cooperate with the Guilty Actions Test. Guilty participants committed a mock-crime and informed innocent participants handled the critical items of the crime in an innocent context. In Experiment 1 the participants were led to believe that the prospects of being found innocent on the test were either high or low. In Experiment 2 the participants were led to believe that the test was either highly accurate or of questionable validity. Results indicated that for both guilty and informed innocent participants low prospects of success and low detection efficacy of the test were associated with enhanced physiological responses to the critical information, whereas high prospects of success and high detection efficacy were associated with attenuated physiological responses. Theoretical and practical implications of the results are discussed.

Employing an event-related potential (ERP)-based concealed information test (CIT), the present study investigated (1) the neurocognitive processes when people received feedbacks regarding their deceptive/truthful responses and (2) whether such feedback-related ERP activities can be used to detect concealed information above and beyond the recognition-related P300. During the CIT, participants were presented with rare, meaningful probes (their own names) embedded within a series of frequent yet meaningless irrelevants (others’ names). Participants were instructed to deny their recognition of the probes. Critically, following participants’ responses, they were provided with feedbacks regarding whether they succeeded or failed in the CIT. Replicating previous ERP-based CITs, we found a larger P300 elicited by probe compared to irrelevant. Regarding feedback-related ERPs, a temporospatial Principle Component Analyses found two ERP components that were not only sensitive to feedback manipulations but also can discriminate probe from irrelevant: an earlier, central-distributed positivity that was elicited by “success” feedbacks peaked around 219 ms; and a later, right central-distributed positivity that was also elicited by “success” feedbacks, peaked around 400 ms. Importantly, the feedback ERPs were not correlated with P300 that was elicited by probe/irrelevant, suggesting that these two ERPs reflect independent processes underlying memory concealment. These findings illustrate the feasibility and promise of using feedback-related ERPs to detect concealed memory and thus deception.

The issue of lying occurs in neuropsychology especially when examinations are conducted in a forensic context. When a subject intentionally either presents non-existent deficits or exaggerates their severity to obtain financial or material compensation, this behaviour is termed malingering. Malingering is discussed in the general framework of lying in psychology, and the different procedures used by neuropsychologists to evidence a lack of collaboration at examination are briefly presented and discussed. When a lack of collaboration is observed, specific emphasis is placed on the difficulty in unambiguously establishing that this results from the patient's voluntary decision.

A current focus in deception research is on developing cognitive-load approaches (CLAs) to detect deception. The aim is to improve lie detection with evidence-based and ecologically valid procedures. Although these approaches show great potential, research on cognitive processes or mechanisms explaining how they operate is lacking. Potential mechanisms underlying the most popular techniques advocated for field application are highlighted. Cognitive scientists are encouraged to conduct basic research that qualifies the 'cognitive' in these new approaches.

Research interest in deception detection has significantly increased during the past decades. The current overview discusses the validity of specific behavioral measures that can detect concealed information. Thus we reviewed the main theoretical assumptions behind a novel method labeled as Reaction Time-based Concealed Information Test (RT-based CIT) and we presented an overview of the current research in the field. Although the RT-based CIT is a promising method for lie detection, the focus of this article is on its potential value in understanding the cognition of deception. Given the robust theoretical background of the RT-based CIT, we argue for the value of this line of research in uncovering the underlying cognitive mechanisms involved in deceptive behavior.

We investigated primary and secondary psychopathy and the ability to detect high-stakes, real-life emotional lies in an on-line experiment (N = 150). Using signal detection analysis, we found that lie detection ability was overall above chance level, there was a tendency towards responding liberally to the test stimuli, and women were more accurate than men Further, sex moderated the relationship between psychopathy and lie detection ability; in men, primary psychopathy had a significant positive correlation with the ability to detect lies, whereas in women there was a significant negative correlation with deception detection. The results are discussed with reference to evolutionary theory and sex differences in processing socio-emotional information.

The present study examined the effectiveness of the Guilty Action Test in detecting critical information from goal-oriented and task-oriented informed innocent examinees. A mock crime procedure was employed and informed innocent participants were either motivated to prove innocence (goal-oriented motivation) or to prove innocence by being cooperative on the test (task-oriented motivation). Half of the participants in each motivation condition were promised course credit reward for successful completion of their mission to prove innocence or to be cooperative (high incentive level). The other half were promised no reward (low incentive level). A fifth group of uninformed innocent participants served for control purposes. Electrodemal, respiration, and cardiovascular measures were used to indicate the motivation effects. Results showed that the combination of goal-oriented instructions and an incentive for success contributed to enhanced responses to the crime-related information. The combination of task-oriented instructions and an incentive for success attenuated these responses. Skin conductance responses were most sensitive to these effects. Theoretical and practical aspects of the results were discussed.

Deception is commonly seen in everyday social interactions. However, most of the knowledge about the underlying neural mechanism of deception comes from studies where participants were instructed when and how to lie. To study spontaneous deception, we designed a guessing game modeled after Greene and Paxton (2009) "Proceedings of the National Academy of Sciences, 106(30), 12506-12511", in which lying is the only way to achieve the performance level needed to end the game. We recorded neural responses during the game using near-infrared spectroscopy (NIRS). We found that when compared to truth-telling, spontaneous deception, like instructed deception, engenders greater involvement of such prefrontal regions as the left superior frontal gyrus. We also found that the correct-truth trials produced greater neural activities in the left middle frontal gyrus and right superior frontal gyrus than the incorrect-truth trials, suggesting the involvement of the reward system. Furthermore, the present study confirmed the feasibility of using NIRS to study spontaneous deception.

Recent literature suggests that lying may be revealed by elevated cognitive effort. A functional magnetic resonance imaging experiment using a match-mismatch detection task was conducted that found support for this hypothesis in two ways. First, compared to truthful reporting, lying (i.e., responding that matches were mismatches or vice versa) triggered greater activation of the working memory network in the brain. This was especially true for lying about a match, where activation in the WM network was found to be greater than when lying about a mismatch. Lying also activated the right rostrolateral prefrontal cortex (BA 10)-a key cognitive control region that regulates the interplay between stimulus-oriented and internally-generated schemas. Second, activation in the right inferior frontal gyrus (BA 44) - a brain region underpinning inhibition - predicted lying skill. The findings show that the neural correlates of cognitive effort and control can be used to detect lying, and that a specific neural marker of inhibition can predict how well one lies.

Previous research on the Concealed Information Test indicates that knowledge of the critical information of a given event is sufficient for the elicitation of strong physiological reactions, thus facilitating detection by the test. Other factors that affect the test's efficacy are deceptive verbal responses to the test's questions and motivation of guilty examinees to avoid detection. In the present study effects of coping and cooperative instructions — delivered to guilty and innocent participants — on detection were examined. In a mock-theft experiment guilty participants who actually committed a mock-crime, and informed innocent participants who handled the critical items of the crime in an innocent context, were instructed to adopt either a coping or a cooperative attitude toward the polygraph test. Results indicated that both, guilt and coping behavior, were associated with enhanced physiological responses to the critical information, whereas innocence and cooperative behavior attenuated physiological responses. Theoretical and applied implications of the results are discussed.