Cross-cultural variation and fMRI lie-detection

As decidedly underscored by a recent editorial in Nature Neuroscience (2010), many experiments in cognitive neuroscience have been carried out with a sample that is not representative of the general human population, as the subjects are usually university students in psychology. The underlying assumption of this practice is that the workings of the brain do not vary much even when subjects come from different cultural groups. Recent research by Henrich et al. (2010) shows that this assumption is unwarranted. On several basic features of perception and cognition, Western university students turn out to be outliers relative to the general human population, so that data based on them should be interpreted with caution. In particular, this situation seems to provide an argument for questioning the conformity of functional Magnetic Resonance Imaging (fMRI) lie-detection to Federal Rule of Evidence 702 and Daubert. Deception is a social phenomenon and it is related to mental functions, such as theory of mind, for which cross-cultural variability at the neural level has been detected. Furthermore, culture is a multi-dimensional variable whose effects are diverse. Thus, the use of fMRI lie-detection in legal contexts may hinder the ascertainment of truth if the experimental results are not shown to be conserved in different cultures. Cross-cultural variability in neural activation patterns is just a facet of the broader issue of external and ecological validity for neuroscientific experiments on the detection of deception; nonetheless, fMRI lie- detection is unlikely to meet the Daubert standards if cross-cultural variation is not controlled by appropriate experiments

A holistic, risk, and futures based approach to deception: technological convergence and emerging patterns of conflict

Modern challenges in forensic and security domains require greater insight and flexibility into the ways deception can be identified and responded to. Deception is common across interactions and understanding how mindset, motive and context affects deception is critical. Research has focussed upon how deception manifests in interpersonal interactions and has sought to identify behaviours indicative of truth-telling and deceit. The growth of mediated communication has further increased challenges in ensuring information is credible. Deception in military environments has focussed on planning deception, where approaches have been developed to deceive others, but rarely examined from counter-deception perspectives. To address these challenges this thesis advocates a holistic approach to deception detection, whereby strategies will be tailored to match context. In accordance with an in vivo approach to research, a critical review of literature related to deception and related areas was conducted leading to the initial development of a theoretical holistic model of deception detection comprising a deception framework and an individual differences (deceiver and target) framework. Following model development, validation with Subject Matter Experts (SMEs) was conducted. Explanatory thematic analysis of interviews conducted with SMEs (n=19) led to the development of meta-themes related to the ‘deceiver’, their ‘intent; ‘strategies and tactics’ of deception, ‘interpretation’ by the target and ‘target’ decision-making strengths and vulnerabilities. These findings led to the development of the Holistic Model of Deception, an approach where detection strategies are tailored to match the context of an interaction, whether interpersonal or mediated. Understanding the impact of culture on decision-making in deception detection and in particular the cues used to detect deception in interpersonal and mediated environments is required for understanding human behaviour in a globalised world. Interviews were conducted with Western (n=22) and Eastern (n=16) participants before being subject to explanatory and comparative thematic analysis identified twelve cross-cultural strategies for assessing credibility and one culturally specific strategy used by Western participants. Risk assessment and management techniques have been used to assess risks posed in forensic and security environments; however, such approaches have not been applied to deception detection. The Deception Assessment Real-Time Nexus©2015 and Deception Risk Assessment Technique©2015 were developed as an early warning tool and a Structured Professional Judgement risk assessment and management technique. The Deception Risk Assessment Technique©2015 outlines multiple ways of identifying and managing threats posed by deception and is employable across individuals and groups. In developing the futures-based approach to deception detection, reactive, active and proactive approaches to deception were reviewed, followed by an examination of scenario planning utility and methodology from futures and strategic forecasting research. Adopting the qualitative ‘intuitive logics’ methodology ten scenarios were developed of potential future threats involving deception. Risk assessment of two scenarios was conducted to show the value of a risk assessment approach to deception detection and management. In conclusion, this thesis has developed a Holistic Model of Deception, explored the links between interpersonal and mediated strategies for detecting deception, formulated a risk assessment and management approach to deception detection and developed future scenarios of threats involving deception

Smile asymmetries and reputation as reliable indicators of likelihood to cooperate: An evolutionary analysis

Cooperating with individuals whose altruism is not motivated by genuine prosocial emotions could have been costly in ancestral division of labour partnerships. How do humans ‘know’ whether or not an individual has the prosocial emotions committing future cooperation? Frank (1988) has hypothesized two pathways for altruist-detection: (a) facial expressions of emotions signalling character; and (b) gossip regarding the target individual’s reputation. Detecting non-verbal cues signalling commitment to cooperate may be one way to avoid the costs of exploitation. Spontaneous smiles while cooperating may be reliable index cues because of the physiological constraints controlling the neural pathways mediating involuntary emotional expressions. Specifically, it is hypothesized that individuals whose help is mediated by a genuine sympathy will express involuntary smiles (which are observably different from posed smiles). To investigate this idea, 38 participants played dictator games (i.e. a unilateral resource allocation task) against cartoon faces with a benevolent emotional expression (i.e. concern furrows and smile). The faces were presented with information regarding reputation (e.g. descriptions of an altruistic character vs. a non-altruistic character). Half of the sample played against icons with symmetrical smiles (representing a spontaneous smile) while the other half played against asymmetrically smiling icons (representing a posed smile). Icons described as having altruistic motives received more resources than icons described as self-interested helpers. Faces with symmetrical smiles received more resources than faces with asymmetrical smiles. These results suggest that reputation and smile asymmetry influence the likelihood of cooperation and thus may be reliable cues to altruism. These cues may allow for altruists to garner more resources in division of labour situations

Deception Detection in Videos

We present a system for covert automated deception detection in real-life courtroom trial videos. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on low level video features which predict human micro-expressions. We show that predictions of high-level micro-expressions can be used as features for deception prediction. Surprisingly, IDT (Improved Dense Trajectory) features which have been widely used for action recognition, are also very good at predicting deception in videos. We fuse the score of classifiers trained on IDT features and high-level micro-expressions to improve performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio domain also provide a significant boost in performance, while information from transcripts is not very beneficial for our system. Using various classifiers, our automated system obtains an AUC of 0.877 (10-fold cross-validation) when evaluated on subjects which were not part of the training set. Even though state-of-the-art methods use human annotations of micro-expressions for deception detection, our fully automated approach outperforms them by 5%. When combined with human annotations of micro-expressions, our AUC improves to 0.922. We also present results of a user-study to analyze how well do average humans perform on this task, what modalities they use for deception detection and how they perform if only one modality is accessible. Our project page can be found at \url{https://doubaibai.github.io/DARE/}.Comment: AAAI 2018, project page: https://doubaibai.github.io/DARE

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

Liar, Liar: Micro-expression Application to Detect Deception

This study focused on nonverbal mirco-expressions using the deception detection method to shed light on the effectiveness of such a tool for use in detecting liars. Five examples of videos depicting instances in which individuals who were later proven to be lying were analyzed in order to assess the reliability of such a tool in an area of interest to both communication and psychology. This study suggested that the theory provides a reliable tool for assessing the use of deception by a variety of people in different situations. The paper upon which this poster was based was written for the Senior Seminar course in Communication Arts