Detecting ulterior motives from verbal cues in group deliberations.

Introduction: Forensic interviewing entails practitioners interviewing suspects to secure valid information and elicit confessions. Such interviews are often conducted in police stations but may also occur in field settings such as border crossings, security checkpoints, bus terminals, and sports venues. Because these real-world interviews often lack experimental control and ground truth, this investigation explored whether results of non-forensic interviews generalize to forensic ones. Methods: Organizational espionage was simulated to determine (1) what verbal signals distinguish truth from deception, (2) whether deception in groups aligns with deception in dyads, and (3) whether non-forensic venues can be generalized to forensic ones. Engaging in a mock hiring deliberation, participants (4-5 strangers) reviewed and discussed resumes of potential candidates. Surreptitiously, two group members assigned the role of "organizational spies" attempted to persuade the group to hire an inferior candidate. Each group member presented notes from an interview of "their" candidate, followed by a discussion of all candidates. Spies were to use any means possible, including deception, to persuade others to select their candidate. A financial incentive was offered for having one's candidate chosen. The interview reports and discussions were transcribed and analyzed with SPLICE, an automated text analysis program. Results: Deceivers were perceived as less trustworthy than truth-tellers, especially when the naïve players won but overall, deceivers were difficult for non-spies to detect even though they were seen as less trustworthy than the naïve participants. Deceivers' language was more complex and exhibited an "echoing" strategy of repeating others' opinions. This collusion evolved naturally, without pre-planning. No other verbal differences were evident, which suggests that the difference between spies and non-spies was subtle and difficult for truth-tellers to spot. Discussion: Whether deception can be successfully detected hinges on a variety of factors including the deceiver's skill to disguise and the detector's ability to sense and process information. Furthermore, the group dynamics and communication context subtly moderate how deception manifests and influence the accuracy of detecting ulterior motives. Our future investigations could encompass non-verbal communication channels and verbal patterns rooted in content, thereby providing a more comprehensive understanding of deception detection.

Linguistic measures of personality in group discussions.

This investigation sought to find the relationships among multiple dimensions of personality and multiple features of language style. Unlike previous investigations, after controlling for such other moderators as culture and socio-demographics, the current investigation explored those dimensions of naturalistic spoken language that most closely align with communication. In groups of five to eight players, participants (N = 340) from eight international locales completed hour-long competitive games consisting of a series of ostensible missions. Composite measures of quantity, lexical diversity, sentiment, immediacy and negations were measured with an automated tool called SPLICE and with Linguistic Inquiry and Word Count. We also investigated style dynamics over the course of an interaction. We found predictors of extraversion, agreeableness, and neuroticism, but overall fewer significant associations than prior studies, suggesting greater heterogeneity in language style in contexts entailing interactivity, conversation rather than solitary message production, oral rather than written discourse, and groups rather than dyads. Extraverts were found to maintain greater linguistic style consistency over the course of an interaction. The discussion addresses the potential for Type I error when studying the relationship between language and personality.

Nonverbal Behaviors "Speak" Relational Messages of Dominance, Trust, and Composure.

Nonverbal signals color the meanings of interpersonal relationships. Humans rely on facial, head, postural, and vocal signals to express relational messages along continua. Three of relevance are dominance-submission, composure-nervousness and trust-distrust. Machine learning and new automated analysis tools are making possible a deeper understanding of the dynamics of relational communication. These are explored in the context of group interactions during a game entailing deception. The "messiness" of studying communication under naturalistic conditions creates many measurement and design obstacles that are discussed here. Possibilities for their mitigation are considered.

How the Brunswikian Lens Model Illustrates the Relationship Between Physiological and Behavioral Signals and Psychological Emotional and Cognitive States.

Social relationships are constructed by and through the relational communication that people exchange. Relational messages are implicit nonverbal and verbal messages that signal how people regard one another and define their interpersonal relationships-equal or unequal, affectionate or hostile, inclusive or exclusive, similar or dissimilar, and so forth. Such signals can be measured automatically by the latest machine learning software tools and combined into meaningful factors that represent the socioemotional expressions that constitute relational messages between people. Relational messages operate continuously on a parallel track with verbal communication, implicitly telling interactants the current state of their relationship and how to interpret the verbal messages being exchanged. We report an investigation that explored how group members signal these implicit messages through multimodal behaviors measured by sensor data and linked to the socioemotional cognitions interpreted as relational messages. By use of a modified Brunswikian lens model, we predicted perceived relational messages of dominance, affection, involvement, composure, similarity and trust from automatically measured kinesic, vocalic and linguistic indicators. The relational messages in turn predicted the veracity of group members. The Brunswikian Lens Model offers a way to connect objective behaviors exhibited by social actors to the emotions and cognitions being perceived by other interactants and linking those perceptions to social outcomes. This method can be used to ascertain what behaviors and/or perceptions are associated with judgments of an actor's veracity. Computerized measurements of behaviors and perceptions can replace manual measurements, significantly expediting analysis and drilling down to micro-level measurement in a previously unavailable manner.

Machine learning methods for credibility assessment of interviewees based on posturographic data.

This paper discusses the advantages of using posturographic signals from force plates for non-invasive credibility assessment. The contributions of our work are two fold: first, the proposed method is highly efficient and non invasive. Second, feasibility for creating an autonomous credibility assessment system using machine-learning algorithms is studied. This study employs an interview paradigm that includes subjects responding with truthful and deceptive intent while their center of pressure (COP) signal is being recorded. Classification models utilizing sets of COP features for deceptive responses are derived and best accuracy of 93.5% for test interval is reported.

When is Deceptive Message Production More Effortful than Truth-Telling? A Baker's Dozen of Moderators.

Deception is thought to be more effortful than telling the truth. Empirical evidence from many quarters supports this general proposition. However, there are many factors that qualify and even reverse this pattern. Guided by a communication perspective, I present a baker's dozen of moderators that may alter the degree of cognitive difficulty associated with producing deceptive messages. Among sender-related factors are memory processes, motivation, incentives, and consequences. Lying increases activation of a network of brain regions related to executive memory, suppression of unwanted behaviors, and task switching that is not observed with truth-telling. High motivation coupled with strong incentives or the risk of adverse consequences also prompts more cognitive exertion-for truth-tellers and deceivers alike-to appear credible, with associated effects on performance and message production effort, depending on the magnitude of effort, communicator skill, and experience. Factors related to message and communication context include discourse genre, type of prevarication, expected response length, communication medium, preparation, and recency of target event/issue. These factors can attenuate the degree of cognitive taxation on senders so that truth-telling and deceiving are similarly effortful. Factors related to the interpersonal relationship among interlocutors include whether sender and receiver are cooperative or adversarial and how well-acquainted they are with one another. A final consideration is whether the unit of analysis is the utterance, turn at talk, episode, entire interaction, or series of interactions. Taking these factors into account should produce a more nuanced answer to the question of when deception is more difficult than truth-telling.

Is interactional dissynchrony a clue to deception? Insights from automated analysis of nonverbal visual cues.

Detecting deception in interpersonal dialog is challenging since deceivers take advantage of the give-and-take of interaction to adapt to any sign of skepticism in an interlocutor's verbal and nonverbal feedback. Human detection accuracy is poor, often with no better than chance performance. In this investigation, we consider whether automated methods can produce better results and if emphasizing the possible disruption in interactional synchrony can signal whether an interactant is truthful or deceptive. We propose a data-driven and unobtrusive framework using visual cues that consists of face tracking, head movement detection, facial expression recognition, and interactional synchrony estimation. Analysis were conducted on 242 video samples from an experiment in which deceivers and truth-tellers interacted with professional interviewers either face-to-face or through computer mediation. Results revealed that the framework is able to automatically track head movements and expressions of both interlocutors to extract normalized meaningful synchrony features and to learn classification models for deception recognition. Further experiments show that these features reliably capture interactional synchrony and efficiently discriminate deception from truth.

Deception effects on standing center of pressure.

Accurate deception detection is a desirable goal with many applications including credibility assessment, security screening, counter-terrorism, and homeland security. However, many deception detection methodologies involve intrusive sensors or other limitations that preclude their use in a covert manner. Posturography may overcome these limitations by using minimally invasive force platform technology. In this study, we tested the hypothesis that posturography would reveal deception-related increases in postural rigidity similar to those observed with previous methodologies. Participants were randomly assigned to a control (CG) or experimental group (EG), and interviewed about the contents of a backpack in their possession while standing on a force platform. EG participants were asked to conceal the presence of several "prohibited" items in the backpack from the interviewer. Center of pressure (COP) measures from the force platform were used to characterize postural sway during participants' verbal responses. We observed a significant deception-related increase in sway frequency, an effect primarily occurring during longer responses that is likely related to increased cognitive load. These findings suggest deception-related increases in postural rigidity as reported in previous work, and demonstrate the feasibility of using posturography as a deception detection tool.