Kinesic Patterning in Deceptive and Truthful Interactions

A persistent question in the deception literature has been the extent to which nonverbal behaviors can reliably distinguish between truth and deception. It has been argued that deception instigates cognitive load and arousal that are betrayed through visible nonverbal indicators. Yet, empirical evidence has often failed to find statistically significant or strong relationships. Given that interpersonal message production is characterized by a high degree of simultaneous and serial patterning among multiple behaviors, it may be that patterns of behaviors are more diagnostic of veracity. Or it may be that the theorized linkage between internal states of arousal, cognitive taxation, and efforts to control behavior and nonverbal behaviors are wrong. The current investigation addressed these possibilities by applying a software program called THEME to analyze the patterns of kinesic movements (adaptor gestures, illustrator gestures, and speaker and listener head movements) rated by trained coders for participants in a mock crime experiment. Our multifaceted analysis revealed that the quantity and quality of patterns distinguish truths from untruths. Quantitative and qualitative analyses conducted by case and condition revealed high variability in the types and complexities of patterns that were produced and differences between truthful and deceptive respondents questioned about a theft. Patterns incorporating adaptors and illustrator gestures were correlated in counterintuitive ways with arousal, cognitive load, and behavioral control, and qualitative analyses produced unique insights into truthful and untruthful communication

Deceptive Language by Innocent and Guilty Criminal Suspects: The Influence of Dominance, Question, and Guilt on Interview Responses

Matthew L. Jensen is an assistant professor in the Price College of Business and a researcher in the Center for Applied Social Research at the University of Oklahoma. His primary research interests are deception and credibility in online and face-to-face interaction. Recent publications have dealt with computer-aided deception detection and establishing credibility online.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

The Viability of Using Rapid Judgments as a Method of Deception Detection

Rapid Judgments (RJs) are quick assessments based on indirect verbal and nonverbal cues that are known to be associated with deception. RJs are advantageous because they eliminate the need for expensive detection equipment and only require minimal training for coders with relatively accurate judgments. Results of testing on two different datasets showed that trained coders were reliably making RJs after watching both long and short interaction segments but their judgments were not more accurate than the expert interviewers. The RJs did not discriminate between truth and deception as hypothesized. This raises more questions about the conditions under which making RJs from verbal and nonverbal cues achieves accurate detection of veracity.18 month embargo; published online: 25 January 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Mitigating bias blind spot via a serious video game

We employed a serious video game to train participants on bias blind spot (BBS), capturing training effects on BBS mitigation and knowledge at three points in time. Experiment 1 (N = 703) compared the effects of hybrid training (a combination of implicit and explicit training) to implicit training; Experiment 2 (N = 620) tested the effects of just-in-time versus delayed feedback; and Experiment 3 (N = 626) examined the effects of singleplayer versus multiplayer learning environments. We also tested differences in game duration (30 vs. 60 minute play) and repetition (single vs. repeated play). Overall, the video game decreased BBS linearly over time and increased BBS knowledge at posttest, but knowledge decayed at 8-week posttest. These and other results are discussed, along with the implications, limitations, and future research directions

Establishing a Foundation for Automated Human Credibility Screening

Automated human credibility screening is an emerging research area that has potential for high impact in fields as diverse as homeland security and accounting fraud detection. Systems that conduct interviews and make credibility judgments can provide objectivity, improved accuracy, and greater reliability to credibility assessment practices, need to be built. This study establishes a foundation for developing automated systems for human credibility screening

Non-Invasive Measurement of Trust in Group Interactions

Trust between group members has many implications for how well a group performs. In this study, we predict perceived trustworthiness of group members when there are subversive group members. We collected multimodal verbal and nonverbal data from a group interaction experiment. During the interaction, we periodically surveyed the group members about their perceptions of trustworthiness of other group members. We used this data to model the relationship between observable behavior and perceptions of trustworthiness. We report the most predictive features and describe them in the context of existing literature on verbal and nonverbal correlates of trust. This research advances the study of behavioral measurement in groups and the role of behavior on perceived trustworthiness

The Psychology of Trust from Relational Messages

A fundamental underpinning of all social relationships is trust. Trust can be established through implicit forms of communication called relational messages. A multidisciplinary, multi-university, cross-cultural investigation addressed how these message themes are expressed and whether they are moderated by culture and veracity. A multi-round decision-making game with 695 international participants assessed the nonverbal and verbal behaviors that express such meanings as affection, dominance, and composure, from which people ultimately determine who can be trusted and who not. Analysis of subjective judgments showed that trust was most predicted by dominance, then affection, and lastly, composure. Behaviorally, several nonverbal and verbal behaviors associated with these message themes were combined to predict trust. Results were similar across cultures but moderated by veracity. Methodologically, automated software extracted facial features, vocal features, and linguistic metrics associated with these message themes. A new attentional computer vision method retrospectively identified specific meaningful segments where relational messages were expressed. The new software tools and attentional model hold promise for identifying nuanced, implicit meanings that together predict trust and that can, in combination, serve as proxies for trust