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. (shrink)

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. (shrink)

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 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. (shrink)