Movement Correlation as a Nonverbal Cue in the Judgment of Affiliation during Social Interaction
Bibliographic record
Abstract
Movement Correlation as a Nonverbal Cue in the Judgment of Affiliation during Social Interaction Nida Latif (n.latif@queensu.ca) 1 Adriano V. Barbosa (adriano.vilela@gmail.com) 3 Eric Vatikiotis-Bateson (evb@mail.ubc.ca) 3 Monica S. Castelhano (monica.castelhano@queensu.ca) 1 Kevin G. Munhall (kevin.munhall@queensu.ca) 1,2 Department of Psychology, Queen’s University Kingston, ON K7L 3N6 Canada Department of Otolaryngology, Queen’s University Kingston, ON K7L 3N6 Canada Department of Linguistics, University of British Columbia Vancouver, BC V6T 1Z4 Canada Abstract It has been demonstrated that brief exposure to behavioral information is sufficient for making accurate social judgments. Movement coordination during social interaction, is one potential cue. Although coordination between individuals has been identified, our ability to perceive it when making judgments regarding affiliation (friends vs. strangers) is unknown. In the present studies, we investigated how correlated movement contributes to observers’ accuracy when judging affiliation. Using correlation map analysis to quantify coordination, we showed that individuals familiar with each other correlated their movements more frequently. Observers were able to use coordination as a cue, but only when the information presented was restricted to movement related to speech (i.e. while only viewing faces). These results suggest that observed movement coordination is influenced by speech-related movements. We suggest that social perception is multi-faceted and cues may be prioritized differentially based on availability. Keywords: social perception; social conversation; movement correlation interaction Introduction Humans are constantly immersed in social interaction and conversation. It is not surprising that we have mechanisms to facilitate these interactions, both while engaging in and observing them. One mechanism is our ability to create a rich representation of social cues from very brief exposures as short as a few seconds. These short exposures or “thin slices” of behavioral and linguistic information are sufficient for making remarkably accurate judgments regarding social situations. Research has demonstrated great accuracy in judgments in a variety of domains including personality, social status and mental states (Ambady & Rosenthal, 1993). However, little known about how specific cues contribute to our accuracy in social perception. Understanding how we use available social cues is important to human behavior because monitoring others’ intentions and actions is a prerequisite for modulating and guiding our own behavior, interactions, and relationship formation (Foulsham et al., 2010). In addition, using social cues is often impaired in many social and psychological disorders such as autism spectrum disorder and schizophrenia leading to difficulty in successful interaction (Klin et al., 2002). Previous research has looked at specific motion patterns that occur during interpersonal communication. Studies have demonstrated that individuals unintentionally synchronize and coordinate their movements and converge in linguistic properties during conversation (Richardson, Dale & Shockley, 2008; Richardson & Dale, 2005; Chartrand & van Baaren, 2009; Pardo, 2006). This has been shown with different attributes of conversation such as facial expression, postures and accents (Capella & Planalp, 1981; McHugo, Lanzetta, Sullivan, Masters & Englis, 1985). Individuals even unintentionally coordinate their movements without visual information from their partner (Shockley et al., 2003). Coordination without visual information suggests that convergence in behavior can be directly influenced by vocal information exchanged during conversation. Further, studies examining social-cognitive variables in convergence have shown through subjective observation that individuals with good rapport coordinate their movements (Grahe & Bernieri, 1999). Also, friends converge more in linguistic properties than strangers (Dunne & Ng, 2002). Coordination may occur because of inherent biological and behavioral rhythms as well as a coupling of conversation-engaged individuals’ mental representation of their perceptions of each other (Richardson & Dale, 2005; Meltzoff & Prinz, 2002). Although the presence of convergence in nonverbal and linguistic properties has been examined, our ability to perceive this convergence has not been investigated. In particular, the contribution of correlated movements has not been examined objectively in the perception of affiliation (i.e. whether individuals engaged in conversation are friends or strangers). The current studies use movement and coordination quantification methods to examine convergence between interacting individuals. These methods allowed us to investigate whether the amount of coordination differs as a function of affiliation and whether it contributes to the accuracy of affiliation judgments made by an external observer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".