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Record W2405053004

Movement Correlation as a Nonverbal Cue in the Judgment of Affiliation during Social Interaction

2013· article· en· W2405053004 on OpenAlexaffabout
Nida Latif, Adraino Barbosa, Eric Vatikiotis-Batesom, Monica S. Castelhano, Kevin G. Munhall

Bibliographic record

VenueeScholarship (California Digital Library) · 2013
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsConversationMovement (music)PsychologyPerceptionNonverbal communicationSocial cueSocial psychologyCorrelationSocial relationCognitive psychologyCommunicationAestheticsArt
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.018
GPT teacher head0.252
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2013
Admission routes2
Has abstractyes

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