Frame-differencing methods for measuring bodily synchrony in conversation.

Paxton, Alexandra; Dale, Rick

doi:10.3758/s13428-012-0249-2

Cited by 171Action Observation and SynchronizationLanguage, Discourse, Communication StrategiesHealth, Education, and Physical Culture

Frame-differencing methods for measuring bodily synchrony in conversation.

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Paxton, Alexandra, Dale, Rick·2013·Behavior Research Methods

DOI 10.3758/s13428-012-0249-2

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Abstract

The study of interpersonal synchrony examines how interacting individuals grow to have similar behavior, cognition, and emotion in time. Many of the established methods of analyzing interpersonal synchrony are costly and time-consuming; the study of bodily synchrony has been especially laborious, traditionally requiring researchers to hand-code movement frame by frame. Because of this, researchers have been searching for more efficient alternatives for decades. Recently, some researchers (e.g., Nagaoka & Komori (IEICE Transactions on Information and Systems, 91(6), 1634-1640, 2008); Ramseyer & Tschacher, 2008) have applied computer science and computer vision techniques to create frame-differencing methods (FDMs) to simplify analyses. In this article, we provide a detailed presentation of one such FDM, created by modifying and adding to existing FDMs. The FDM that we present requires little programming experience or specialized equipment: Only a few lines of MATLAB code are required to execute an automated analysis of interpersonal synchrony. We provide sample code and demonstrate its use with an analysis of brief, friendly conversations; using linear mixed-effects models, the measure of interpersonal synchrony was found to be significantly predicted by time lag (p < .001) and by the interaction between time lag and measures of interpersonal liking (p < .001). This pattern of results fits with existing literature on synchrony. We discuss the current limitations and future directions for FDMs, including their use as part of a larger methodology for capturing and analyzing multimodal interaction.

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MEA has become a useful tool for investigating interpersonal synchrony in clinical and non-clinical contexts.
“MEA has become a useful tool for investigating interpersonal synchrony in clinical and non-clinical contexts (e.g., Ramseyer and Tschacher, 2011; Paxton and Dale, 2013).”
From Synchrony in Psychotherapy: A Review and an Integrative Framework for the Therapeutic Alliance (2016) · cites this paper as Paxton and Dale, 2013

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ORI GIN AL PA PER Measuring the Dynamics of Interactional Synchrony
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Authors: Paxton, A., & Dale, R.
Affiliations: University of California, Merced (US)
Corresponding author: Paxton, Alexandra
ORCID: Paxton, Alexandra
Dale, Rick
Venue: Behavior Research Methods
Publisher: Springer Science and Business Media LLC
Published: 2012-10-06
Volume: 45
Issue: 2
Pages: 329-343
ISSN: 1554-351X (ISSN-L)
1554-3528
DOI: 10.3758/s13428-012-0249-2
OpenAlex: W2073406124
Cited by: 171
References: 47
Citations by year: 2026: 7 · 2025: 13 · 2024: 6 · 2023: 4 · 2022: 26 · 2021: 11 · 2020: 15 · 2019: 16 · 2018: 16 · 2017: 15 · 2016: 12 · 2015: 11 · 2014: 9 · 2013: 10
FWCI: 14.49
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
Locations: Behavior Research Methods (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation (0826825)
Topics: Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
Social Sciences › Arts and Humanities › Language and Linguistics › Language, Discourse, Communication Strategies
Social Sciences › Psychology › Social Psychology › Health, Education, and Physical Culture
Keywords: interpersonal synchrony, computer vision, conversation, time delay, interpersonal liking, multimodal interaction, MATLAB code
MeSH terms: Adult, Algorithms, Emotions, Female, Humans, Kinesics, Male, Models, Psychological, Posture, Young Adult

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Frame-differencing methods for measuring bodily synchrony in conversation.

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