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Frame-differencing methods for measuring bodily synchrony in conversation.
Open accessAbstract
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 (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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Metadata
- Authors
- Paxton, A., & Dale, R.
- Affiliations
- University of California, Merced (US)
- Corresponding author
- Paxton, Alexandra
- Venue
- Behavior Research Methods
- Published
- 2012-10-06
- Volume
- 45
- Issue
- 2
- Pages
- 329-343
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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