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

The DOI matches its Crossref record; 46 of the 47 references it cites were found in a registry or an index.

Frame-differencing methods for measuring bodily synchrony in conversation.

Open access
Paxton, Alexandra, Dale, Rick·2013·Behavior Research Methods
DOI 10.3758/s13428-012-0249-2
Open this paper in CoChat — free accountOpen PDF

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.

Overview

Integrity signals

Registry

Crossref · 10.3758/s13428-012-0249-2

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 2

  1. Battersby, S. A., Lavelle, M., Healey, P. G., & McCabe, R (2008). Analysing interaction: A comparison of 2D and 3D techniques. Paper presented at the Multimodal Corpora Workshop in the Sixth International Language Resources and Evaluation (LREC’08), Marrakech, Morocco.
  2. Kreuz, R. J., & Riordan, M. A. (2011). The transcription of face-to-face interaction. In W. Bublitz & N. R. Norrick (Eds.), Handbooks of pragmatics (Vol. 1, pp. 657–680). Berlin: De Gruyter Mouton.

Cited by

Sorted by citations

See all 171

Related

See all

Metadata

Authors
Paxton, A., & Dale, R.
Affiliations
University of California, Merced (US)
Corresponding author
Paxton, Alexandra
ORCID
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
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 ›

Are you an author, or do you see a problem with this page? Request a review