Synchronisation nonverbalen Verhaltens

Altmann, Uwe

doi:10.1007/978-3-531-19815-6

Cited by 25 publications

(38 citation statements)

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“…Nonverbal synchrony was measured using an automated algorithm using windowed cross-lagged correlation (WCLC) and a peak-picking algorithm implemented by Altmann (2011Altmann ( , 2013; publicly available at https://github.com/10101-00001/sync_ident). Before WCLC was run, the time series of both patient and therapist were logarithm transformed to account for different peak heights.…”

Section: Quantification Of Nonverbal Synchronymentioning

confidence: 99%

“…Whereas former studies investigating nonverbal synchrony used human ratings to analyze body movements (e.g., Chartrand & Bargh, 1999), recent studies make use of more economic automatic video analysis systems, finding comparable results (e.g., Altmann, 2013;Tschacher, Rees, & Ramseyer, 2014). In this line of research working with automatic video analysis systems, motion energy analysis (MEA; e.g., Altmann, 2011Altmann, , 2013Ramseyer & Tschacher, 2011;Watanabe, 1983) is the most commonly applied approach in clinical research. It assesses simultaneous and slightly time-lagged movements of interacting persons within a video clip (generated values based on pixel changes are subsequently used to calculate synchrony).…”

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confidence: 99%

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Patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multidimensional outcome.

Lutz¹,

Prinz²,

Schwartz³

et al. 2020

Journal of Counseling Psychology

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Early change is an increasing area of investigation in psychotherapy research. In this study, we analyzed patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multiple outcome measures for the first time. We used growth mixture modeling to identify different latent classes of early change in interpersonal problems with 212 patients who underwent cognitive-behavioral treatment including interpersonal and emotion-focused elements. Furthermore, videotaped sessions were analyzed using motion energy analysis, providing values for the calculation of nonverbal synchrony to predict early change in interpersonal problems. The relationship between early change patterns and symptoms as well as overall change in interpersonal problems was also investigated. Three latent subgroups were identified: 1 class with slow improvement (n ϭ 145), 1 class with fast improvement (n ϭ 12), and 1 early deterioration class (n ϭ 55). Lower levels of early nonverbal synchrony were significantly related to fast improvement in interpersonal change patterns. Furthermore, such patterns predicted treatment outcome in symptoms and interpersonal problems. The results suggest that nonverbal synchrony is associated with early change patterns in interpersonal problems, which are also predictive of treatment outcome. Limitations of the applied methods as well as possible applications in routine care are discussed.Editor's Note. Sigal Zilcha-Mano served as the action editor for this article.-DMK Jr.

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Section: Quantification Of Nonverbal Synchronymentioning

confidence: 99%

mentioning

confidence: 99%

Patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multidimensional outcome.

Lutz¹,

Prinz²,

Schwartz³

et al. 2020

Journal of Counseling Psychology

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“…Therefore, future studies should examine the nonverbal behavior of patients in different contexts. Altmann [31] found, for example, that 2 interacting students synchronized their body motions more often in positive situations than in negative ones. This possibly holds also for nonverbal behavior related to attachment.…”

Section: Discussionmentioning

confidence: 99%

“…Individuals without a matching partner were excluded. A further exclusion criterion was an interview video unsuitable for the analysis of movements using the Motion Energy Analysis (MEA) [31, 32], for example, due to suboptimal lighting conditions or only partial visibility of the participant. The sample of the present study consisted of 53 healthy individuals, 21 patients with panic disorder and/or agoraphobia, and 21 patients with panic disorder and/or agoraphobia and a comorbid major depression (see Fig.…”

Section: Methodsmentioning

confidence: 99%

Movement and Emotional Facial Expressions during the Adult Attachment Interview: Interaction Effects of Attachment and Anxiety Disorder

et al. 2021

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Introduction: Adult attachment is commonly associated with emotion regulation. Less is known about the nonverbal embodiment of adult attachment. Objective: We hypothesized that dismissing attachment is related to less movement and fewer facial expressions of emotions, whereas preoccupied attachment is associated with more negative emotional facial expressions. Moreover, the interaction of attachment and the presence of an anxiety disorder (AD) was explored. Methods: The sample included 95 individuals, 21 with AD without comorbidity, 21 with AD and comorbid major depression (AD-CD), and 53 healthy controls. We analyzed nonverbal behavior during a part of the Adult Attachment Interview (AAI) asking about the family and parental figures. The movements of the interviewees were captured via Motion Energy Analysis. Facial expressions were coded according to the Facial Action Coding System using the OpenFace software. We compared individuals with secure, dismissing, and preoccupied states of mind (assessed with the AAI) with regard to the frequency and complexity of movements and the frequency of the facial expressions such as happy, sad, and contemptuous. Results: As expected, dismissingly attached individuals moved less often and with lower complexity than securely attached. For emotional facial expressions, a main effect of the disorder group and interaction effects of attachment by disorder were found. In the AD-CD group, dismissingly attached patients showed comparatively fewer happy facial expressions than securely attached individuals. Conclusions: Reduced movement specifically seems to be related to dismissing attachment when interviewees talk about significant parental figures. Facial expressions of emotions related to attachment occurred when maladaptive emotion regulation strategies were intensified by a psychological disorder.

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“…In the case of non-verbal movement synchrony, motion energy analysis has become a widespread tool to quantify movement from video ( Ramseyer, 2013 ). It is made available through standalone software ( Ramseyer, 2019 ), a MATLAB implementation ( Altmann, 2013 ), and an R-package for synchronization analysis and visualization ( Kleinbub and Ramseyer, 2019 ). This allows researchers to engage non-verbal synchrony in an automized, objective, reproducible, and non-labor-intensive fashion in their respective setting and has accelerated research on non-verbal movement synchrony in the clinical dyad ( Delaherche et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research

Fürer¹,

Schenk²,

Röth

et al. 2020

Front. Psychol.

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Speaker diarization is the practice of determining who speaks when in audio recordings. Psychotherapy research often relies on labor intensive manual diarization. Unsupervised methods are available but yield higher error rates. We present a method for supervised speaker diarization based on random forests. It can be considered a compromise between commonly used labor-intensive manual coding and fully automated procedures. The method is validated using the EMRAI synthetic speech corpus and is made publicly available. It yields low diarization error rates (M: 5.61%, STD: 2.19). Supervised speaker diarization is a promising method for psychotherapy research and similar fields.

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Synchronisation nonverbalen Verhaltens

Cited by 25 publications

References 0 publications

Patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multidimensional outcome.

Patterns of early change in interpersonal problems and their relationship to nonverbal synchrony and multidimensional outcome.

Movement and Emotional Facial Expressions during the Adult Attachment Interview: Interaction Effects of Attachment and Anxiety Disorder

Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research

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