Embodied mood regulation: the impact of body posture on mood recovery, negative thoughts, and mood-congruent recall

Veenstra, Lotte; Schneider, Iris K.; Koole, Sander L.

doi:10.1080/02699931.2016.1225003

Cited by 73 publications

(46 citation statements)

References 42 publications

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“…Riskind (1983) reported quicker recall for pleasant memories when participants sat in an upright posture, but quicker recall for negative events (both in quantity and intensity) when in a slumped posture. Similar effects on cognition and memory have been found, where the posture adopted by an individual (upright or slumped) directly affected performance on valence words tasks (Michalak, Mischnat, & Teismann, 2014), and mood-recovery interventions (Veenstra, Schneider, & Koole, 2017).…”

Section: Body Posturessupporting

confidence: 63%

Sitting in judgment: How body posture influences deception detection and gazing behavior

Zloteanu¹,

Krumhuber²,

Richardson³

2019

Preprint

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Body postures can affect how we process and attend to information. Here, a novel effect of adopting an open or closed posture on the ability to detect deception was investigated. It was hypothesized that the posture adopted by decoders would affect their social acuity, resulting in differences in the detection of nonverbal behavior and discrimination of deceptive and truthful statements. In Study 1, adopting an open posture produced higher accuracy for detecting naturalistic lies, but no difference in the recognition of brief facial expressions, as compared to adopting a closed posture. Study 2 measured differences in gaze behavior based on posture when detecting both low and high stakes lies. Sitting in an open posture reduced visual attention towards senders, and in particular, the attention given to their hands. The findings suggest that simply shifting posture can impact veracity judgments and the way they attend to visual information.

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Section: Body Posturessupporting

confidence: 63%

Sitting in judgment: How body posture influences deception detection and gazing behavior

Zloteanu¹,

Krumhuber²,

Richardson³

2019

Preprint

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show abstract

“…Previous studies have utilized a contrast experimental paradigm to manipulate the following participants’ moods: positive versus negative mood [2]; negative or neutral [31]; positive, negative, and neutral [32,33] using music or avatars. Past research findings indicate that negative moods tend to reduce mood recovery and a slower response for accurately identifying other emotional expressions [20,31].…”

Section: Discussionmentioning

confidence: 99%

“…Past research findings indicate that negative moods tend to reduce mood recovery and a slower response for accurately identifying other emotional expressions [20,31]. Although these user studies did not apply to emotion recognition from sensor data obtained from a smart watch, we did not address issues, such as reduced mood recovery, for participants who were shown the sad stimulus first; however, we did perform counterbalancing for our stimuli on our participants.…”

Section: Discussionmentioning

confidence: 99%

Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

2018

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BackgroundResearch in psychology has shown that the way a person walks reflects that person’s current mood (or emotional state). Recent studies have used mobile phones to detect emotional states from movement data.ObjectiveThe objective of our study was to investigate the use of movement sensor data from a smart watch to infer an individual’s emotional state. We present our findings of a user study with 50 participants.MethodsThe experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual “movie clips” and audio “music clips”). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items; Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an individual’s emotion.ResultsOverall, 50 young adults participated in our study; of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P<.006. For the task of emotion recognition using classifiers, our results showed that personal models outperformed personal baselines and achieved median accuracies higher than 78% for all conditions of the design study for binary classification of happiness versus sadness.ConclusionsOur findings show that we are able to detect changes in the emotional state as well as in behavioral responses with data obtained from the smartwatch. Together with high accuracies achieved across all users for classification of happy versus sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.

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“…This early activation was correlated to arousal ratings and heart beats changes (Rudrauf et al, 2009). Also, it is known that bodily movements can actively influence emotions (Strack et al, 1988; Niedenthal et al, 2005), the manipulation of body posture can alter the regulation of mood (Veenstra et al, 2016), and intentional movement can regulate emotional states (Shafir et al, 2013). From this, even more relevant is the fact that previous emotional states can strongly influence cognitions and attention processes (Okon-Singer et al, 2015), which then will drive the emotion regulation process.…”

Section: Integrating Psychological Clinical and Neuroscience Evidencmentioning

confidence: 99%

Mindfulness and Emotion Regulation: Insights from Neurobiological, Psychological, and Clinical Studies

2017

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There is increasing interest in the beneficial clinical effects of mindfulness-based interventions (MBIs). Research has demonstrated their efficacy in a wide range of psychological conditions characterized by emotion dysregulation. Neuroimaging studies have evidenced functional and structural changes in a myriad of brain regions mainly involved in attention systems, emotion regulation, and self-referential processing. In this article we review studies on psychological and neurobiological correlates across different empirically derived models of research, including dispositional mindfulness, mindfulness induction, MBIs, and expert meditators in relation to emotion regulation. From the perspective of recent findings in the neuroscience of emotion regulation, we discuss the interplay of top-down and bottom-up emotion regulation mechanisms associated with different mindfulness models. From a phenomenological and cognitive perspective, authors have argued that mindfulness elicits a “mindful emotion regulation” strategy; however, from a clinical perspective, this construct has not been properly differentiated from other strategies and interventions within MBIs. In this context we propose the distinction between top-down and bottom-up mindfulness based emotion regulation strategies. Furthermore, we propose an embodied emotion regulation framework as a multilevel approach for understanding psychobiological changes due to mindfulness meditation regarding its effect on emotion regulation. Finally, based on clinical neuroscientific evidence on mindfulness, we open perspectives and dialogues regarding commonalities and differences between MBIs and other psychotherapeutic strategies for emotion regulation.

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Embodied mood regulation: the impact of body posture on mood recovery, negative thoughts, and mood-congruent recall

Cited by 73 publications

References 42 publications

Sitting in judgment: How body posture influences deception detection and gazing behavior

Sitting in judgment: How body posture influences deception detection and gazing behavior

Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

Mindfulness and Emotion Regulation: Insights from Neurobiological, Psychological, and Clinical Studies

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