Physiological synchrony promotes cooperative success in real-life interactions

Abstract

15interaction. 20 Significance StatementHuman cooperation is remarkable in its scale, complexity, and efficiency. However, whereas people think of themselves as rational agents, they actually partly base their decision to cooperate with someone on gut feelings. These feelings are informed by non-verbal expressions that are picked up implicitly and that synchronize across interaction partners. For the first time, 5 we show that the alignment of people's arousal over multiple rounds of the Prisoners' dilemma game predicts cooperative success. Through synchrony, partners converge emotionally, fostering understanding and bonding, which are key ingredients when it comes to successful cooperation.This suggests that successfully cooperating does not depend on individuals, but on the connection between individuals, emphasizing the importance of studying social decision-making 10 processes in real-life settings. 15 20

Quantifying Physiological Synchrony through Windowed Cross-Correlation Analysis: Statistical and Theoretical Considerations

“…(2) there were differences between the two conditions, but the method was not sensitive to detect them. In support of the first explanation, in two previous studies, we have used parameters included in the current analysis with which we were able to detect differences in within-subject conditions and could link it to interpersonal outcomes (Behrens et al, 2019;Prochazkova, Sjak-Shie, Behrens, Lindh, & Kret, 2019). The method therefore has been shown to be sensitive in other contexts.…”

“…To study the underlying mechanisms of synchrony, its boundary conditions and individual differences, researchers are often interested in how synchrony changes in relation to experimental manipulations. For example, in a previous study, we observed that physiological synchrony promoted cooperative success, but only when partners could see each other and not when a cover prevented eye contact (our manipulation) (Behrens et al, 2019). Another study investigated the effect of emotional salience during storytelling on pupil mimicry and showed that physiological coupling between the speaker and the listener was stronger during emotionally intense moments compared to less salient moments (Kang & Wheatley, 2017).…”

“…In Kang and Wheatley's (2017) study, listeners watched videos of speakers telling the story and therefore did not engage in an actual conversation. However, direct face-to-face interactions has been shown to affect synchrony levels (Behrens et al, 2019). Therefore, in the current study, two individuals engaged in face-to-face storytelling and completed baseline measures, silent moments of eye-contact.…”

“…Not surprising then, the causes and consequences of synchrony have been studied in a broad range of contexts investigating the dynamic nature of social interactions from clinical (Galazka et al, 2019;Wehebrink, Koelkebeck, Piest, De Dreu, & Kret, 2018), developmental (de Klerk, Hamilton, & Southgate, 2018;Shih, Quiñones-Camacho, Karan, & Davis, 2019), evolutionary (Mancini, Ferrari, & Palagi, 2013;Palagi, Leone, Mancini, & Ferrari, 2009), neural (Hasson, Nir, Levy, Fuhrmann, & Malach, 2004;Prochazkova et al, 2018), social (Behrens et al, 2019;Tarr, Launay, & Dunbar, 2016), and cognitive (Kret, Fischer, & De Dreu, 2015;Kret & De Dreu, 2017) perspectives. Such fascination across disciplines has unraveled the far-reaching scope of synchrony: it has been demonstrated in different species, it occurs from birth on, and it influences a variety of interpersonal processes such as marital quality, cooperative success between strangers and outcomes of therapeutic interactions (Behrens et al, 2019;Feldman et al, 2011;Kret, Tomonaga, & Matsuzawa, 2014;Levenson & Gottman, 1983;Ramseyer & Tschacher, 2011). Because of these QUANTIFYING PHYSIOLOGICAL SYNCHRONY 4 implications and this wide interest, it is of particular importance to establish solid statistical methods to quantify synchrony.…”

Extracting Nonlinear Dynamics from Psychological and Behavioral Time Series Through HAVOK Analysis

“…The MEA software tool utilizes pixel differencing methods to quantify motion in selected regions of a video stream. MEA has been used in a number of behavioral studies with topics ranging from patients and care-provider interactions in psychotherapy sessions and medical settings, to team-based cooperative problem solving (e.g., Behrens et al, 2019;Hamel et al, 2018;Nelson, Grahe, Ramseyer, & Serier, 2014). Recently, Daniel et al (2020) used ecological momentary assessment (EMA) methodologies to model the effect of cognitive bias modification strategies on the behaviors of individuals high in social anxiety.…”