DOI: 10.5061/dryad.g79cnp5zf
title: Social bonding through shared experiences: The role of emotional intensity

description: Sharing emotions with other individuals is a widespread phenomenon.
 Previous research proposed that experiencing intense and similar emotions
 with other individuals reinforces social bonds. However, several aspects
 of this phenomenon remain unclear, notably whether social bonding requires
 the convergence and synchronisation of emotions in the group, and whether
 these effects generalise across positive and negative emotional contexts.
 To address these questions, we measured subjective emotional experiences,
 physiological activity (cardiac, respiratory, electrodermal) and social
 attitudes in dyads of unacquainted individuals who watched videos in the
 presence of each other. We manipulated the emotional content of the videos
 and the type of shared attention between participants, to test for the
 contribution of interpersonal influence. The results revealed that intense
 emotions indexed by physiological arousal were associated with the
 emergence of reciprocal prosocial attitudes within dyads, and that this
 effect depended on joint attention. We did not observe the convergence and
 synchronisation of emotions within dyads, which suggests that experiencing
 similar emotions was not necessary for social bonding. We discuss
 implications of this study for research on collective effervescence and
 the social consequences of shared experiences.
description: # Social bonding through shared experiences Access datasets on Dryad:
 [https://doi.org/10.5061/dryad.g79cnp5zf](https://doi.org/10.5061/dryad.g79cnp5zf) --- ## **General information** ### Data collection Paris, France, 2020 ### Language English ### Content This repository includes (1) anonymized individual pre-processed psychophysiological data (`data_table.csv`) and their description (`data_dictionary.csv`), and (2) anonymized individual raw and preprocessed physiological time series (`physiology.mat`). Physiological data include cardiac activity (ECG), respiratory activity (RSP) and electrodermal activity (EDA) recorded over five-minute experimental conditions (resting state, positively valenced video, negatively valenced video, and neutral control video). We have also provided R and MATLAB scripts for preprocessing physiological time series and evaluating the effect of emotional valence on psychophysiological data. ### De-Identification The data set has been modified in the following ways to prevent the indirect re-identification of the participants: (1) the participants' gender has been masked; (2) the participants' age in years has been replaced with categorical values corresponding to age brackets; (3) the information about the participants' highest education diploma has been removed. ### Usage notes Microsoft Excel can be used to view `data_table.csv` and `data_dictionary.csv`. MATLAB is required to open `physiology.mat`. --- ## **Data** ### data\_table.csv CSV file (semicolon separated) including anonymized individual pre-processed data (demographics, self-reports, physiology). Each row corresponds to a participant and each column corresponds to a variable from the following categories: identifier, experimental cluster, demographics, pre-test and post-test measures, self-report repeated-measures, physiological repeated-measures, estimates of physiological synchrony. Empty cells are marked with "NA" when the index could not be computed (i.e., the mean prominence of phasic electrodermal peaks could not be computed when there was no peak or the indices of respiratory and electrodermal synchrony could not be computed when one of the co-participants' signal had artefacts). All of the variables are described in the file `data_dictionary.csv`. ### data\_dictionary.csv CSV file (semicolon separated) providing information about the variables from the file `data_table.csv`. The first column contains the variable name that appears as a column header. The second column contains a readable plot-friendly version of the variable name. The third column groups the variables into different categories. The fourth column describes the data type, and the fifth column describes the unit. The sixth column is a description of the variable. ### physiology.mat MATLAB file including anonymized individual physiological time series. The MATLAB data structure is divided into "raw" and "preprocessed" data. #### Raw data * The "raw" data structure is divided into embedding hierarchical structures for each type of signal: cardiac (ecg), respiratory (rsp), electrodermal (eda). Each structure is then sub-divided into individual structures for each participant and organized as per LabChart's default output. * The data is contained in a row vector. All the samples from each block and channel are combined and sequentially output to the Data Vector by first stepping through the channels and then through the blocks. Empty channels will be skipped over to keep the size small and increase performance for large amounts of data. * The "titles" vector is a column vector containing strings of each Channel name. * The data start, and end positions of each block and channel in the Data Vector are contained in separate matrices called "datastart" and "dataend". Because columns represent blocks and rows represent channels, each block and channel can be addressed in the data vector. The vector "blocktimes" is a row vector that contains the time of day at which the first sample in each block was recorded. * The vector "tickrate" is a column vector containing each block's maximum sample rate. The sampling rate of each block and channel in the Data Vector are contained in the "samplerate" matrix in units of samples per second. * Comment information is conveyed in the "com" matrix and the "comtext" column vector. The "com" matrix has the dimension of "number of comments" by five. Each comment corresponds to one row in the matrix and is sorted by time from the start of the file or selection. #### Preprocessed data * The "physiology_preproc" data structure is divided into three embedding hierarchical structures for each type of signal: cardiac (ECG, Interbeat intervals after interpolation, cubic spline, resampling at 20Hz), respiratory (RSP, circumference of the participant’s chest as a proxy for tidal volume), electrodermal (EDA, skin conductance after correction of artefacts with interpolation, cubic spline, resampling at 20Hz). Each structure is then sub-divided into "data" and "info". * The "data" structure is sub-divided into arrays per type of experimental condition: "fixation" (resting state), positive (video), negative (video), neutral (video). Physiological time series are stored as numerical values in a 112x6,000 array with rows corresponding to participants and columns corresponding to data samples. The "label" structure contains a 112x1 array with participants' codes. * The "info" structure contains the sample rate ("fsample"), the name of the signal ("signal") and its unit ("unit"). --- ## Code/Software ### Preprocessing of physiological time series MATLAB scripts are provided for the preprocessing of (1) cardiac activity (`preproc_ecg.m`), (2) respiratory activity (`preproc_rsp.m`) and (3) electrodermal activity (`preproc_eda.m`), which involves two functions for detecting cardiac peaks (`ecg_peak_detect.m`) and respiratory peaks (`rsp_peak_detect.m`). These scripts were created using MATLAB R2019b. ### Computation of physiological synchrony MATLAB scripts are also provided for (1) the computation of synchrony for experimental dyads (`synchrony_experimental.m`); (2) the generation of a list of surrogate dyads whose members did not take part in the experiment together (`list_surrogate.m`); (3) the computation of synchrony for surrogate dyads (`synchrony_surrogate.m`); (4) the comparison of experimental and surrogate synchrony thanks to non-parametric bootstrapping (`synchrony_bootstrap.m`). These scripts were created using MATLAB R2019b. ### Statistical analyses The R script `analyses.R` was created using version 4.2.1. The script takes as input the file `data_table.csv` and evaluates the effects of emotional valence (positive, neutral, negative) and attention type (joint, disjoint) on measures of subjective emotional experience, peripheral physiology and social attitudes at dyad-level.