The Impact of Inter-brain Synchrony on Experience Sharing and Empathic Accuracy
Creators: name: Lin, Tong
name: Gutsell, Jennifer
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social and Behavioral Sciences

description: Study Information Title: The Impact of Inter-brain Synchrony on Experience Sharing and Empathic Accuracy Main objective of the research: To investigate how inter-brain synchrony affects empathic accuracy during experience sharing. Brief Background section to justify Hypotheses : In recent years, there has been an increased interest in the field of social neuroscience to use inter-brain synchrony, which is the synchronization between two different individuals’ brains (Müller, Sänger, &amp; Lindenberger, 2013), to study social phenomena: fMRI, EEG and fNIRS studies have linked inter-brain synchrony with various social factors(Nam at al., 2020): a few recent studies have linked inter-brain synchrony with increased social connectedness (Kinreich, Djalovski, Kraus, Louzoun, Feldman, 2017), cooperation more generally (Hu, Pan, Shi, Cai, Li, Cheng, 2018), collaborative problem solving more specifically (Antonenko, Davis, Wang, Celepkolu, 2019), affect sharing (Anders et al., 2020; Kinoshita et al., 2019) and empathic accuracy regarding pain (Goldstein, Weissman-fogel, Dumas Shamay-tsoory, 2018). All these studies suggest that synchrony might be related to empathic accuracy for emotional experiences, but to our knowledge no study has tested this relationship. Emotional expressivity might also have an impact on empathic accuracy as previous studies suggest that individuals tend to empathize with targets who are more emotional expressive (Zaki, Bolger, &amp; Ochsner, 2009; Lee, Zaki, Harvey, Ochsner, &amp; Green, 2011). Also, no study has looked at how both neural synchrony and emotional expressivity might interact to affect how well people understand others’ feelings in social interactions. A recent review of hyper-scanning in interpersonal interaction found that the mirror neuron system, which involves similar neural activation patterns during both the experience and the observation of the experience in another, is typically involved in inter-brain synchrony (Wang et al., 2018, as cited in Mayseless, Hawthorne, &amp; Reiss, 2019). Emotional expressivity can potentially reflect mirroring. Therefore, this study intends to explore how inter-brain synchrony reflets the potential connection between experience sharing and empathic accuracy in social interactions. Research Questions: In this study, we will explore people’s ability to understand another’s emotions by combining a face-to-face interaction with a relatively new neural method, which is EEG hyper-scanning, a technique that measures brain activity of two or more participants at the same time (Liu, Liu, Liu, Zhang, Jin, &amp; Zhang, 2018). In the experiment, the participants were randomly paired with another participant and shared one positive and one negative experience with their partner. Their experience sharing was video recorded. Later they watched their own and their partner’s video and rated the emotion expressed by their partner in the videos. Then we followed a method used by a previous study (Ong, Zaki, &amp; Perry, 2018, preprint) to calculate the similarity between their and their partner’s emotion rating scores to estimate their empathic accuracy, the extent to which they understand each other’s emotions. While the participants were sharing their stories, their neural EEG activities was recorded in order to later calculate for inter-brain synchrony. We will use PLV as the synchrony index. For emotional expressivity, we will use the frequency of facial expressions in each video as the index of emotional expressivity. Our research questions are: How does emotional expressivity influence empathic accuracy? Does neural synchrony predict empathic accuracy? And can the association between emotional expressivity and empathic accuracy be explained by neural synchrony? Hypotheses Hypothesis 1: Emotion expressions increase synchrony. Hypothesis 2: Emotion expressions predicts empathic accuracy. Hypothesis 3: Interbrain synchrony (measured through PLV) mediates a positive relationship between speaker’s expressivity and empathic accuracy. Hypothesis 4: Interbrain synchrony (measured through PLV) mediates the relationship between listener’s expressivity and empathic accuracy, controlling for the effect of emotional content on empathic accuracy. Sampling Plan Explanation of existing data This study is part of a larger project on empathy and interaction quality during which dyads of participants completed various tasks and questionnaires, only a subset of which are included in this study. In addition to the tasks in this study, participants also completed a ball squeezing task, during which they took turns repeatedly squeezing and observing their partner squeezing a stress ball, and an ice breaker task during which they took turns asking and answering a set of mundane questions, before they completed the experience sharing task. In 50% of the cases, they also completed an action coordination task before experience sharing. For the other 50% they did the action coordination after experience sharing but before the measurement of empathic accuracy. All dyads also did another round of ball squeezing before they were separated into individual testing rooms. Data on human uniqueness, mu suppression during ball squeezing and empathic accuracy have been published on Simon, J. C., &amp; Gutsell, J. N. (2021). Recognizing humanity: dehumanization predicts neural mirroring and empathic accuracy in face-to-face interactions. Social cognitive and affective neuroscience, 16(5), 463-473. Two existing pre-registrations that concern data from the big project have also been created: https://osf.io/awd4g https://osf.io/72atz Neither emotional expressivity nor EEG synchrony during the experience sharing task have been analyzed or viewed by the researchers. Data collection procedures Participants were recruited through Craigslist and flyering around the university campus and surrounding community, and dyads were age-matched within 10 years when possible. Sample size 38 dyads Power analysis: Based on power simulations (with 1000 replications, ICC= 0.1) using SIMR package in R, we will need 45 dyads to achieve 80% power to detect a medium effect (r = .4). Variables Measured variables Inter-brain synchrony, empathic accuracy, Expressivity of the emotional sharing, Emotional Content of the Conversation Inter-brain Synchrony: We will explore how synchrony is related to emotion sharing and empathic accuracy in a general sense using averages across entire task segments through PLV. PLV will be computed according to this formula (Jian, Chen, &amp; McFarland, 2017; Celka, 2007, Lachaux et al., 1999 as cited in Aydore, Pantazis, &amp; Leahy, 2013): where ⟨ ⟩ stands for measuring the mean value of the computed window. φi, φj are the phase of EEG signals of channel i and j, computed by the Hilbert transformation. We will choose all 31 channels, which will result in a 31 × 31 matrix for each trial. This synchronization calculation will be done for all pairs of participants, resulting in 38 inter-brain connectivity matrices, using the same method as in Zhu, Su, Zhang, Cui, Cichocki, Zhou, &amp; Li, 2020. PLV will be calculated for the theta-(4–8 Hz), alpha-(8–13 Hz), beta-(13–30 Hz) and gamma-(30–80 Hz) band using a Butterworth bandpass-filter. Since the interbrain synchrony literature is quite varied in terms of electrode locations. We will take a data-driven approach to interbrain synchronization and model on Lu and Hao (2019)’s method: we will calculate PLVs for each of the four frequency bands (theta, alpha, beta, gamma) and all electrode combinations (31 x 31) averaged across every dyad the experience sharing tasks. We will then compare each PLV value to a permuted null formed by randomly pairing non-partnered participants’ data during the same task using 10000 bootstrapped permutations, thresholded at α = .0005. These averages for each dyad form our index of interbrain synchrony. In other words, each dyad’s interbrain synchrony will be the average PLV calculated over the entire experience sharing section from the pairwise electrode combinations that were significant for actual versus shuffled dyads across all participants. Empathic Accuracy Task (adapted from Zaki et al., 2008): Participants watched their partner and their own videos after the ball squeezing task. While watching the video, they rated the emotions of their partner and themselves on a rating bar of a 9-point Likert scale from “very negative” to “very positive” through arrow keys: clicking the left arrow would mean feeling more negative emotions and clicking the right arrow would indicate more positive emotion. We will follow how previous studies calculated empathic accuracy: we will operationalize empathic accuracy by quantifying participants’ agreement on how emotional content of the video changes (Ong et al., 2018, preprint). For each one-second interval, we will classify participants' ratings into one of three categories: either an increase in affect, a decrease, or maintained from the previous epoch. Specifically, we defined deltaPart(t) as a scale-invariant derivative: deltaPart(t) = {"increase", if part(t) - part(t-1) &gt; 0 {"maintain", if part(t) - part(t-1) == 0 { "decrease", if part(t) - part(t-1) &lt; 0 We will define a corresponding variable, deltaTarg(t), using the target's self-reported ratings (Ong et al., 2018, preprint). We will then operationalize change detection such that a "successful" change detection occurs if participants' rated change matches the target's change at that second. If they do not match, this would be a "failed" change detection (Ong et al., 2018, preprint). changeDetection(t) = {1, if deltaPart(t)==deltaTarg(t) { 0, otherwise Therefore, change detection will be a binary variable for each one-second interval that reflects whether the participant successfully detected any change in the target's affect (Ong et al., 2018, preprint). Last, we will average across the entire time period of each shared experience, so the final score will range from 0 to 1. Then we will create averages of the positive and the negative experience for each participant, so the final score for empathic accuracy will be a continuous variable ranging from 0 to 1. Expressivity of the emotional sharing: each video for both speakers and listeners in both positive sharing condition and negative sharing condition will be coded by two research assistants using The Facial Expression Coding System (FACES) (Kring, &amp; Sloan, 1991). The subcomponents of facial expression coding include frequencies, duration, intensities, and valence of the expressions. When raters detect a change in the participant’s facial expression on the video, they will write down when this change occurs and ends, as well as its intensity and valence (either positive or negative). Raters will be trained to use the FACES. We also aim to have an inter-rater reliability higher than 80%. For videos with low agreements between the two raters who are assigned to code them, we will have those videos recoded by two more raters and finalize the coding based on the majority of the raters’ judgement. The minimum rating for frequency, duration and intensity will be zero and there will be no upper limit. Following previous studies that used this scale (Kring, &amp; Sloan, 2007; Kring, &amp; Gordon, 1998), we will only use frequency to reduce the number of dependent variables in the analysis. Also, the other two components are strongly correlated with frequency. We will use the total frequency of positive expressions as an index of each participant’s emotional expressions, while for the negative condition, we will use the total frequency of negative expressions as an index of each participant’s emotional expressions. We will also average the frequencies of both positive and negative facial expressions in both positive and negative conditions to calculate the average emotional expressivity of each participant. Study type Experiment Blinding Experimenters are aware of the experimental manipulations, but study has a within subject design. Study design Within subject design. Randomization No Analysis Plan: EEG-pre-processing steps EEG was recorded from 31 active electrodes embedded in a stretch-lycra cap (ActiCap, BrainProducts GmbH, Munich, Germany) arranged according to the 10–20 system with impedances kept below 10 kΩ. The EEG was digitized at 250 Hz using BrainAmp amplifiers and the BrainVision recorder software (BrainProducts GmbH, Munich, Germany) with an initial reference at FCz, later rereferenced offline to an average reference with EEGLAB (Delorme and Makeig, 2004). During the interaction, each subject was connected through an individual electrode input box to their own amplifier, allowing for individual reference and ground electrodes. The amplifiers were connected to the same USB 2 Adapter (BUA; BrainProducts, GmbH, Munich Germany) for synchronization between amplifiers. EEG recordings will be sectioned into non-overlapping activity-locked segments before further processing, which will be proceeded with custom 2018b MATLAB (Math-Works) scripts implementing EEGLAB functions. Unusable channels will be identified by visual inspection of raw data, excluded and replaced with interpolated channels using the ‘spherical’ method in the EEGLAB function pop_interp() (Delorme and Makeig, 2004). Data will be filtered with a bandpass between 1 and 30 Hz and line noise will be removed using pop_cleanline(). We will also use prep_pipeline and clean_rawdata for bad channel removal, and low frequency drifts and artifact correction. Independent components will be identified with the second-order blind identification algorithm, then categorized using ICLabel(), an algorithm trained to classify components in EEG data (Pion-Tonachini et al., 2019). Those given a greater than 50% chance of being ocular, muscular or ‘other’ artifacts will be removed. Remaining bad epochs will then be removed with the pop_eegthresh() function, which will take out 1 s epochs containing a change in amplitude greater than 100 μV. EEG-analysis steps: We will do two kinds of analyses: a data-driven whole brain analysis, and a region of interest based analysis where we’ll test our hypotheses for our ROIS. 1. We will choose all 31 channels, which will result in a 31 × 31 matrix for each trial. This synchronization calculation will be done for all 38 pairs of participants, resulting in 35 inter-brain connectivity matrices, using the same method as in Zhu, Su, Zhang, Cui, Cichocki, Zhou, &amp; Li, 2020. We will take a data-driven approach to interbrain synchronization and model on Lu and Hao (2019)’s method: we will calculate PLVs and PLIs respectively for each of the four frequency bands (theta, alpha, beta, gamma) and all electrode combinations (31 x 31) averaged across every dyad the experience sharing tasks. We will then compare each PLV and PLI value to a permuted null formed by randomly pairing non-partnered participants’ data during the same task using 10000 bootstrapped permutations, thresholded at α = .0005. These averages for each dyad form our index of interbrain synchrony. In other words, each dyad’s interbrain synchrony will be the average PLV calculated over the entire experience sharing section from the pairwise electrode combinations that were significant for actual versus shuffled dyads across all participants. 2. ROIS: Our frequencies of interest are gamma (28–40 Hz) and alpha (8 – 13 Hz). So we plan to measure gamma synchrony and alpha synchrony. Statistical models Statistical models: Hypothesis 1—effect of expressivity (speaker &amp; listener) on synchrony— We will model synchrony in a 2-level multilevel model where speaker and listener expressivity scores are nested within dyads. A main effect of expressivity could support the prediction that participants will synchronize with their partners more when they display more facial expressions. Directionality will be tested with planned comparisons. Hypothesis 2—effect of expressivity (speaker &amp; listener) on empathic accuracy— We will model empathic accuracy in a 2-level multilevel model where speaker and listener expressivity scores are nested within dyads. A main effect of expressivity could support the prediction that participants will empathize with their partners more effectively when they display more facial expressions. Directionality will be tested with planned comparisons. Hypothesis 3: Interbrain synchrony (measured through PLV) mediates a positive relationship between speaker’s expressivity and empathic accuracy. We will first test the effect of expressivity on empathic accuracy. Next, we will test the effect of expressivity on synchrony. Then, we will test how expressivity and synchrony together influence empathic accuracy. Hypothesis 4: Interbrain synchrony (measured through PLV) mediates a positive relationship between listener’s expressivity and empathic accuracy. We will first test the effect of expressivity on empathic accuracy. Next, we will test the effect of expressivity on synchrony. Follow-up analyses Will use Bayesian Analysis for null effects Follow up analysis for the synchrony: Temporal-parietal gamma activity has been linked to mentalizing and experience sharing, and gamma interbrain synchrony is stronger during episodes of positive interaction behavior. Suppression of the mu-alpha band over sensorimotor areas has been linked to experience sharing and empathic concern, and alpha-band inter-brain synchrony predicts social coordination. So we plan to measure temporal-parietal gamma synchrony and sensorimotor alpha/mu synchrony. It is possible that the relationships between synchrony and expressivity are bi-directional, so we will test whether synchrony predicts expressivity of both interaction partners in two separate regression models. Inference criteria p-value: 0.05 Data exclusion Initial EEG data exclusions will be done through visual inspection. Participants will be excluded for having overly noisy EEG data (i.e. incurable line noise, more than six unusable channels) or missing segment markers in their EEG data.