Dynamics and diversity of heart rate responses to a disaster motion picture

Multilevel analysis of positive emotional induction: Which is the role of gender and valence on cognitive control processes?

How emotions are processed affects people's cognition and behavior. One of the most used measures to study the physiological component of emotions is heart rate (HR), where greater HR variation (range) implies greater emotional processing. Several investigations established the importance of gender modulation of these processes due to the different cultural treatment given to emotions among genders. However, very heterogeneous results are reported today, and few studies incorporated these parameters when investigating emotion modulation of cognitive processes according to individual factors. The present work consists of two studies. In a pilot study, 97 young adults, through the Self-Assessment Manikin (SAM), ranged the valence and arousal to select two positive and neutral videos. The second study analyses the role of gender and valence in the variation of subjective (SAM) and physiological (HR) indicators of emotional processing associated with performance in two cognitive tasks (Stroop and Tower of London) under two different emotional conditions. Participants were 60 adults, who were randomly assigned to positive or neutral video visualization, and their HR was registered. There was a tendency for women to have higher HR during positive videos than in neutral. While the subjective reports indicated variances in the appraisal of the videos, the heart rate did not exhibit differences across the experimental conditions. Few studies integrate emotional and cognitive components, and include physiological and subjective measures of emotional processing. These findings illustrate the importance of including various measures of emotional and cognitive processing.

Weakened sympathetic response and lower parasympathetic activity in intimate partner violence perpetrators when empathizing: Influence of autonomous activation in affective approach and prosocial behavior

The autonomic nervous system (ANS) functioning has been proposed as a relevant method to characterize the therapeutic needs of intimate partner violence (IPV) perpetrators. Nevertheless, research has neglected the influence of the ANS on socio-affective functions in this population. The aim of the present study was to analyze the psychophysiological activity of IPV perpetrators (n = 52) compared to controls (n = 46) following an empathic induction task, performed through negative emotion-eliciting videos. We employed two general ANS markers (heart rate [HR] and respiratory rate [RR]), two sympathetic-related indexes (pre-ejection period [PEP] and skin conductance level [SCL]) and a parasympathetic biomarker (respiratory sinus arrhythmia [RSA]). Additionally, we explored the impact of psychophysiological activity on prosocial behavior using Hare's donation procedure. Compared to controls, IPV perpetrators reported lower HR and SCL following the task, as well as longer PEP, suggesting an attenuated sympathetic response to others' distress. No differences in the RSA response pattern were found, however, IPV perpetrators displayed lower overall RSA levels throughout the protocol, indicative of reduced parasympathetic activity. Besides, while no differences in prosocial performance were observed, greater sympathetic responses and overall parasympathetic activity predicted increased donations across the sample. Thus, a high sympathetic and parasympathetic activity might influence the occurrence of prosocial behavior. The present study provides further evidence supporting that IPV perpetrators cope differently with others' negative emotions. In line with this biopsychosocial perspective, insights are gained on the emotional processing of IPV perpetrators which, in turn, could contribute to improve IPV psychotherapeutic programs.

Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses

Two people's physiological responses become more similar as those people talk or cooperate, a phenomenon called physiological synchrony. The degree of synchrony correlates with conversation engagement and cooperation quality, and could thus be used to characterize interpersonal interaction. In this study, we used a combination of physiological synchrony metrics and pattern recognition algorithms to automatically classify four different dyadic conversation scenarios: two-sided positive conversation, two-sided negative conversation, and two one-sided scenarios. Heart rate, skin conductance, respiration and peripheral skin temperature were measured from 16 dyads in all four scenarios, and individual as well as synchrony features were extracted from them. A two-stage classifier based on stepwise feature selection and linear discriminant analysis achieved a four-class classification accuracy of 75.0% in leave-dyad-out crossvalidation. Removing synchrony features reduced accuracy to 65.6%, indicating that synchrony is informative. In the future, such classification algorithms may be used to, e.g., provide real-time feedback about conversation mood to participants, with applications in areas such as mental health counseling and education. The approach may also generalize to group scenarios and adjacent areas such as cooperation and competition.

It does not need two: Assessing physiological linkage from videos across the valence dimension

The phenomenon of physiological linkage describes similar fluctuations of two individuals' physiology, for example, the cardiac inter-beat interval (IBI). Physiological linkage is a well-documented occurrence in research settings of interacting dyads but the literature on non-interacting dyads, that is, someone watching a video of another person, is sparse. The current study investigated whether physiological linkage, based on IBI, occurs from watching videos where strangers report about personal (neutral, positive, negative non-traumatic, and negative traumatic) experiences. Videos were produced with six individuals and then presented to observers (N = 26). Time-frequency-domain cross-wavelet analyses supplemented by threshold-free cluster enhancement (TFCE; to account for multiple testing) showed significant physiological linkage between the IBI of observers and persons in the videos for 16 out of the 21 tested videos. Although significant physiological linkage also emerged for neutral videos and positive, negative valence videos led to such associations more reliably. This study shows that physiological linkage can be investigated in highly controlled conditions based on video stimuli paving the path for experimental manipulation in future research. Furthermore, due to the provision of information on time and frequency, the use of cross-wavelet analysis is encouraged to learn more about factors modulating physiological linkage. The current study presents the next step toward identifying psychophysiological causal and modulating factors of physiological linkage.

Robustness of Physiological Synchrony in Wearable Electrodermal Activity and Heart Rate as a Measure of Attentional Engagement to Movie Clips

Individuals that pay attention to narrative stimuli show synchronized heart rate (HR) and electrodermal activity (EDA) responses. The degree to which this physiological synchrony occurs is related to attentional engagement. Factors that can influence attention, such as instructions, salience of the narrative stimulus and characteristics of the individual, affect physiological synchrony. The demonstrability of synchrony depends on the amount of data used in the analysis. We investigated how demonstrability of physiological synchrony varies with varying group size and stimulus duration. Thirty participants watched six 10 min movie clips while their HR and EDA were monitored using wearable sensors (Movisens EdaMove 4 and Wahoo Tickr, respectively). We calculated inter-subject correlations as a measure of synchrony. Group size and stimulus duration were varied by using data from subsets of the participants and movie clips in the analysis. We found that for HR, higher synchrony correlated significantly with the number of answers correct for questions about the movie, confirming that physiological synchrony is associated with attention. For both HR and EDA, with increasing amounts of data used, the percentage of participants with significant synchrony increased. Importantly, we found that it did not matter how the amount of data was increased. Increasing the group size or increasing the stimulus duration led to the same results. Initial comparisons with results from other studies suggest that our results do not only apply to our specific set of stimuli and participants. All in all, the current work can act as a guideline for future research, indicating the amount of data minimally needed for robust analysis of synchrony based on inter-subject correlations.

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage

Aggregated community-scale data could be harnessed to provide insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During the winter storm that brought historically low temperatures, snow, and ice to the entire state of Texas in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent collapse of the power grid when power demand overwhelmed supply. To reveal the disparate impact of managed power outages on vulnerable subpopulations in Harris County, Texas, which encompasses the city of Houston, we collected and analyzed community-scale big data using statistical and trend classification analyses. The results highlight the spatial and temporal patterns of impacts on vulnerable subpopulations in Harris County. The findings show a significant disparity in the extent and duration of power outages experienced by low-income and minority groups, suggesting the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access, as a proxy for storm impact, were more severe for low-income and minority groups. Insights provided by the results could form a basis from which infrastructure operators might enhance social equality during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.

Cognitive processing of a common stimulus synchronizes brains, hearts, and eyes

Neural, physiological, and behavioral signals synchronize between human subjects in a variety of settings. Multiple hypotheses have been proposed to explain this interpersonal synchrony, but there is no clarity under which conditions it arises, for which signals, or whether there is a common underlying mechanism. We hypothesized that cognitive processing of a shared stimulus is the source of synchrony between subjects, measured here as intersubject correlation (ISC). To test this, we presented informative videos to participants in an attentive and distracted condition and subsequently measured information recall. ISC was observed for electro-encephalography, gaze position, pupil size, and heart rate, but not respiration and head movements. The strength of correlation was co-modulated in the different signals, changed with attentional state, and predicted subsequent recall of information presented in the videos. There was robust within-subject coupling between brain, heart, and eyes, but not respiration or head movements. The results suggest that ISC is the result of effective cognitive processing, and thus emerges only for those signals that exhibit a robust brain-body connection. While physiological and behavioral fluctuations may be driven by multiple features of the stimulus, correlation with other individuals is co-modulated by the level of attentional engagement with the stimulus.

Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony

Research on brain signals as indicators of a certain attentional state is moving from laboratory environments to everyday settings. Uncovering the attentional focus of individuals in such settings is challenging because there is usually limited information about real-world events, as well as a lack of data from the real-world context at hand that is correctly labeled with respect to individuals' attentional state. In most approaches, such data is needed to train attention monitoring models. We here investigate whether unsupervised clustering can be combined with physiological synchrony in the electroencephalogram (EEG), electrodermal activity (EDA), and heart rate to automatically identify groups of individuals sharing attentional focus without using knowledge of the sensory stimuli or attentional focus of any of the individuals. We used data from an experiment in which 26 participants listened to an audiobook interspersed with emotional sounds and beeps. Thirteen participants were instructed to focus on the narrative of the audiobook and 13 participants were instructed to focus on the interspersed emotional sounds and beeps. We used a broad range of commonly applied dimensionality reduction ordination techniques-further referred to as mappings-in combination with unsupervised clustering algorithms to identify the two groups of individuals sharing attentional focus based on physiological synchrony. Analyses were performed using the three modalities EEG, EDA, and heart rate separately, and using all possible combinations of these modalities. The best unimodal results were obtained when applying clustering algorithms on physiological synchrony data in EEG, yielding a maximum clustering accuracy of 85%. Even though the use of EDA or heart rate by itself did not lead to accuracies significantly higher than chance level, combining EEG with these measures in a multimodal approach generally resulted in higher classification accuracies than when using only EEG. Additionally, classification results of multimodal data were found to be more consistent across algorithms than unimodal data, making algorithm choice less important. Our finding that unsupervised classification into attentional groups is possible is important to support studies on attentional engagement in everyday settings.

Does Perceived Stress of University Students Affected by Preferences for Movie Genres? an Exploratory Cross-Sectional Study in China

This study aims to explore whether different preferences for movie genres were related to different perceived stress of college students. An online questionnaire was designed and it was filled out by 1,549 students voluntarily. The 10-item perceived stress scale (PSS-10) and multinomial logistic regression were used to access the perceived stress and the association between the movie preference genres. Over 90% of participants had mild to serious levels of stress. Differences were found between participants with different stress perception states in terms of smoking history, active exercise, and sleep duration (p < 0.05). The participants who showed a preference for suspense movies more probably had lower stress [relative risk ratio (RRR)1 = 0.34, RRR2 = 0.26, p < 0.05], while students who showed preferences for crime film and disaster film more probably had higher stress (RRR = 2.03, p < 0.05, RRR = 3.15, p < 0.05). And the significant gender gap in different film genre preferences was observed in this study (p < 0.05). The males who showed preference for horror movies were more probably to have moderate stress (OR = 3.68, p < 0.05), and females who showed a preference for disaster movies were more probably to have high stress (OR = 3.27, p < 0.05). The perceived stress of Chinese university students is high after 1.5 years of coronavirus disease (COVID-19) pandemic. The personal preferences for certain film genres were significantly associated with perceived stress. As different film genre preferences, such as the preference for disaster, crime, and horror, are associated with high perceived stress, it may turn out to be useful to pay more attention to an individual's film viewing. The teachers need to be concerned with the media usage history and preferences of their students and may advise students with high-level stress to avoid potentially harmful media content.

Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements

Physiological responses of two interacting individuals contain a wealth of information about the dyad: for example, the degree of engagement or trust. However, nearly all studies on dyadic physiological responses have targeted group-level analysis: e.g., correlating physiology and engagement in a large sample. Conversely, this paper presents a study where physiological measurements are combined with machine learning algorithms to dynamically estimate the engagement of individual dyads. Sixteen dyads completed 15-min naturalistic conversations and self-reported their engagement on a visual analog scale every 60 s. Four physiological signals (electrocardiography, skin conductance, respiration, skin temperature) were recorded, and both individual physiological features (e.g., each participant's heart rate) and synchrony features (indicating degree of physiological similarity between two participants) were extracted. Multiple regression algorithms were used to estimate self-reported engagement based on physiological features using either leave-interval-out crossvalidation (training on 14 60-s intervals from a dyad and testing on the 15th interval from the same dyad) or leave-dyad-out crossvalidation (training on 15 dyads and testing on the 16th). In leave-interval-out crossvalidation, the regression algorithms achieved accuracy similar to a 'baseline' estimator that simply took the median engagement of the other 14 intervals. In leave-dyad-out crossvalidation, machine learning achieved a slightly higher accuracy than the baseline estimator and higher accuracy than an independent human observer. Secondary analyses showed that removing synchrony features and personality characteristics from the input dataset negatively impacted estimation accuracy and that engagement estimation error was correlated with personality traits. Results demonstrate the feasibility of dynamically estimating interpersonal engagement during naturalistic conversation using physiological measurements, which has potential applications in both conversation monitoring and conversation enhancement. However, as many of our estimation errors are difficult to contextualize, further work is needed to determine acceptable estimation accuracies.

Conscious processing of narrative stimuli synchronizes heart rate between individuals

Heart rate has natural fluctuations that are typically ascribed to autonomic function. Recent evidence suggests that conscious processing can affect the timing of the heartbeat. We hypothesized that heart rate is modulated by conscious processing and therefore dependent on attentional focus. To test this, we leverage the observation that neural processes synchronize between subjects by presenting an identical narrative stimulus. As predicted, we find significant inter-subject correlation of heart rate (ISC-HR) when subjects are presented with an auditory or audiovisual narrative. Consistent with our hypothesis, we find that ISC-HR is reduced when subjects are distracted from the narrative, and higher ISC-HR predicts better recall of the narrative. Finally, patients with disorders of consciousness have lower ISC-HR, as compared to healthy individuals. We conclude that heart rate fluctuations are partially driven by conscious processing, depend on attentional state, and may represent a simple metric to assess conscious state in unresponsive patients.