Multivariate model for cooperation: bridging Social Physiological Compliance and Hyperscanning

The neurophysiological analysis of cooperation has evolved over the past 20 years, moving towards the research of common patterns in neurophysiological signals of people interacting. Social Physiological Compliance (SPC) and Hyperscanning represent two frameworks for the joint analysis of autonomic and brain signals respectively. Each of the two approaches allows to know about a single layer of cooperation according to the nature of these signals: SPC provides information mainly related to emotions, and Hyperscanning that related to cognitive aspects. In this work, after the analysis of the state of the art of SPC and Hyperscanning, we explored the possibility to unify the two approaches creating a complete neurophysiological model for cooperation considering both affective and cognitive mechanisms. We synchronously recorded electrodermal activity, cardiac and brain signals of 14 cooperative dyads. Time series from these signals were extracted and Multivariate Granger Causality was computed. The results showed that only when subjects in a dyad cooperate there is a statistically significant causality between the multivariate variables representing each subject. Moreover, the entity of this statistical relationship correlates with the dyad's performance. Finally, given the novelty of this approach and its exploratory nature, we provided its strengths and limitations

Coherence between subjective experience and physiology in emotion: Individual differences and implications for well-being.

Emotion theorists have characterized emotions as involving coherent responding across various emotion response systems (e.g., covariation of subjective experience and physiology). Greater response system coherence has been theorized to promote well-being, yet very little research has tested this assumption. The current study examined whether individuals with greater coherence between physiology and subjective experience of emotion report greater well-being. We also examined factors that may predict the magnitude of coherence, such as emotion intensity, cognitive reappraisal, and expressive suppression. Participants (N = 63) completed self-report measures of well-being, expressive suppression, and cognitive reappraisal. They then watched a series of emotionally evocative film clips designed to elicit positive and negative emotion. During the films, participants continuously rated their emotional experience using a rating dial, and their autonomic physiological responses were recorded. Time-lagged cross-correlations were used to calculate within-participant coherence between intensity of emotional experience (ranging from neutral to very negative or very positive) and physiology (composite of cardiac interbeat interval, skin conductance, ear pulse transit time, finger pulse transit time and amplitude, systolic and diastolic blood pressure). Results indicated that individuals with greater coherence reported greater well-being. Coherence was highest during the most emotionally intense film and among individuals who reported lower expressive suppression. However, coherence was not associated with reappraisal. These findings provide support for the idea that greater emotion coherence promotes well-being and also shed light on factors that are associated with the magnitude of coherence. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

Autonomic nervous system functioning assessed during the Still-Face Paradigm: A meta-analysis and systematic review of methods, approach and findings.

Animal and human research suggests that the development of the autonomic nervous system (ANS) is particularly sensitive to early parenting experiences. The Still-Face Paradigm (SFP), one of the most widely used measures to assess infant reactivity and emotional competence, evokes infant self-regulatory responses to parental interaction and disengagement. This systematic review of 33 peer-reviewed studies identifies patterns of parasympathetic (PNS) and sympathetic (SNS) nervous system activity demonstrated by infants under one year of age during the SFP and describes findings within the context of sample demographic characteristics, study methodologies, and analyses conducted. A meta-analysis of a subset of 14 studies with sufficient available respiratory sinus arrhythmia (RSA) data examined whether the SFP reliably elicited PNS withdrawal (RSA decrease) during parental disengagement or PNS recovery (RSA increase) during reunion, and whether results differed by socioeconomic status (SES). Across SES, the meta-analysis confirmed that RSA decreased during the still-face episode and increased during reunion. When studies were stratified by SES, low-SES or high-risk groups also showed RSA decreases during the still face episode but failed to show an increase in RSA during reunion. Few studies have examined SNS activity during the SFP to date, preventing conclusions in that domain. The review also identified multiple qualifications to patterns of SFP ANS findings, including those that differed by ethnicity, infant sex, parental sensitivity, and genetics. Strengths and weaknesses in the extant research that may explain some of the variation in findings across the literature are also discussed, and suggestions for strengthening future research are provided

Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications

Bridging the gap between emotion and joint action

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd. Joint action brings individuals (and embodiment of their emotions) together, in space and in time. Yet little is known about how individual emotions propagate through embodied presence in a group, and how joint action changes individual emotion. In fact, the multi-agent component is largely missing from neuroscience-based approaches to emotion, and reversely joint action research has not found a way yet to include emotion as one of the key parameters to model socio-motor interaction. In this review, we first identify the gap and then stockpile evidence showing strong entanglement between emotion and acting together from various branches of sciences. We propose an integrative approach to bridge the gap, highlight five research avenues to do so in behavioral neuroscience and digital sciences, and address some of the key challenges in the area faced by modern societies

Synchrony and concordance: A multilevel analysis of the effects of individual differences during a CO2 challenge

Emotion theories posit that emotion systems (e.g., behavior, self-report, physiology) should be related when an emotion is being elicited because this serves an adaptive purpose and allows the individual to respond appropriately to the present situation. Oftentimes, this coherent relationship is not found, and research has hypothesized that the type of analyses used and lack of examination of individual differences could be affecting this relationship. Most studies examine the relationship between emotion systems between-subjects when within-subjects analyses may be more appropriate. The present study examined the relationship between self-reported distress (SUDS) and heart rate, and whether trait differences of anxiety sensitivity and heart rate variability affect that relationship. Undergraduate students (N = 294) completed an anxiety sensitivity measure and their heart rate variability was calculated prior to undergoing a 7.5% CO2 challenge. SUDS was collected 11 times throughout the challenge and heart rate was collected continuously. Consistent with studies examining both concordance (between-subjects correlation between systems) and synchrony (within-subjects correlation between systems), synchrony was found between heart rate and SUDS, but concordance was not found between the two variables. Contrary to our hypotheses, neither anxiety sensitivity nor heart rate variability predicted synchrony between heart rate and SUDS. Our results suggest that synchrony is a more appropriate measure of adaptive emotional response than concordance because synchrony allows for examination of coordination of emotion systems over time