Affective science最新文献

Affective science is stuck in a version of the nature-versus-nurture debate, with theorists arguing whether emotions are evolved adaptations or psychological constructions. We do not see these as mutually exclusive options. Many adaptive behaviors that humans have evolved to be good at, such as walking, emerge during development – not according to a genetically dictated program, but through interactions between the affordances of the body, brain, and environment. We suggest emotions are the same. As developing humans acquire increasingly complex goals and learn optimal strategies for pursuing those goals, they are inevitably pulled to particular brain-body-behavior states that maximize outcomes and self-reinforce via positive feedback loops. We call these recurring, self-organized states emotions. Emotions display many of the hallmark features of self-organized attractor states, such as hysteresis (prior events influence the current state), degeneracy (many configurations of the underlying variables can produce the same global state), and stability. Because most bodily, neural, and environmental affordances are shared by all humans – we all have cardiovascular systems, cerebral cortices, and caregivers who raised us – similar emotion states emerge in all of us. This perspective helps reconcile ideas that, at first glance, seem contradictory, such as emotion universality and neural degeneracy.

Behavior change can be challenging to facilitate and achieve. Behavior change frameworks largely focus on social cognitive determinants, omitting affective determinants or including them in a superficial way. However, evidence points to the role of affect in decision-making and behavior, particularly when the behavior at focus for change is affectively pleasant or when the behavior to be facilitated is affectively unpleasant. This paper identifies challenges and opportunities to further affective science by using behavior change as a context and, relatedly, to further the science of behavior change by leveraging theoretical and methodological innovations in affective science.

People express their own emotions and perceive others’ emotions via a variety of channels, including facial movements, body gestures, vocal prosody, and language. Studying these channels of affective behavior offers insight into both the experience and perception of emotion. Prior research has predominantly focused on studying individual channels of affective behavior in isolation using tightly controlled, non-naturalistic experiments. This approach limits our understanding of emotion in more naturalistic contexts where different channels of information tend to interact. Traditional methods struggle to address this limitation: manually annotating behavior is time-consuming, making it infeasible to do at large scale; manually selecting and manipulating stimuli based on hypotheses may neglect unanticipated features, potentially generating biased conclusions; and common linear modeling approaches cannot fully capture the complex, nonlinear, and interactive nature of real-life affective processes. In this methodology review, we describe how deep learning can be applied to address these challenges to advance a more naturalistic affective science. First, we describe current practices in affective research and explain why existing methods face challenges in revealing a more naturalistic understanding of emotion. Second, we introduce deep learning approaches and explain how they can be applied to tackle three main challenges: quantifying naturalistic behaviors, selecting and manipulating naturalistic stimuli, and modeling naturalistic affective processes. Finally, we describe the limitations of these deep learning methods, and how these limitations might be avoided or mitigated. By detailing the promise and the peril of deep learning, this review aims to pave the way for a more naturalistic affective science.

This study investigated interpersonal effects of regulating naturalistic facial signals on cooperation during an iterative Prisoner’s Dilemma (IPD) game. Fifty pairs of participants played ten IPD rounds across a video link then reported on their own and their partner’s expressed emotion and facial regulation in a video-cued recall (VCR) procedure. iMotions software allowed us to auto-code actors’ and partners’ facial activity following the outcome of each round. We used two-level mixed effects logistic regression to assess over-time actor and partner effects of auto-coded facial activity, self-reported facial regulation, and perceptions of the partner’s facial regulation on the actor’s subsequent cooperation. Actors were significantly less likely to cooperate when their partners had defected on the previous round. None of the lagged scores based on auto-coded facial activity were significant predictors of cooperation. However, VCR variables representing partner’s positive regulation of expressions and actor’s perception of partner’s positive regulation both significantly increased the probability of subsequent actor cooperation after controlling for prior defection. These results offer preliminary evidence about interpersonal effects of facial regulation in interactive contexts and illustrate how dynamic dyadic emotional processes can be systematically investigated in controlled settings.

Empathy is a multidimensional construct that includes changes in cognitive, affective, and physiological processes. However, the physiological processes that contribute to empathic responding have received far less empirical attention. Here, we investigated whether physiological synchrony emerged during an empathy-inducing activity in which individuals disclosed a time of suffering while their romantic partner listened and responded (N = 111 couples). Further, we examined the extent to which trait and state measures of cognitive and affective empathy were associated with each other and with physiological synchrony during this activity. We found evidence for physiological synchrony in skin conductance reactivity and also in interbeat interval reactivity, though only when disclosers were women, but not for respiratory sinus arrhythmia reactivity. Physiological synchrony was not consistently associated with other well-established trait and state measures of empathy. These findings identify the nuanced role of physiological synchrony in empathic responding to others’ suffering.

For affective science to advance, researchers will need to develop a better understanding of neutral affect. At first glance, neutral affect may seem uninteresting to some affective scientists because the goal is to investigate hedonic experiences, not the presumed absence of them. This failure to fully consider and examine neutral affect, however, limits the field’s potential for new discoveries. In this paper, I discuss how a greater understanding of neutral affect can inform researchers’ views of valence, subjective well-being, and behavior. I define neutral affect and discuss evidence indicating that neutral affect is a commonly felt state that occurs independently of positive and negative affect. These data suggest that to understand the entirety of the affective landscape, researchers should move beyond traditional measures of valence and consider how positive, negative, and neutral affective states might inform their phenomenon of interest. I then illustrate how neutral affect might be a key, albeit complex, influence on subjective well-being. I also discuss how neutrality might be a fundamental and unique predictor of inaction. If affective scientists want to fully understand how feelings operate and function, it is essential that they explore the possibility that neutral affect might hold some of the essential clues needed to solve their affective puzzle.

AI research focused on interactions with humans, particularly in the form of robots or virtual agents, has expanded in the last two decades to include concepts related to affective processes. Affective computing is an emerging field that deals with issues such as how the diagnosis of affective states of users can be used to improve such interactions, also with a view to demonstrate affective behavior towards the user. This type of research often is based on two beliefs: (1) artificial emotional intelligence will improve human computer interaction (or more specifically human robot interaction), and (2) we understand the role of affective behavior in human interaction sufficiently to tell artificial systems what to do. However, within affective science the focus of research is often to test a particular assumption, such as “smiles affect liking.” Such focus does not provide the information necessary to synthesize affective behavior in long dynamic and real-time interactions. In consequence, theories do not play a large role in the development of artificial affective systems by engineers, but self-learning systems develop their behavior out of large corpora of recorded interactions. The status quo is characterized by measurement issues, theoretical lacunae regarding prevalence and functions of affective behavior in interaction, and underpowered studies that cannot provide the solid empirical foundation for further theoretical developments. This contribution will highlight some of these challenges and point towards next steps to create a rapprochement between engineers and affective scientists with a view to improving theory and solid applications.

While the field of affective science has seen increased interest in and representation of the role of culture in emotion, prior research has disproportionately centered on Western, English-speaking, industrialized, and/or economically developed nations. We investigated the extent to which emotional experiences and responding may be shaped by cultural display rule understanding among Yucatec Maya children, an indigenous population residing in small-scale communities in remote areas of Mexico’s Yucatan peninsula. Data were collected from forty-two 6- and 10-year-old Yucatec children who completed a resting baseline and a structured disappointing gift task. Children were asked about whether specific emotions are better to show or to hide from others and self-reported the intensity of their discrete positive and negative emotional experiences. We observed and coded expressive positive and negative affective behavior during and after the disappointing gift task, and continuously acquired physiological measures of autonomic nervous system function. These multi-method indices of emotional responding enable us to provide a nuanced description of children’s observable and unobservable affective experiences. Results generally indicated that children’s understanding of and adherence to cultural display rules (i.e., to suppress negative emotions but openly show positive ones) was evidenced across indices of emotion, as predicted. The current study is a step toward the future of affective science, which lies in the pursuit of more diverse and equitable representation in study samples, increased use of concurrent multimethod approaches to studying emotion, and increased exploration of how emotional processes develop.

Contrary to early theorizing, emotions often last for longer periods of time. Variability in people’s emotion duration contributes to psychopathologies. Therefore, emotion theories need to account for this variability. So far, reviews only list predictors of emotion duration without integrating them in a theoretical framework. Mechanisms explaining why these predictors relate to emotion duration remain unknown. I propose to embed research on emotion duration in a network model of emotions and illustrate the central ideas with simulations using a formal network model. In the network model, the components of an emotion have direct causal effects on each other. According to the model, emotions last longer (a) when the components are more strongly connected or (b) when the components have higher thresholds (i.e., they are more easily activated). High connectivity prolongs emotions because components are constantly reactivated. Higher thresholds prolong emotions because components are more easily reactivated even when connectivity is lower. Indirect evidence from research on emotion coherence and research on the relationship of predictors of emotion duration with components outside of emotional episodes supports the usefulness of the network model. I further argue and show in simulations that a common cause model, in which a latent emotion causes changes in emotion components, cannot account for research on emotion duration. Finally, I describe future directions for research on emotion duration and emotion dynamics from a network perspective.

Group living is thought to benefit from the ability to empathize with others. Much attention has been paid to empathy for the pain of others as an inhibitor of aggression. Empathizing with the positive affect of others has received less attention although it could promote helping by making it vicariously rewarding. Here, we review this latter, nascent literature to show that three components of the ability to empathize with positive emotions are already present in rodents, namely, the ability to perceive, share, and prefer actions that promote positive emotional states of conspecifics. While it has often been argued that empathy evolved as a motivation to care for others, we argue that these tendencies may have selfish benefits that could have stabilized their evolution: approaching others in a positive state can provide information about the source of valuable resources; becoming calmer and optimistic around animals in a calm or positive mood can help adapt to the socially sensed safety level in the environment; and preferring actions also benefiting others can optimize foraging, reduce aggression, and trigger reciprocity. Together, these findings illustrate an emerging field shedding light on the emotional world of rodents and on the biology and evolution of our ability to cooperate in groups.