Neural signatures of emotional inference and experience align during social consensus.

Humans seamlessly transform dynamic social signals into inferences about the internal states of the people around them. To understand the neural processes that sustain this transformation, we collected fMRI data from participants (N = 100) while they rated the emotional intensity of people (targets) describing significant life events. Targets rated themselves on the same scale to indicate the intended "ground truth" emotional intensity of their videos. Next, we developed two multivariate models of observer brain activity- the first predicted the "ground truth" (r = 0.50, p < 0.0001) and the second predicted observer inferences (r = 0.53, p < 0.0001). When individuals make more accurate inferences, there is greater moment-by-moment concordance between these two models, suggesting that an observer's brain activity contains latent representations of other people's emotional states. Using naturalistic socioemotional stimuli and machine learning, we developed reliable brain signatures that predict what an observer thinks about a target, what the target thinks about themselves, and the correspondence between them. These signatures can be applied in clinical data to better our understanding of socioemotional dysfunction.

Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering.

A fundamental challenge in emotion research is measuring feeling states with high granularity and temporal precision without disrupting the emotion generation process. Here we introduce and validate a new approach in which responses are sparsely sampled and the missing data are recovered using a computational technique known as collaborative filtering (CF). This approach leverages structured covariation across individual experiences and is available in Neighbors, an open-source Python toolbox. We validate our approach across three different experimental contexts by recovering dense individual ratings using only a small subset of the original data. In dataset 1, participants (n=316) separately rated 112 emotional images on 6 different discrete emotions. In dataset 2, participants (n=203) watched 8 short emotionally engaging autobiographical stories while simultaneously providing moment-by-moment ratings of the intensity of their affective experience. In dataset 3, participants (n=60) with distinct social preferences made 76 decisions about how much money to return in a hidden multiplier trust game. Across all experimental contexts, CF was able to accurately recover missing data and importantly outperformed mean and multivariate imputation, particularly in contexts with greater individual variability. This approach will enable new avenues for affective science research by allowing researchers to acquire high dimensional ratings from emotional experiences with minimal disruption to the emotion-generation process. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-022-00161-2.

The Brain Activation-Based Sexual Image Classifier (BASIC): A Sensitive and Specific fMRI Activity Pattern for Sexual Image Processing.

Previous studies suggest there is a complex relationship between sexual and general affective stimulus processing, which varies across individuals and situations. We examined whether sexual and general affective processing can be distinguished at the brain level. In addition, we explored to what degree possible distinctions are generalizable across individuals and different types of sexual stimuli, and whether they are limited to the engagement of lower-level processes, such as the detection of visual features. Data on sexual images, nonsexual positive and negative images, and neutral images from Wehrum et al. (2013) (N = 100) were reanalyzed using multivariate support vector machine models to create the brain activation-based sexual image classifier (BASIC) model. This model was tested for sensitivity, specificity, and generalizability in cross-validation (N = 100) and an independent test cohort (N = 18; Kragel et al. 2019). The BASIC model showed highly accurate performance (94-100%) in classifying sexual versus neutral or nonsexual affective images in both datasets with forced choice tests. Virtual lesions and tests of individual large-scale networks (e.g., visual or attention networks) show that individual networks are neither necessary nor sufficient to classify sexual versus nonsexual stimulus processing. Thus, responses to sexual images are distributed across brain systems.

Recommendations for the Development of Socioeconomically-Situated and Clinically-Relevant Neuroimaging Models of Pain.

Pain is a complex, multidimensional experience that emerges from interactions among sensory, affective, and cognitive processes in the brain. Neuroimaging allows us to identify these component processes and model how they combine to instantiate the pain experience. However, the clinical impact of pain neuroimaging models has been limited by inadequate population sampling - young healthy college students are not representative of chronic pain patients. The biopsychosocial approach to pain management situates a person's pain within the diverse socioeconomic environments they live in. To increase the clinical relevance of pain neuroimaging models, a three-fold biopsychosocial approach to neuroimaging biomarker development is recommended. The first level calls for the development of diagnostic biomarkers via the standard population-based (nomothetic) approach with an emphasis on diverse sampling. The second level calls for the development of treatment-relevant models via a constrained person-based (idiographic) approach tailored to unique individuals. The third level calls for the development of prevention-relevant models via a novel society-based (social epidemiologic) approach that combines survey and neuroimaging data to predict chronic pain risk based on one's socioeconomic conditions. The recommendations in this article address how we can leverage pain's complexity in service of the patient and society by modeling not just individuals and populations, but also the socioeconomic structures that shape any individual's expectations of threat, safety, and resource availability.

Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset.

Human emotions unfold over time, and more affective computing research has to prioritize capturing this crucial component of real-world affect. Modeling dynamic emotional stimuli requires solving the twin challenges of time-series modeling and of collecting high-quality time-series datasets. We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches in affective computing, including discriminative and generative models. We then introduce the first version of the Stanford Emotional Narratives Dataset (SENDv1): a set of rich, multimodal videos of self-paced, unscripted emotional narratives, annotated for emotional valence over time. The complex narratives and naturalistic expressions in this dataset provide a challenging test for contemporary time-series emotion recognition models. We demonstrate several baseline and state-of-the-art modeling approaches on the SEND, including a Long Short-Term Memory model and a multimodal Variational Recurrent Neural Network, which perform comparably to the human-benchmark. We end by discussing the implications for future research in time-series affective computing.

Touch and social support influence interpersonal synchrony and pain.

Interpersonal touch and social support can influence physical health, mental well-being and pain. However, the mechanisms by which supportive touch promotes analgesia are not well understood. In Study 1, we tested how three kinds of social support from a romantic partner (passive presence, gentle stroking and handholding) affect pain ratings and skin conductance responses (SCRs). Overall, support reduced pain ratings in women, but not men, relative to baseline. Support decreased pain-related SCRs in both women and men. Though there were no significant differences across the three support conditions, effects were largest during handholding. Handholding also reduced SCRs in the supportive partner. Additionally, synchronicity in couples' SCR was correlated with reductions in self-reported pain, and individual differences in synchrony were correlated with the partner's trait empathy. In Study 2, we re-analyzed an existing dataset to explore fMRI activity related to individual differences in handholding analgesia effects in women. Increased activity in a distributed set of brain regions, including valuation-encoding frontostriatal areas, was correlated with lower pain ratings. These results may suggest that social support can reduce pain by changing the value of nociceptive signals. This reduction may be moderated by interpersonal synchrony and relationship dynamics.

Emotion schemas are embedded in the human visual system.

Theorists have suggested that emotions are canonical responses to situations ancestrally linked to survival. If so, then emotions may be afforded by features of the sensory environment. However, few computational models describe how combinations of stimulus features evoke different emotions. Here, we develop a convolutional neural network that accurately decodes images into 11 distinct emotion categories. We validate the model using more than 25,000 images and movies and show that image content is sufficient to predict the category and valence of human emotion ratings. In two functional magnetic resonance imaging studies, we demonstrate that patterns of human visual cortex activity encode emotion category-related model output and can decode multiple categories of emotional experience. These results suggest that rich, category-specific visual features can be reliably mapped to distinct emotions, and they are coded in distributed representations within the human visual system.

Cognitive self-regulation influences pain-related physiology.

Cognitive self-regulation can shape pain experience, but its effects on autonomic responses to painful events are unclear. In this study, participants (N = 41) deployed a cognitive strategy based on reappraisal and imagination to regulate pain up or down on different trials while skin conductance responses (SCRs) and electrocardiogram activity were recorded. Using a machine learning approach, we first developed stimulus-locked SCR and electrocardiogram physiological markers predictive of pain ratings. The physiological markers demonstrated high sensitivity and moderate specificity in predicting pain across 2 data sets, including an independent test data set (N = 84). When we tested the markers on the cognitive self-regulation data, we found that cognitive self-regulation had significant impacts on both pain ratings and pain-related physiology in accordance with regulatory goals. These findings suggest that self-regulation can impact autonomic nervous system responses to painful stimuli and provide pain-related autonomic profiles for future studies.

Brain systems at the intersection of chronic pain and self-regulation.

Chronic pain is a multidimensional experience with cognitive, affective, and somatosensory components that can be modified by expectations and learning. Individual differences in cognitive and affective processing, as well as contextual aspects of the pain experience, render chronic pain an inherently personal experience. Such individual differences are supported by the heterogeneity of brain representations within and across chronic pain pathologies. In this review, we discuss the complexity of brain representations of pain, and, with respect to this complexity, identify common elements of network-level disruptions in chronic pain. Specifically, we identify prefrontal-limbic circuitry and the default mode network as key elements of functional disruption. We then discuss how these disrupted circuits can be targeted through self-regulation and related cognitive strategies to alleviate chronic pain. We conclude with a proposal for how to develop personalized multivariate models of pain representation in the brain and target them with real-time neurofeedback, so that patients can explore and practice self-regulatory techniques with maximal efficiency.

Attenuating Neural Threat Expression with Imagination.

Imagination is an internal simulation of real-life events and a common treatment tool for anxiety disorders; however, the neural processes by which imagination exerts behavioral control are unclear. This investigation tests whether and how imagined exposures to a threatening stimulus, conditioned in the real world, influence neural and physiological manifestations of threat. We found that imagined and real extinction are equally effective in the reduction of threat-related neural patterns and physiological responses elicited upon re-exposure to real-world threatening cues. Network connectivity during the extinction phase showed that imagined, like real, extinction engaged the ventromedial prefrontal cortex (vmPFC) as a central hub. vmPFC, primary auditory cortex, and amygdala activation during imagined and real extinction were predictive of individual differences in extinction success. The nucleus accumbens, however, predicted extinction success in the imagined extinction group alone. We conclude that deliberate imagination can attenuate reactions to threat through perceptual and associative learning mechanisms.

Modeling Pain Using fMRI: From Regions to Biomarkers.

Pain is a subjective and complex phenomenon. Its complexity is related to its heterogeneity: multiple component processes, including sensation, affect, and cognition, contribute to pain experience and reporting. These components are likely to be encoded in distributed brain networks that interact to create pain experience and pain-related decision-making. Therefore, to understand pain, we must identify these networks and build models of these interactions that yield testable predictions about pain-related outcomes. We have developed several such models or 'signatures' of pain, by (1) integrating activity across multiple systems, and (2) using pattern-recognition to identify processes related to pain experience. One model, the Neurologic Pain Signature, is sensitive and specific to pain in individuals, involves brain regions that receive nociceptive afferents, and shows little effect of expectation or self-regulation in tests to date. Another, the 'Stimulus Intensity-Independent Pain Signature', explains substantial additional variation in trial-to-trial pain reports. It involves many brain regions that do not show increased activity in proportion to noxious stimulus intensity, including medial and lateral prefrontal cortex, nucleus accumbens, and hippocampus. Responses in this system mediate expectancy and perceived control effects in several studies. Overall, this approach provides a pathway to understanding pain by identifying multiple systems that track different aspects of pain. Such componential models can be combined in unique ways on a subject-by-subject basis to explain an individual's pain experience.

Aberrant link between empathy and social attribution style in borderline personality disorder.

In social interactions, we often need to quickly infer why other people do what they do. More often than not, we infer that behavior is a result of personality rather than circumstances. It is unclear how the tendency itself may contribute to psychopathology and interpersonal dysfunction. Borderline personality disorder (BPD) is characterized by severe interpersonal dysfunction. Here, we investigated if this dysfunction is related to the tendency to over-attribute behaviors to personality traits. Healthy controls and patients with BPD judged positive and negative behaviors presented within a situational constraint during functional magnetic resonance imaging. Before the experiment, we measured trait levels of empathy, paranoia, and need for cognition. Behaviorally, we found that empathy levels predicted the tendency to attribute behavior to traits in healthy controls, whereas in patients with BPD this relationship was significantly weakened. Whole brain analysis of group-by-empathy interaction revealed that when participants judged the behavior during the attribution phase, several brain regions implicated in mentalizing distinguished patients from controls: In healthy controls, neural activity scaled negatively with empathy, but this relationship was reversed in BPD patients. Due to the cross-sectional study design we cannot establish a causal link between empathy and social attributions. These findings indicate that the self-reported tendency to feel for others is related to the tendency to integrate situational information beyond personality. In BPD patients, by contrast, the association between empathy and attribution was significantly weaker, rendering empathy less informative in predicting the overall attribution style.

Testing the disgust conditioning theory of food-avoidance in adolescents with recent onset anorexia nervosa.

Anorexia nervosa is characterized by chronic food avoidance that is resistant to change. Disgust conditioning offers one potential unexplored mechanism for explaining this behavioral disturbance because of its specific role in facilitating food avoidance in adaptive situations. A food based reversal learning paradigm was used to study response flexibility in 14 adolescent females with restricting subtype anorexia nervosa (AN-R) and 15 healthy control (HC) participants. Expectancy ratings were coded as a behavioral measure of flexibility and electromyography recordings from the levator labii (disgust), zygomaticus major (pleasure), and corrugator (general negative affect) provided psychophysiological measures of emotion. Response inflexibility was higher for participants with AN-R, as evidenced by lower extinction and updated expectancy ratings during reversal. EMG responses to food stimuli were predictive of both extinction and new learning. Among AN-R patients, disgust specific responses to food were associated with impaired extinction, as were elevated pleasure responses to the cued absence of food. Disgust conditioning appears to influence food learning in acutely ill patients with AN-R and may be maintained by counter-regulatory acquisition of a pleasure response to food avoidance and an aversive response to food presence. Developing strategies to target disgust may improve existing interventions for patients with AN.

The challenges of forecasting resilience.

Developing prospective models of resilience using the translational and transdiagnostic framework proposed in the target article is a challenging endeavor and will require large-scale data sets with dense intraindividual temporal sampling and innovative analytic methods.

Stressor controllability modulates fear extinction in humans.

Traumatic events are proposed to play a role in the development of anxiety disorders, however not all individuals exposed to extreme stress experience a pathological increase in fear. Recent studies in animal models suggest that the degree to which one is able to control an aversive experience is a critical factor determining its behavioral consequences. In this study, we examined whether stressor controllability modulates subsequent conditioned fear expression in humans. Participants were randomly assigned to an escapable stressor condition, a yoked inescapable stressor condition, or a control condition involving no stress exposure. One week later, all participants underwent fear conditioning, fear extinction, and a test of extinction retrieval the following day. Participants exposed to inescapable stress showed impaired fear extinction learning and increased fear expression the following day. In contrast, escapable stress improved fear extinction and prevented the spontaneous recovery of fear. Consistent with the bidirectional controllability effects previously reported in animal models, these results suggest that one's degree of control over aversive experiences may be an important factor influencing the development of psychological resilience or vulnerability in humans.