Aberrant cognitive empathy in individuals with elevated social anxiety and regulation with emotional working memory training.

Social anxiety may disrupt the empathic process, and well-regulated empathy is critical for navigating the social world. Two studies aimed to further understand empathy in the context of social anxiety. Study 1 compared individuals with elevated or normative social anxiety on a measure assessing cognitive and affective empathy for positive and negative emotions conveyed by other people ("targets"), completed under social threat. Relative to individuals with normative social anxiety, individuals with elevated social anxiety had greater cognitive empathy and no differences in affective empathy, regardless of emotion type. As greater cognitive empathy can be maladaptive, Study 2 tested whether this could be down-regulated. Individuals with elevated social anxiety underwent emotional working memory training (eWMT) for negative emotional information, or control training (CT). Effects on an empathy measure completed under social threat were assessed. Cognitive empathy for negative emotions decreased following eWMT but not CT, and this was only evident for those with higher pre-training working memory capacity. Cognitive empathy for positive emotions and affective empathy were not affected. Overall, social anxiety is associated with aberrant elevated cognitive empathy for negative and positive emotions, and the deviation in cognitive empathy for negative emotions can be regulated with eWMT for certain individuals.Trial registration: Australian New Zealand Clinical Trials Registry identifier: ACTRN12618001196235..

Probabilistic programming versus meta-learning as models of cognition.

We summarize the recent progress made by probabilistic programming as a unifying formalism for the probabilistic, symbolic, and data-driven aspects of human cognition. We highlight differences with meta-learning in flexibility, statistical assumptions and inferences about cogniton. We suggest that the meta-learning approach could be further strengthened by considering Connectionist and Bayesian approaches, rather than exclusively one or the other.

Empathy and well-being correlate with centrality in different social networks.

Individuals benefit from occupying central roles in social networks, but little is known about the psychological traits that predict centrality. Across four college freshman dorms (n = 193), we characterized individuals with a battery of personality questionnaires and also asked them to nominate dorm members with whom they had different types of relationships. This revealed several social networks within dorm communities with differing characteristics. In particular, additional data showed that networks varied in the degree to which nominations depend on (i) trust and (ii) shared fun and excitement. Networks more dependent upon trust were further defined by fewer connections than those more dependent on fun. Crucially, network and personality features interacted to predict individuals' centrality: people high in well-being (i.e., life satisfaction and positive emotion) were central to networks characterized by fun, whereas people high in empathy were central to networks characterized by trust. Together, these findings provide network-based corroboration of psychological evidence that well-being is socially attractive, whereas empathy supports close relationships. More broadly, these data highlight how an individual's personality relates to the roles that they play in sustaining their community.

Strategic Resource Use for Learning: A Self-Administered Intervention That Guides Self-Reflection on Effective Resource Use Enhances Academic Performance.

Many educational policies provide learners with more resources (e.g., new learning activities, study materials, or technologies), but less often do they address whether students are using these resources effectively. We hypothesized that making students more self-reflective about how they should approach their learning with the resources available to them would improve their class performance. We designed a novel Strategic Resource Use intervention that students could self-administer online and tested its effects in two cohorts of a college-level introductory statistics class. Before each exam, students randomly assigned to the treatment condition strategized about which academic resources they would use for studying, why each resource would be useful, and how they would use their resources. Students randomly assigned to the treatment condition reported being more self-reflective about their learning throughout the class, used their resources more effectively, and outperformed students in the control condition by an average of one third of a letter grade in the class.

A Second Look at Automatic Theory of Mind: Reconsidering Kovács, Téglás, and Endress (2010).

In recent work, Kovács, Téglás, and Endress (2010) argued that human adults automatically represented other agents' beliefs even when those beliefs were completely irrelevant to the task being performed. In a series of 13 experiments, we replicated these previous findings but demonstrated that the effects found arose from artifacts in the experimental paradigm. In particular, the critical findings demonstrating automatic belief computation were driven by inconsistencies in the timing of an attention check, and thus do not provide evidence for automatic theory of mind in adults.

Emotion understanding as third-person appraisals: Integrating appraisal theories with developmental theories of emotion.

Emotion understanding goes beyond recognizing emotional displays-it also involves reasoning about how people's emotions are affected by their subjective evaluations of what they experienced. Inspired by work in adults on cognitive appraisal theories of emotion, we propose a framework that can guide systematic investigations of how an adult-like, sophisticated understanding of emotion develops from infancy to adulthood. We integrate basic concepts of appraisal theories with developmental theories of emotion understanding and suggest that over development, young children construct an intuitive, theory-like understanding of other people's emotions that is structurally similar to appraisal theories. That is, children are increasingly able to evaluate other people's situations from those people's perspectives along various appraisal dimensions and use such third-person appraisals to understand those people's emotional responses to events. This "third-person-appraisal" framework can not only incorporate existing empirical findings but can also identify gaps in the literature, providing a guiding framework for systematically investigating the development of emotion understanding. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Face and context integration in emotion inference is limited and variable across categories and individuals.

The ability to make nuanced inferences about other people's emotional states is central to social functioning. While emotion inferences can be sensitive to both facial movements and the situational context that they occur in, relatively little is understood about when these two sources of information are integrated across emotion categories and individuals. In a series of studies, we use one archival and five empirical datasets to demonstrate that people could be integrating, but that emotion inferences are just as well (and sometimes better) captured by knowledge of the situation alone, while isolated facial cues are insufficient. Further, people integrate facial cues more for categories for which they most frequently encounter facial expressions in everyday life (e.g., happiness). People are also moderately stable over time in their reliance on situational cues and integration of cues and those who reliably utilize situation cues more also have better situated emotion knowledge. These findings underscore the importance of studying variability in reliance on and integration of cues.

Enhancing rotational diffusion using oscillatory shear.

Taylor dispersion--shear-induced enhancement of translational diffusion--is an important phenomenon with applications ranging from pharmacology to geology. Through experiments and simulations, we show that rotational diffusion is also enhanced for anisotropic particles in oscillatory shear. This enhancement arises from variations in the particle's rotation (Jeffery orbit) and depends on the strain amplitude, rate, and particle aspect ratio in a manner that is distinct from the translational diffusion. This separate tunability of translational and rotational diffusion opens the door to new techniques for controlling positions and orientations of suspended anisotropic colloids.

Mu rhythm suppression over sensorimotor regions is associated with greater empathic accuracy.

When people encounter others' emotions, they engage multiple brain systems, including parts of the sensorimotor cortex associated with motor simulation. Simulation-related brain activity is commonly described as a 'low-level' component of empathy and social cognition. It remains unclear whether and how sensorimotor simulation contributes to complex empathic judgments. Here, we combine a naturalistic social paradigm with a reliable index of sensorimotor cortex-based simulation: electroencephalography suppression of oscillatory activity in the mu frequency band. We recruited participants to watch naturalistic video clips of people ('targets') describing emotional life events. In two experiments, participants viewed these clips (i) with video and sound, (ii) with only video or (iii) with only sound and provided continuous ratings of how they believed the target felt. We operationalized 'empathic accuracy' as the correlation between participants' inferences and targets' self-report. In Experiment 1 (US sample), across all conditions, right-lateralized mu suppression tracked empathic accuracy. In Experiment 2 (Israeli sample), this replicated only when using individualized frequency-bands and only for the visual stimuli. Our results provide novel evidence that sensorimotor representations-as measured through mu suppression-play a role not only in low-level motor simulation, but also in higher-level inferences about others' emotions, especially when visual cues are crucial for accuracy.

A brief intervention to motivate empathy among middle school students.

Empathy tracks socioemotional adjustment during early adolescence, yet adolescents this age tend to show reductions in empathy compared with younger children. Here we took a novel approach to building empathy among early adolescents in four middle schools (n = 857). Rather than addressing the ability to empathize, we targeted the motivation to empathize. To do so, we leveraged strategies demonstrated to change motivation among early adolescents: social norms and mindsets. Compared with those in other conditions, students who received a norms-based intervention reported greater motivation to empathize with others, which was in turn associated with increased peer-reported prosocial behaviors, as well as lower levels of loneliness and aggression. The effects of this norms condition were strongest at schools with relatively high engagement with the intervention. Findings suggest a novel avenue for increasing empathy among early adolescents-focusing on peer-driven motivation-and underscore the importance of context in shaping intervention outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Amplification in the evaluation of multiple emotional expressions over time.

Social interactions are dynamic and unfold over time. To make sense of social interactions, people must aggregate sequential information into summary, global evaluations. But how do people do this? Here, to address this question, we conducted nine studies (N = 1,583) using a diverse set of stimuli. Our focus was a central aspect of social interaction-namely, the evaluation of others' emotional responses. The results suggest that when aggregating sequences of images and videos expressing varying degrees of emotion, perceivers overestimate the sequence's average emotional intensity. This tendency for overestimation is driven by stronger memory of more emotional expressions. A computational model supports this account and shows that amplification cannot be explained only by nonlinear perception of individual exemplars. Our results demonstrate an amplification effect in the perception of sequential emotional information, which may have implications for the many types of social interactions that involve repeated emotion estimation.

Real-world effectiveness of a social-psychological intervention translated from controlled trials to classrooms.

Social-psychological interventions have raised the learning and performance of students in rigorous efficacy trials. Yet, after they are distributed "in the wild" for students to self-administer, there has been little research following up on their translational effectiveness. We used cutting-edge educational technology to tailor, scale up, and track a previously-validated Strategic Resource Use intervention among 12,065 college students in 14 STEM and Economics classes. Students who self-administered this "Exam Playbook" benefitted by an average of 2.17 percentage points (i.e., a standardized effect size of 0.18), compared to non-users. This effect size was 1.65 percentage points when controlling for college entrance exam scores and 1.75 [-1.88] for adding [dropping] the Exam Playbook in stratified matching analyses. Average benefits differed in magnitude by the conduciveness of the class climate (including peer norms and incentives), gender, first-generation status, as well as how often and how early they used the intervention. These findings on how, when, and who naturally adopts these resources address a need to improve prediction, translation, and scalability of social-psychological intervention benefits.

Applying Probabilistic Programming to Affective Computing.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.

Building empathy through motivation-based interventions.

Empathy is associated with adaptive social and emotional outcomes; as such, a crucial outstanding question is whether it can be bolstered in ways that make practical differences in people's lives. Most empathy-building efforts address one's ability to empathize, increasing empathy by training skills like perspective taking. However, empathy is more than the ability to share and understand others' feelings; it also reflects underlying motives that drive people to experience or avoid it. As such, another strategy for increasing empathy could focus on shifting relevant motives. Here we explored this idea, leveraging two intervention techniques (mindsets and social norms) to increase motivation to empathize. Two hundred ninety-two first-year college students were randomly assigned to one of three intervention conditions-malleable mindset, social norms, or a combination of the two-or a control condition. Eight weeks later, participants in the intervention conditions endorsed stronger beliefs about empathy's malleability and exhibited greater empathic accuracy when rating others' positive emotions as compared to the control condition. They also reported having made a greater number of friends since starting college. The interventions did not affect outcomes related to intergroup processes or empathic accuracy when rating others' negative emotions, indicating a boundary condition for these interventions. This experiment underscores the potential of motivation-based empathy interventions to generate positive, real-world impact. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

Glassy dislocation dynamics in 2D colloidal dimer crystals.

Although glassy relaxation is typically associated with disorder, here we report on a new type of glassy dynamics relating to dislocations within 2D crystals of colloidal dimers. Previous studies have demonstrated that dislocation motion in dimer crystals is restricted by certain particle orientations. Here, we drag an optically trapped particle through such dimer crystals, creating dislocations. We find a two-stage relaxation response where initially dislocations glide until encountering particles that cage their motion. Subsequent relaxation occurs logarithmically slowly through a second process where dislocations hop between caged configurations. Finally, in simulations of sheared dimer crystals, the dislocation mean squared displacement displays a caging plateau typical of glassy dynamics. Together, these results reveal a novel glassy system within a colloidal crystal.

Integrating expectations and outcomes: Preschoolers' developing ability to reason about others' emotions.

People's emotional experiences depend not only on what actually happened, but also on what they thought would happen. However, these expectations about future outcomes are not always communicated explicitly. Thus, the ability to infer others' expectations in context and understand how these expectations influence others' emotions is an important aspect of our social intelligence. Prior work suggests that an abstract understanding of how expectations modulate emotional responses may not emerge until 7 to 8 years of age. Using a novel paradigm that capitalizes on intuitive physics to generate contextually plausible expectations, we present evidence for expectation-based emotion inference in preschool-aged children. Given two bowlers who experienced identical final outcomes (hitting 3 of 6 pins), we varied the trajectory of their balls such that one would initially expect to hit all pins (high-expectation), while the other would expect to hit none (low-expectation). In Experiment 1, both 4- and 5-year-olds appropriately adjusted characters' happiness ratings upward (low-expectation) or downward (high-expectation) relative to their initial emotions; however, only 5-year-olds made adjustments robust enough to manifest as higher final ratings for the low-expectation than the high-expectation character. In Experiments 2-3, we replicate these results and show that 5-year-olds reliably differentiate the characters' emotions even when their expectations must be inferred from context. An internal meta-analysis revealed a robust and consistent effect across the three experiments. Together, these findings provide the earliest evidence for expectation-based emotion reasoning and suggest that the ability to spontaneously generate and consider others' expectations continues to develop during preschool years. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap.

Research on social cognition has fruitfully applied computational modeling approaches to explain how observers understand and reason about others' mental states. By contrast, there has been less work on modeling observers' understanding of emotional states. We propose an intuitive theory framework to studying affective cognition-how humans reason about emotions-and derive a taxonomy of inferences within affective cognition. Using this taxonomy, we review formal computational modeling work on such inferences, including causal reasoning about how others react to events, reasoning about unseen causes of emotions, reasoning with multiple cues, as well as reasoning from emotions to other mental states. In addition, we provide a roadmap for future research by charting out inferences-such as hypothetical and counterfactual reasoning about emotions-that are ripe for future computational modeling work. This framework proposes unifying these various types of reasoning as Bayesian inference within a common "intuitive Theory of Emotion." Finally, we end with a discussion of important theoretical and methodological challenges that lie ahead in modeling affective cognition.

Happier than thou? A self-enhancement bias in emotion attribution.

People tend to judge themselves as exhibiting above average levels of desirable traits-including competence, kindness, and life satisfaction-but does this self-enhancement extend to emotional responses? Here, we explore this question by having people attribute emotions to themselves and others following simple gambles. We demonstrate that people display an emotional self-enhancement bias that varies with the context of the emotion-eliciting situation. People judge themselves as experiencing more positive emotional reactions on average, and they also believed that others' emotions are more sensitive to gamble outcomes, such that people judge others to experience stronger negative affect in response to negative outcomes (Study 1). This self-enhancement bias further tracks social distance, such that people attribute less positive and more negative emotion to more dissimilar, as compared with more similar others (Study 2). People also predict less favorable emotional states for themselves and others experiencing events in the future, as compared with the present (Study 3), suggesting that this attribution bias extends across multiple dimensions of psychological distance. Broadly, these data suggest that people exhibit self-enhancement in emotion attribution, but do so in subtle ways that depend on situational and social factors. (PsycINFO Database Record

Interpersonal emotion regulation: Implications for affiliation, perceived support, relationships, and well-being.

People often recruit social resources to manage their emotions, a phenomenon known as interpersonal emotion regulation (IER). Despite its importance, IER's psychological structure remains poorly understood. We propose that two key dimensions describe IER: (a) individuals' tendency to pursue IER in response to emotional events, and (b) the efficacy with which they perceive IER improves their emotional lives. To probe these dimensions, we developed the Interpersonal Regulation Questionnaire (IRQ), a valid and reliable measure of individual differences in IER. Factor analyses of participants' responses confirmed tendency and efficacy as independent dimensions of IER (Study 1; N = 285), and demonstrated independence between how individuals engage with IER in response to negative, versus positive, emotion. In Study 2 (N = 347), we found that individuals high in IER tendency and efficacy are more emotionally expressive, empathetic, and socially connected. Two subsequent studies highlighted behavioral consequences of IER dimensions: people high in IER tendency sought out others more often following experimentally induced emotion (Study 3; N = 400), and individuals high in IER efficacy benefitted more from social support after real-world emotional events (Study 4; N = 787). Finally, a field study of social networks in freshman dormitories revealed that individuals high in IER tendency and efficacy developed more supportive relationships during the first year of college (Study 5; N = 193). These data (a) identify distinct dimensions underlying IER, (b) demonstrate that these dimensions can be stably measured and separated from related constructs, and (c) reveal their implications for relationships and well-being. (PsycINFO Database Record