This too shall pass, but when? Children's and adults' beliefs about the time duration of emotions, desires, and preferences.

This research investigated children's and adults' understanding of the mind by assessing beliefs about the temporal features of mental states. English-speaking North American participants, varying in socioeconomic status (Study 1: N = 50 adults; Study 2: N = 112, 8- to 10-year-olds and adults; and Study 3: N = 116, 5- to 7-year-olds and adults; tested 2017-2022), estimated the duration (seconds to a lifetime) of emotions, desires (wanting), preferences (liking), and control trials (e.g., napping and having eyes). Participants were 56% female and 44% male; 32% Asian, 1% Black, 13% Hispanic/Latino, 38% White (non-Hispanic/Latino), and 16% multiracial or another race/ethnicity. Children and adults judged that preferences last longer than emotions and desires, with age differences in distinguishing specific emotions by duration ( η p 2 s > .03 $$ {\eta}_{\mathrm{p}}^2\mathrm{s}>.03 $$ ). By 5 to 7 years, ideas about the mind include consideration of time.

A synthesis of evidence for policy from behavioural science during COVID-19.

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

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.

A neural signature of social support mitigates negative emotion.

Social support can mitigate the impact of distressing events. Such stress buffering elicits activity in many brain regions, but it remains unclear (1) whether this activity constitutes a stable brain signature, and (2) whether brain activity can predict buffering across people. Here, we developed a neural signature that predicted social buffering of negative emotion in response to real life stressors. During neuroimaging, participants (n = 95) responded to stressful autobiographical memories either naturally, or by imagining a conversation with a peer. Using supervised dimensionality reduction and machine learning techniques, we identified a spatio-temporal neural signature that distinguished between these two trials. Activation of this signature was associated with less negative affect across trials, and people who most activated the signature reported more supportive social connections and lower loneliness outside the lab. Together, this work provides a behaviorally relevant neurophysiological marker for social support that underlies stress buffering.

Social microclimates and well-being.

Emotional well-being has a known relationship with a person's direct social ties, including friendships; but do ambient social and emotional features of the local community also play a role? This work takes advantage of university students' assignment to different local networks-or "social microclimates"-to probe this question. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, we quantify the collective impact of individual, social network, and microclimate factors on the emotional well-being of a cohort of first-year college students. Results indicate that well-being tracks individual factors but also myriad social and microclimate factors, reflecting one's peers and social surroundings. Students who belonged to emotionally stable and tight-knit microclimates (i.e., had emotionally stable friends or resided in densely connected residence halls) reported lower levels of psychological distress and higher levels of life satisfaction, even when controlling for factors such as personality and social network size. Although rarely discussed or acknowledged in the policies that create them, social microclimates are consequential to well-being, especially during life transitions. The effects of microclimate factors are small relative to some individual factors; however, they explain unique variance in well-being that is not directly captured by emotional stability or other individual factors. These findings are novel, but preliminary, and should be replicated in new samples and contexts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Toward a social psychology of cynicism.

Cynicism is the attitude that people are primarily motivated by self-interest. It tracks numerous negative outcomes, and yet many people are cynical. To understand this 'cynicism paradox', we review and call for more social psychological work on how cynicism spreads, with implications for how we might slow it down.

Belief in the Utility of Cross-Partisan Empathy Reduces Partisan Animosity and Facilitates Political Persuasion.

In polarized political environments, partisans tend to deploy empathy parochially, furthering division. We propose that belief in the usefulness of cross-partisan empathy-striving to understand other people with whom one disagrees politically-promotes out-group empathy and has powerful ramifications for both intra- and interpersonal processes. Across four studies (total N = 4,748), we examined these predictions in online and college samples using surveys, social-network analysis, preregistered experiments, and natural-language processing. Believing that cross-partisan empathy is useful is associated with less partisan division and politically diverse friendship networks (Studies 1 and 2). When prompted to believe that empathy is a political resource-versus a political weakness-people become less affectively polarized (Study 3) and communicate in ways that decrease out-partisans' animosity and attitudinal polarization (Study 4). These findings demonstrate that belief in cross-partisan empathy impacts not only individuals' own attitudes and behaviors but also the attitudes of those they communicate with.

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.

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).

Impaired empathic accuracy following damage to the left hemisphere.

Failing to understand others accurately can be extremely costly. Unfortunately, events such as strokes can lead to a decline in emotional understanding. Such impairments have been documented in stroke patients and are widely hypothesized to be related to right-hemisphere lesions, as well as to the amygdala, and are thought to be driven in part by attentional biases, for example, less fixation on the eyes. Notably, most of the previous research relied on measurements of emotional understanding from simplified cues, such as facial expressions or prosody. We hypothesize that chronic damage to the left hemisphere could hinder empathic accuracy and emotion recognition in naturalistic social settings that require complex language comprehension, even after a patient regains core language capacities. To assess this notion, we use an empathic accuracy task and eye-tracking measurements with chronic stroke patients with either right (N = 13) or left (N = 11) hemispheric damage-together with aged-matched controls (N = 15)-to explore the patients' understanding of others' affect inferred from stimuli that separates audio and visual cues. While we find that patients with right-hemisphere lesions showed visual attention bias compared to the other two groups, we uncover a disadvantage for patients with left-hemisphere lesions in empathic accuracy, especially when only auditory cues are present. These results suggest that patients with left-hemisphere damage have long-lasting difficulties comprehending real-world complex emotional situations.

Policy stringency and mental health during the COVID-19 pandemic: a longitudinal analysis of data from 15 countries.

BACKGROUND: To date, public health policies implemented during the COVID-19 pandemic have been evaluated on the basis of their ability to reduce transmission and minimise economic harm. We aimed to assess the association between COVID-19 policy restrictions and mental health during the COVID-19 pandemic. METHODS: In this longitudinal analysis, we combined daily policy stringency data from the Oxford COVID-19 Government Response Tracker with psychological distress scores and life evaluations captured in the Imperial College London-YouGov COVID-19 Behaviour Tracker Global Survey in fortnightly cross-sections from samples of 15 countries between April 27, 2020, and June 28, 2021. The mental health questions provided a sample size of 432 642 valid responses, with an average of 14 918 responses every 2 weeks. To investigate how policy stringency was associated with mental health, we considered two potential mediators: observed physical distancing and perceptions of the government's handling of the pandemic. Countries were grouped on the basis of their response to the COVID-19 pandemic as those pursuing an elimination strategy (countries that aimed to eliminate community transmission of SARS-CoV-2 within their borders) or those pursuing a mitigation strategy (countries that aimed to control SARS-CoV-2 transmission). Using a combined dataset of country-level and individual-level data, we estimated linear regression models with country-fixed effects (ie, dummy variables representing the countries in our sample) and with individual and contextual covariates. Additionally, we analysed data from a sample of Nordic countries, to compare Sweden (that pursued a mitigation strategy) to other Nordic countries (that adopted a near-elimination strategy). FINDINGS: Controlling for individual and contextual variables, higher policy stringency was associated with higher mean psychological distress scores and lower life evaluations (standardised coefficients ß=0·014 [95% CI 0·005 to 0·023] for psychological distress; ß=-0·010 [-0·015 to -0·004] for life evaluation). Pandemic intensity (number of deaths per 100 000 inhabitants) was also associated with higher mean psychological distress scores and lower life evaluations (standardised coefficients ß=0·016 [0·008 to 0·025] for psychological distress; ß=-0·010 [-0·017 to -0·004] for life evaluation). The negative association between policy stringency and mental health was mediated by observed physical distancing and perceptions of the government's handling of the pandemic. We observed that countries pursuing an elimination strategy used different policy timings and intensities compared with countries pursuing a mitigation strategy. The containment policies of countries pursuing elimination strategies were on average less stringent, and fewer deaths were observed. INTERPRETATION: Changes in mental health measures during the first 15 months of the COVID-19 pandemic were small. More stringent COVID-19 policies were associated with poorer mental health. Elimination strategies minimised transmission and deaths, while restricting mental health effects. FUNDING: None.

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.

Mental Health During the First Year of the COVID-19 Pandemic: A Review and Recommendations for Moving Forward.

COVID-19 has infected millions of people and upended the lives of most humans on the planet. Researchers from across the psychological sciences have sought to document and investigate the impact of COVID-19 in myriad ways, causing an explosion of research that is broad in scope, varied in methods, and challenging to consolidate. Because policy and practice aimed at helping people live healthier and happier lives requires insight from robust patterns of evidence, this article provides a rapid and thorough summary of high-quality studies available through early 2021 examining the mental-health consequences of living through the COVID-19 pandemic. Our review of the evidence indicates that anxiety, depression, and distress increased in the early months of the pandemic. Meanwhile, suicide rates, life satisfaction, and loneliness remained largely stable throughout the first year of the pandemic. In response to these insights, we present seven recommendations (one urgent, two short-term, and four ongoing) to support mental health during the pandemic and beyond.

Social belonging, compassion, and kindness: Key ingredients for fostering resilience, recovery, and growth from the COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has led to increases in anxiety, depression, posttraumatic stress disorder, burnout, grief, and suicide, particularly for healthcare workers and vulnerable individuals. In some places, due to low vaccination rates and new variants of SARS-CoV-2 emerging, psychosocial strategies for remaining resilient during an ongoing multi-faceted stressor are still needed. Elsewhere, thanks to successful vaccination campaigns, some countries have begun reopening but questions remain regarding how to best recover, adjust, and grow following the collective stress and loss caused by the pandemic. METHOD: Here, we briefly describe three evidence-based strategies that can help foster individual and collective recovery, growth, and resilience: cultivating social belonging, practicing compassion, and engaging in kindness. RESULTS: Social belonging involves a sense of interpersonal connectedness. Practicing compassion involves perceiving suffering as part of a larger shared human experience and directing kindness toward it. Finally, engaging in kindness involves prosocial acts toward others. CONCLUSIONS: Together, these strategies can promote social connectedness and help reduce anxiety, stress, and depression, which may help psychologists, policymakers, and the global community remain resilience in places where cases are still high while promoting adjustment and growth in communities that are now recovering and looking to the future.

Belief in Altruistic Motives Predicts Prosocial Actions and Inferences.

Are humans ever truly altruistic? Or are all actions, however noble, ultimately motivated by self-interest? Psychologists and philosophers have long grappled with this question, but few have considered laypeople's beliefs about the nature of prosocial motives. Here we examine these beliefs and their social correlates across two experiments (N = 445). We find that people tend to believe humans can be, and frequently are, altruistically motivated-echoing prior work. Moreover, people who more strongly believe in altruistic motives act more prosocially themselves-for instance, sacrificing greater amounts of money and time to help others-a relationship that holds even when controlling for trait empathy. People who believe in altruistic motives also judge other prosocial agents to be more genuinely kind, especially when agents' motives are ambiguous. Lastly, people independently show a self-serving bias-believing their own motives for prosociality are more often altruistic than others'. Overall, this work suggests that believing in altruistic motives predicts the extent to which people both see altruism and act prosocially, possibly reflecting the self-fulfilling nature of such lay theories.

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.

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.

Beliefs About Self-Compassion: Implications for Coping and Self-Improvement.

Self-compassion-treating oneself with care and understanding during difficult times-promotes adaptive coping and self-improvement. Nonetheless, many people are not self-compassionate. We examined a key barrier people face to treating themselves self-compassionately: their negative beliefs about self-compassion (i.e., that it leads to complacency, indulgence, or irresponsibility). Across three studies, the more people held these negative beliefs, the less self-compassionately they reported responding to a real-world event (Study 2) and hypothetical emotional challenges (Studies 1 and 3). Self-compassionate responding, in turn, predicted adaptive coping strategies and intentions for self-improvement. Experimentally inducing people to hold positive, as opposed to negative, beliefs about self-compassion predicted self-compassionate responding 5 to 7 days later (Study 3). By recognizing and targeting peoples' beliefs, our findings highlight the importance of reducing such beliefs that are barriers to practicing self-compassion, as a means to improve the way people respond to difficult times.

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).

Conservative and liberal attitudes drive polarized neural responses to political content.

People tend to interpret political information in a manner that confirms their prior beliefs, a cognitive bias that contributes to rising political polarization. In this study, we combined functional magnetic resonance imaging with semantic content analyses to investigate the neural mechanisms that underlie the biased processing of real-world political content. We scanned American participants with conservative-leaning or liberal-leaning immigration attitudes while they watched news clips, campaign ads, and public speeches related to immigration policy. We searched for evidence of "neural polarization": activity in the brain that diverges between people who hold liberal versus conservative political attitudes. Neural polarization was observed in the dorsomedial prefrontal cortex (DMPFC), a brain region associated with the interpretation of narrative content. Neural polarization in the DMPFC intensified during moments in the videos that included risk-related and moral-emotional language, highlighting content features most likely to drive divergent interpretations between conservatives and liberals. Finally, participants whose DMPFC activity closely matched that of the average conservative or the average liberal participant were more likely to change their attitudes in the direction of that group's position. Our work introduces a multimethod approach to study the neural basis of political cognition in naturalistic settings. Using this approach, we characterize how political attitudes biased information processing in the brain, the language most likely to drive polarized neural responses, and the consequences of biased processing for attitude change. Together, these results shed light on the psychological and neural underpinnings of how identical information is interpreted differently by conservatives and liberals.