Jamil Zaki - US grants

DESCRIPTION (provided by applicant): Relationships vitally support individuals' physical and mental health; social isolation, for instance, constitutes a powerful risk factor for mortality, cardiovascular disease, and mood disorders. The predominant scientific model of this phenomenon holds that social ties bolster health through stress buffering: reduced stress reactivity in the presence of supportive others. Although stress buffering offers considerable explanatory power, not all relationships reduce individuals' stress; some forms of support fail to reduce-and can even worsen-subjective and physiological indices of stress. Scientists have begun to examine the specific features of relationships and support behaviors that may moderate the effects of relationships on stress and mental health, but these efforts have been limited in at least two ways. First, this work focuses almost exclusively on relationship-level predictors of stress buffering (e.g., marital satisfaction) as opposed to characteristics of individuals who effectively buffer others' stress. This is especially important because research on empathy suggests that individuals who insightfully understand others' affective states (empathic accuracy) and tend to vicariously share those states (affect sharing) might also engage in prosocial and adaptive interpersonal behaviors. Second, existing work has tended to examine stress buffering using naturalistic self-report measures (e.g., daily diaries) or controlle laboratory tasks, but has rarely combined these techniques, preventing an integration of knowledge garnered by each approach. The proposed work will address these gaps in knowledge and propose a multilevel, integrative, and conceptually novel model of stress buffering. This model posits that empathic individuals provide high quality support that, in turn, reduces support recipients' stress and negative affect. We will test this model using a hybrid laboratory and field paradigm our group has recently developed. We will select pairs of close friends and examine empathy in one member of each pair through a behavioral marker of empathic accuracy-performance on an accuracy task we have developed-and a neural marker of affect sharing we have also developed-individuals' engagement of mesolimbic dopaminergic targets while watching their friend receive monetary prizes. Friend pairs will then complete daily diaries reporting on their patterns of stress and social support. Finally, we will collect samples f diurnal salivary cortisol-a canonical neuroendocrine measure of stress reactivity-and reports of sustained threat and loss (subdomains of the Negative Valence Systems RDoC domain), from the other member of the friend pair. We predict that neural and behavioral markers of empathy in support providers will predict support recipients' reductions in endocrine responses to stress, as well as reductions in subjective sustained threat and loss, and that this relationship will be mediated by the quality of support high empathy individuals provide. These data will provide multiple novel insights concerning the social underpinnings of psychological well-being and pave the way for translational work aimed at improving mental health on a broad scale.

Empathy - sharing and understanding of others' emotions - serves as a social "glue" that produces generosity, strengthens social ties, and reduces prejudice. Despite its importance, empathy is also fragile, and often breaks down just when it is needed most. This is especially true in interactions between different social groups. People often dismiss the opinions of individuals with whom they disagree, fail to engage with the emotions of outgroup members, and avoid others' suffering. Empathic breakdowns have severe and wide-ranging consequences: producing polarizing ideological stalemates, fomenting conflict, and driving bullying in schools. Empathy is often considered a relatively stable characteristic that is not amenable to change. In contrast, Jamil Zaki (Stanford University) and colleagues have developed a novel theoretical approach, which proposes that empathy is a motivated process; thus, it could be modified by shifting the motives that may drive one towards or away from engaging in empathetic responses. This project will test two theoretical approaches that could shift motivational processes and "expand" individuals' empathy, especially in challenging intergroup situations.

This research leverages two social psychological concepts - mindsets and social influence - to shift empathetic responses. Mindsets describe people's beliefs about psychological phenomena such as intelligence and personality. Whereas some people believe these characteristics are stable and unchangeable (so-called "fixed mindsets"), others believe these phenomena are malleable and under their control ("growth mindsets"). This project will test whether encouraging a growth mindset of empathy will cause shifts in motivation to engage in empathetic responses. The second concept, social influence, draws on findings that people are motivated to change their behavior, beliefs, and opinions to match those of others; therefore, providing a norm that others value empathy could consequently promote empathetic responses. This project will measure the effects of shifting mindsets and social norms on (i) individuals' ability to understand others' emotions, (ii) their willingness to take the perspective of people who differ from them, and (iii) the strength and diversity of their social relationships. Additionally, the research will test whether motivational shifts towards empathetic responses increase generous behaviors and decrease bullying among high school students. Overall, this work stands to make key advances both in the basic science of empathy and in how this science can be applied to critical social settings. With respect to basic science, this work will improve understanding of the process and mechanism through which empathy can be expanded, and also test a novel theory of empathy as a motivated process. With respect to application, this work can help to remedy some of the harmful consequences of empathic breakdowns including intergroup discrimination, bullying, and selfishness in the context of charitable giving.

The purpose of this project is to develop computational and brain-based models of emotion cue integration: people?s inferences about others? emotions based on dynamic, multimodal cues. Observers often decide how targets feel based on cues such as facial expressions, prosody, and language. Such inferences scaffold healthy social interaction, and abnormal inference both marks and exacerbates social deficits in numerous psychiatric disorders. Psychologists and neuroscientists have studied emotion inference for decades, but the vast majority of this work employs simplified social cues, such as vignettes or static images of faces. By contrast, ?real world? emotion cues are complex, dynamic, and multimodal. Cue integration?inference based on naturalistic emotion information?likely differs from simpler inference at cognitive and neural levels, but this phenomenon remains poorly understood. This means that scientists lack a clear model of how observers adaptively process complex emotion cues, and how that processing goes awry in mental illness. Especially lacking are mechanistic models that can describe the computations and brain processes involved in cue integration with sufficient precision to predict inference in new cases, observers, and samples. This project will merge tools from social psychology, computer science, and neuroscience to generate a novel and rigorous model of emotion cue integration. We have demonstrated that in the face of complex emotion cues, observers dynamically ?weight? cues from each modality (e.g., visual, linguistic) over time, a process that (i) tracks shifts in brain activity and connectivity; and (ii) can be captured using Bayesian models. Here, we will expand this work in several ways. First, we will develop precise computational tools to isolate features of emotion cues?such as facial movements, prosody, and linguistic sentiment?that track observers? use of each cue modality during integration. Second, we will develop multi-region ?signatures? of brain activity and connectivity that track emotion inference in each modality. We will use these signatures in conjunction with machine learning to predict unimodal emotion inference and cue integration in new observers and samples, based on brain data alone. Third, we will explore the context-dependence of naturalistic emotion inference by testing whether reinforcement learning can bias observers? cue integration and accompanying brain signatures. Finally, we will model computational and neural abnormalities associated with cue integration in patients with Major Depressive Disorder and Bipolar Disorder. At the level of basic science, these data will generate a fundamentally new?and more naturalistic?approach to the neuroscience of emotion inference. The computational and brain metrics we produce will also be made publically available to facilitate the open and cumulative study of emotion inference across labs. At a translational level, we will provide a mechanistic, rich account of abnormal emotion inference in mood disorders, paving the way for computational and brain markers that can be used to assess social dysfunction and treatment efficacy in these and other mental illnesses.

PROJECT SUMMARY The purpose of this project is to examine social factors in the long-term mental health of young adults. Depression, anxiety, and loneliness have steeply risen among college and university students in the last decades, creating an enormous public health burden. Mental health difficulties promise to intensify during and after the COVID-19 pandemic, making it especially urgent to examine and amplify sources of resilience among young adults. Decades of evidence demonstrate that social connectedness, in the form of subjective belonging, objective social ties, and supportive interpersonal interactions, bolster mental health in several key ways. We propose that connectedness early in college, and students? ability to regulate their emotions through social interactions, could play a pivotal role in encouraging long-term mental health. Though foundational, past work is limited in its ability to test these predictions because it typically examines (i) dyadic relationships rather than broader networks, (ii) the effect of small numbers of social factors, independently, and (iii) short time spans. These limitations are especially relevant to undergraduate settings, as student social life is centered in broad communities on which individuals depend for social support. This project will merge tools from social psychology, network analysis, and neuroscience to provide a rich, precise, and longitudinal account of how social connectedness supports young adult mental health over time. Our team has mapped the social networks formed by a large (n > 850) cohort of incoming university students, and combined this with ecological momentary assessment of students? interactions and indices of mental health. We have found novel evidence that (i) ?social microclimates,? such as the empathy of a student?s neighbors, affect individual well being, (ii) students search their social networks for supportive peers when under stress, (iii) peer interactions mitigate stress over time, and (iv) lonely students under-perceive close social ties, and under-utilize social resources. Here, we will expand this work in several ways. First, we will incorporate a longitudinal approach: measuring students? connectedness and well being over their college career. We will combine these data with cutting-edge predictive modeling to quantify how social ties formed early in college relate to well being in later years, as well as students? subsequent ?mental health trajectories.? Second, we will recruit a longitudinal replication cohort to establish the robustness of our effects. Third, we will build on previous neuroimaging work of our team to probe neural ?signatures? of social connectedness and examine their relationship to other measures of connection, and to well being, over time. At the level of basic science, this project will represent a novel, naturalistic approach to the study of social factors in mental health, and produce a large-scale, multifaceted dataset, which will be made publicly available to facilitate the collaborative and cumulative study of social connection. At a translational level, the resulting data can pave the way for policies aimed at fostering stronger social ties?and mental health?among a broad swath of the population.