Researchers at the Stanford Social Neuroscience Lab focus broadly on how people connect with, respond to, and care for each other. Since this topic can be approached from many directions, we research a wide variety of topics.
For an in-depth introduction on the overview of our topic area we would recommend the following papers
Below we highlight some major threads we explore across our research areas.
The Nature and Consequences of Empathy
Audience members’ palms sweat while they watch a tightrope walker teeter over a precipice. Friends wonder how to help each other through struggles, and customers wonder whether a used car salesman is genuinely happy to see them. All of these instances represent forms of empathy: sharing, thinking about, and feeling concern for others’ emotions.
The SSNL studies empathy through a wide range of approaches and methods. We differentiate between different “pieces” of empathy, such as vicariously taking on others’ feelings (emotional empathy), thinking about their experiences (cognitive empathy) and feeling a motivation to improve their well-being (empathic concern, see Zaki, 2016; Zaki & Ochsner, 2016). Our work probes the brain processes that support pieces of empathy, (e.g., Zaki et al., 2016; Morelli et al., 2018), and use computational models to describe how people make sense of others’ emotions based on facial expressions, language and other cues (Ong et al. 2015, 2019; Zaki, 2013). We’re also interested in when and how empathy leads people to accurate, versus inaccurate impressions of what others are going through, with an eye towards improving interpersonal understanding (Zaki & Ochsner, 2011).
Our lab also explores the benefits of empathy, for example in reducing individuals’ stress and in building relationships (Morelli et al., 2015, Zaki, 2016). We are also interested in the noisy, but powerful role of empathy in guiding moral decisions (Zaki, 2016).
Connections and Communities
Empathy is often viewed as residing within individuals, or connecting pairs of people, such as married couples, a parent and child, or doctor and patient. But people of course exist in broader communities, like towns, teams, companies, and schools. Through a collaboration with Stanford, the SSNL is examining the ways that empathy affects these larger groups.
Using a combination of social network analysis, personality measurements, and neuroimaging, our lab has probed how empathy tracks individuals’ position and role in new communities (Morelli et al., 2017) and brain processes involved in detecting people who are central to one’s social group (Morelli et al., 2018). We are now extending this work to examine how, when, and why individuals’ social ties help them cope effectively with stress.
People's lay beliefs about the (dis)utility of outgroup empathy can have important implications for their social and political attitudes. A line of research in the lab investigates how challenging these preconceived ideas can foster outgroup empathy and promote social change. For example, we are investigating if learning about the utility of empathizing across party lines can help decrease partisan animosity and foster more productive political discussions (Santos et al., in press). We are also testing the connection between feeling empathy toward a particular target and policy endorsements (e.g., support for social welfare).
Caring and understanding often crumble just when they’re needed the most: a problem that characterizes polarized political climates, callous physician-patient interactions, and burnt-out workplaces (Zaki & Cikara, 2015). The SSNL has probed ways to rebuild empathy under these circumstances (Weisz & Zaki, 2017).
Critically, we highlight that empathy is not an emotional reflex, but rather an experience people choose in response to motives that drive them to approach or avoid engaging with others’ emotions (Zaki, 2014; Weisz & Zaki, 2018). This means that by bolstering empathic motives, scientists should be able to “grow” empathy even under hard circumstances.
We have demonstrated that motivation-based manipulations, such as social norms and “growth mindsets,” indeed inspire people to empathize even when they might not otherwise (Schumman et al., 2014; Nook et al., 2016). We have also found that empathy-building interventions built on these principles—at least in some contexts—can improve connection and social well being (Weisz et al., in press). Our lab is now exploring a range of interventions, for including virtual reality simulations (Herrera et al., 2018) and engagement with narrative arts, that can broaden empathy even towards people from different groups, and across social division.
A major factor in mental health is people’s ability to successfully regulate their emotions, tuning their feelings in ways that are sustainable and adaptive. Social interactions are a key ingredient for this ability. People often vent to others, seeking support and commiseration to regulate their own emotions; parents, friends, partners, and colleagues also work hard to regulate each other.
The SSNL has developed a framework for understanding such interpersonal emotion regulation (Zaki & Williams, 2013). We have also examined IER as varying across people, in ways that predict well-being, social connection, and the ability to form supportive relationships (Williams et al., 2018). Finally, we are probing the connection between empathy and IER, especially in cases when helping someone requires making them feel negative emotions (Zaki, 2020). We are now expanding this work to focus on IER in broader communities, and consider ways of helping people improve their abilities to manage emotions through social interactions.
Our views regarding right and wrong strongly influence our group affiliations, collective actions, and policy endorsements. With this in mind, we are interested in understanding how motivated affect can help elucidate the ways moral beliefs are perceived, entrenched, and distributed through social networks.
In this line of work, we have ongoing empirical research investigating the precursors and downstream consequences of political attitude moralization. Moreover, we have been using natural language processing to examine how political topics - such as immigration, abortion, and healthcare - can become imbued with moral relevance (e.g., seen as matters of right and wrong) through time and partisan dynamics.