Motivation and the Limits of Empathy
One of the primary topics of study in the EMP Lab is empathy: the ability to vicariously resonate with and share the experiences and feelings of others. The EMP Lab examines when and why people feel and behave empathically toward others. This line of research is united by the framework that empathy is often a motivated choice: many apparent limitations of empathy may result from how people strategically weigh its costs and benefits (Cameron, Inzlicht, & Cunningham, under review; Cameron & Rapier, 2016). This research focuses on motivational factors that cause people to either down-regulate or up-regulate empathy, as well as emotion regulation mechanisms (e.g., reappraisal, situation selection, attention allocation) that shape empathic outcomes.
In one line of research, the lab examines the motivated regulation of empathy in the context of large-scale crises, such as natural disasters and genocides. Much research has established that people tend to feel more compassion for single identifiable victims (e.g., Cecil the Lion, Baby Jessica) than large masses of victims. Empathy seems to be fundamentally innumerate, or insensitive to the scope of mass tragedies. Yet research has found that this “compassion collapse” depends on having the motivation and ability to regulate emotions (Cameron & Payne, 2011, JPSP). People only show less empathy for many victims than for single victims of disasters when they are expect to incur a financial cost of helping, and only when they can skillfully regulate their emotions. In ongoing research, we are exploring how other motivations, such as the desire to avoid emotional exhaustion, may cause people to avoid empathy and produce compassion collapse. The lab is also examining motivational interventions that might counteract compassion collapse, such as changing people’s lay theories about the nature of empathy.
In related work, the lab has examined motivated empathy regulation in the context of stigmatized out-groups, such as drug addicts and homeless individuals. Much research has established that people tend to dehumanize, or deny mental states to, stigmatized targets. Empathy seems to be fundamentally parochial, or insensitive to the suffering of out-groups. We have found that this dehumanization effect depends on the motivation to avoid emotional exhaustion (Cameron, Harris, & Payne, 2016, SPPS). People anticipate more emotional exhaustion from helping stigmatized (vs. non-stigmatized) targets, and this is associated with greater dehumanization. Moreover, manipulating people’s beliefs about whether empathy will be exhausting can remove the dehumanization effect.
Currently, the lab is further examining empathy as a choice through the development of the Empathy Selection Task: a free choice task in which people can choose whether to select into or out of empathy-eliciting situations (Cameron, Ferguson, Hutcherson, Scheffer, & Inzlicht, under review). This work reveals that people have a strong preference to avoid choosing empathy for others, and that this is associated with perceptions of empathy as effortful, negative, and inefficacious. Related work extending this task finds that people have a similar preference to avoid choosing compassion (Cameron, Scheffer, & Inzlicht, in preparation).
More recently, we have been extending the motivated empathy framework to understand variation in empathy in political contexts (e.g., among political activists at the Iowa Caucus; Cameron, Scherer, Scheffer, & McKee, in preparation) and professional contexts (e.g., among practicing physicians; Cameron & Inzlicht, in preparation).
Affective Dynamics of Moral Judgment
When we decide whether an action is morally right or wrong, or whether a person deserves punishment and blame, are we driven by the heart or the head? The answer to this question, which traces from Plato through Hume to the present day, turns out to be both. Emotions are multifaceted and complex phenomena, built from concepts, core affect, and the situations around us. Paying due attention to the dynamic construction of emotions can greatly advance our knowledge about how people manage their moral lives. In recent work, we have applied constructionist models of the mind to understand the relationship between affect, emotions, and moral judgment (Cameron, Lindquist, & Gray, 2015, PSPR; Spring, Cameron, Gray, & Lindquist, BBS). This constructionist perspective is novel for the field of moral psychology, because it challenges assumptions about emotions and moral domains as natural kinds, and instead suggests examining how domain-general mechanisms of affect, attention, and conceptual knowledge interact to shape moral decision-making.
In the EMP Lab’s other primary line of research, we use tools from social cognition–including implicit measurement and mathematical modeling–to understand individual differences in moral judgment and empathy for pain. Many models of moral judgment emphasize the importance of implicit moral evaluations: spontaneous, unintentional evaluations of the morality of actions or persons. Yet little research has formally modeled such implicit moral evaluations in a way that separates their influence from other component processes that might be active during moral decision-making. The EMP Lab has developed a novel sequential priming measure of moral judgment called the Moral Categorization Task, along with a multinomial model that quantifies individual differences in implicit moral evaluations (Cameron, Payne, Sinnott-Armstrong, Scheffer, & Inzlicht, 2017). This work finds that implicit moral evaluations converge with moral personality (e.g., moral identity, guilt proneness, psychopathic tendencies) and associate with voting behavior. We are currently using this paradigm to examine implicit moral evaluations in patients with damage to the ventromedial prefrontal cortex (Cameron, Reber, Spring, & Tranel, under review), and in incarcerated psychopathic offenders. We have also adapted a similar approach to understand individual differences in intentional and unintentional empathy for the pain of others (Cameron, Spring, & Todd, in press). Implicit measurement and formal modeling together allow for theoretical and methodological refinement in exploring the implicit moral evaluations that are at the heart of many moral psychology theories.