Disagreeing with others about how to interpret a social interaction is a common occurrence. We often find ourselves offering divergent interpretations of others’ motives, intentions, beliefs, and emotions. Remarkably, philosophical accounts of how we understand others do not explain, or even attempt to explain such disagreements. I argue these disparities in social interpretation stem, in large part, from the effect of social categorization and our goals in social interactions, phenomena long studied by social psychologists. I argue we ought to expand (...) our accounts of how we understand others in order to accommodate these data and explain how such profound disagreements arise amongst informed, rational, well-meaning individuals. (shrink)

We have various strategies available to us for understanding another person’s state of mind. Cognitive empathy may be achieved by mental simulation, i.e. by imagining yourself in another’s situation and figuring out what you would think and feel in that situation. Alternatively, you could consider all the relevant information about the person’s situation and folk psychology and draw a sophisticated inference to the best explanation of that person’s perspective. In this chapter, I examine the conditions under which we are likely (...) to use these two familiar strategies for cognitive empathy and when they are likely to be effective. In addition, I discuss a third underexplored pattern of reasoning in understanding others. Self-serving goals, such as anxiety reduction, self-esteem, and confirmation of one’s worldview, distort cognitive empathy. I consider these different strategies in light of hybrid theories of cognitive empathy. (shrink)

How are biases encoded in our representations of social categories? Philosophical and empirical discussions of implicit bias overwhelmingly focus on salient or statistical associations between target features and representations of social categories. These are the sorts of associations probed by the Implicit Association Test and various priming tasks. In this paper, we argue that these discussions systematically overlook an alternative way in which biases are encoded, that is, in the dependency networks that are part of our representations of social categories. (...) Dependency networks encode information about how features in a conceptual representation depend on each other. This information determines the degree of centrality of a feature for a conceptual representation. Importantly, centrally encoded biases systematically disassociate from those encoded in salient-statistical associations. Furthermore, the degree of centrality of a feature determines its cross-contextual stability: in general, the more central a feature is for a concept, the more likely it is to survive into a wide array of cognitive tasks involving that concept. Accordingly, implicit biases that are encoded in the central features of concepts are predicted to be more resilient across different tasks and contexts. As a result, the distinction between centrally encoded and salient-statistical biases has important theoretical and practical implications. (shrink)