Cognitive science can help us understand which explanations people might expect, and in which format they frame these explanations, whether causal, counterfactual, or teleological (i.e., purpose-oriented). Understanding the relevance of these concepts is crucial for building good explainable AI (XAI) which offers recourse and actionability. Focusing on autonomous driving, a complex decision-making domain, we report empirical data from two surveys on (i) how people explain the behavior of autonomous vehicles in 14 unique scenarios (N1=54), and (ii) how they perceive these explanations in terms of complexity, quality, and trustworthiness (N2=356). Participants deemed teleological explanations significantly better quality than counterfactual ones, with perceived teleology being the best predictor of perceived quality and trustworthiness. Neither the perceived teleology nor the quality were affected by whether the car was an autonomous vehicle or driven by a person. This indicates that people use teleology to evaluate information about not just other people but also autonomous vehicles. Taken together, our findings highlight the importance of explanations that are framed in terms of purpose rather than just, as is standard in XAI, the causal mechanisms involved. We release the 14 scenarios and more than 1,300 elicited explanations publicly as the Human Explanations for Autonomous Driving Decisions (HEADD) dataset.

Imitation is a key component of human social behavior, and is widely used by both children and adults as a way to navigate uncertain or unfamiliar situations. But in an environment populated by multiple heterogeneous agents pursuing different goals or objectives, indiscriminate imitation is unlikely to be an effective strategy -- the imitator must instead determine who is most useful to copy. There are likely many factors that play into these judgements, depending on context and availability of information. Here we investigate the hypothesis that these decisions involve inferences about other agents' reward functions. We suggest that people preferentially imitate the behavior of others they deem to have similar reward functions to their own. We further argue that these inferences can be made on the basis of very sparse or indirect data, by leveraging an inductive bias toward positing the existence of different \textit{groups} or \textit{types} of people with similar reward functions, allowing learners to select imitation targets without direct evidence of alignment.