When teaching, people often intentionally intervene on alearner while it is acting. For instance, a dog owner mightmove the dog so it eats out of the right bowl, or a coach mightintervene while a tennis player is practicing to teach a skill.How do people teach by intervention? And how do thesestrategies interact with learning mechanisms? Here, we ex-amine one global and two local strategies: working backwardsfrom the end-goal of a task (backwards chaining), placing alearner in a previous state when an incorrect action was taken(undoing), or placing a learner in the state they would be in ifthey had taken the correct action (correcting). Depending onhow the learner interprets an intervention, different teachingstrategies result in better learning. We also examine how peo-ple teach by intervention in an interactive experiment and finda bias for using local strategies like undoing.

When observing others’ behavior, people use Theory of Mind to infer unobservable beliefs, desires, and intentions. And when showing what activity one is doing, people will modify their behavior in order to facilitate more accurate interpretation and learning by an observer. Here, we present a novel model of how demonstrators act and observers interpret demonstrations corresponding to different levels of recursive social reasoning (i.e. a cognitive hierarchy) grounded in Theory of Mind. Our model can explain how demonstrators show others how to per- form a task and makes predictions about how sophisticated ob- servers can reason about communicative intentions. Addition- ally, we report an experiment that tests (1) how well an ob- server can learn from demonstrations that were produced with the intent to communicate, and (2) how an observer’s interpre- tation of demonstrations influences their judgments.

Successfully navigating the social world requires reasoningabout both high-level strategic goals, such as whether to co-operate or compete, as well as the low-level actions neededto achieve those goals. While previous work in experimentalgame theory has examined the former and work on multi-agentsystems has examined the later, there has been little work in-vestigating behavior in environments that require simultaneousplanning and inference across both levels. We develop a hierar-chical model of social agency that infers the intentions of otheragents, strategically decides whether to cooperate or competewith them, and then executes either a cooperative or competi-tive planning program. Learning occurs across both high-levelstrategic decisions and low-level actions leading to the emer-gence of social norms. We test predictions of this model inmulti-agent behavioral experiments using rich video-game likeenvironments. By grounding strategic behavior in a formalmodel of planning, we develop abstract notions of both co-operation and competition and shed light on the computationalnature of joint intentionality.

Teaching with evaluative feedback involves expectations about how a learner will interpret rewards and punishments. We formalize two hypotheses of how a teacher implicitly expects a learner to interpret feedback – a reward-maximizing model based on standard reinforcement learning and an action-feedback model based on research on communicative intent – and describe a virtual animal-training task that distinguishes the two. The results of two experiments in which people gave learners feedback for isolated actions (Exp. 1) or while learning over time (Exp. 2) support the action-feedback model over the reward-maximizing model