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  1. null (Ed.)
    Speakers communicate to influence their partner's beliefs and shape their actions. Belief- and action-based objectives have been explored independently in recent computational models, but it has been challenging to explicitly compare or integrate them. Indeed, we find that they are conflated in standard referential communication tasks. To distinguish these accounts, we introduce a new paradigm called signaling bandits, generalizing classic Lewis signaling games to a multi-armed bandit setting where all targets in the context have some relative value. We develop three speaker models: a belief-oriented speaker with a purely informative objective; an action-oriented speaker with an instrumental objective; and a combined speaker which integrates the two by inducing listener beliefs that generally lead to desirable actions. We then present a series of simulations demonstrating that grounding production choices in future listener actions results in relevance effects and flexible uses of nonliteral language. More broadly, our findings suggest that language games based on richer decision problems are a promising avenue for insight into rational communication. 
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  2. When teaching, people often intentionally intervene on a learner while it is acting. For instance, a dog owner might move the dog so it eats out of the right bowl, or a coach might intervene while a tennis player is practicing to teach a skill. How do people teach by intervention? And how do these strategies interact with learning mechanisms? Here, we examine one global and two local strategies: working backwards from the end-goal of a task (backwards chaining), placing a learner in a previous state when an incorrect action was taken (undoing), or placing a learner in the state they would be in if they had taken the correct action (correcting). Depending on how the learner interprets an intervention, different teaching strategies result in better learning. We also examine how people teach by intervention in an interactive experiment and find a bias for using local strategies like undoing. 
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  3. People often learn from others’ demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration has been less well studied computationally. Here, we develop a Bayesian model for teaching by demonstration. Stark differences arise when demonstrators are intentionally teaching (i.e. showing) a task versus simply performing (i.e. doing) a task. In two experiments, we show that human participants modify their teaching behavior consistent with the predictions of our model. Further, we show that even standard IRL algorithms benefit when learning from showing versus doing. 
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