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Helping is a universal human behavior, and is a core aspect of a functioning society. However, the decision to provide help, and what type of help to provide, is a complex cognitive calculation that weights many costs and benefits simultaneously. In this paper, we explore how various costs influence the moment-tomoment decision to help in a simple video game. Participants were paired with another human participant and were asked to make repeated decisions that could benefit either themselves or their partner. Several preregistered manipulations altered the cost each person paid for actions in the environment, the intrinsic resource capacity of individuals to perform the task, the visibility of the other player’s score, and the affordances within the environment for helping. The results give novel insight into the cost-benefit analyses that people apply when providing help, and highlight the role of reciprocity in influencing helping decisions. 

A detailed model of the outside world is an essential ingredient of human cognition, enabling us to navigate, form goals, exe- cute plans, and avoid danger. Critically, these world models are flexible—they can arbitrarily expand to introduce previously- undetected objects when new information suggests their pres- ence. Although the number of possible undetected objects is theoretically infinite, people rapidly and accurately infer un- seen objects in everyday situations. How? Here we investigate one approach to characterizing this behavior—as nonparamet- ric clustering over low-level cues—and report preliminary re- sults comparing a computational model to human physical in- ferences from real-world video. 

When people solve problems, they may try multiple invalid solutions before finally having an insight about the correct solution. Insight problem-solving is an example of the flexibility of the human mind which remains unmatched by machines. In this paper, we present a novel experimental paradigm for studying insight problem-solving behavior in a physical reasoning domain. Using this paradigm, we seek to quantify precisely what it means to have an insight during physical problem-solving and identify behavioral traces that predict subjective insight ratings collected from human participants. The project provides the first steps towards a computationally informed theory of insight problems solving. 

People often rely on knowledgeable teachers to help them learn. Sometimes, this teaching is direct: teachers provide in- structions, examples, demonstrations, or feedback. But other times, teaching is more subtle: teachers construct the physi- cal environment in which a learner explores. In the present research, we investigate this more subtle form of teaching in an artificial grid-based learning environment. How do people construct the physical environment to teach, and how does the (pedagogical) design of the physical environment affect peo- ple’s learning? Study 1 shows that people pursue multiple ap- proaches to pedagogical environment design. Study 2 shows that learners make systematic, often accurate inferences from pedagogically designed environments, even in the absence of exploration. Together, these studies add to our understanding of the myriad ways in which experts communicate their knowl- edge to novices—a ca