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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. 

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 

From navigation in unfamiliar environments to career plan- ning, people typically first sample information before com- mitting to a plan. However, most studies find that people adopt myopic strategies when sampling information. Here we challenge those findings by investigating whether contingency planning is a driver of information sampling. To this aim, we developed a novel navigation task that is a shortest path find- ing problem under uncertainty of bridge closures. Participants (n = 109) were allowed to sample information on bridge sta- tuses prior to committing to a path. We developed a computa- tional model in which the agent samples information based on the cost of switching to a contingency plan. We find that this model fits human behavior well and is qualitatively similar to the approximated optimal solution. Together, this suggests that humans use contingency planning as a driver of information sampling.