Helping others to achieve mutual goals involves a great deal of social cognition. For example, teaching a skill to another person requires understanding the learner’s goals, current knowledge, and preferences. Despite the ubiquity of humans helping each other, the majority of cognitive science research has focused on individual learning and decision making. Therefore, not much is known about the computational cognitive mechanisms that humans use to reduce uncertainty about the knowledge, goals, and preferences of others in order to provide relevant information and to help them more effectively. For example, how do we decide when we ask a question to better understand the goals or understanding of another person? How do we adapt our assistance to another on the basis of our incomplete understanding of their mind?
In this proposal, the investigators develop several computational cognitive models and compare these with human behavior in newly developed cognitive assays of helping and teaching. The project leverages the framework of Partially Observable Monte Carlo Decision Problems, an Artificial Intelligence framework, to provide a benchmark of efficient behavior by deriving the optimal policy (i.e., the most efficient helper strategy) in these paradigms of collaborative problem-solving. Insights into the algorithmic basis of how humans solve this complex problem may inform research on human-machine teams that will define the workplace of the future.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
