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Groups coordinate more effectively when individuals are able to learn fromothers' successes. But acquiring such knowledge is not always easy, especiallyin real-world environments where success is hidden from public view. We suggestthat social inference capacities may help bridge this gap, allowing individualsto update their beliefs about others' underlying knowledge and success fromobservable trajectories of behavior. We compared our social inference modelagainst simpler heuristics in three studies of human behavior in a collectivesensing task. In Experiment 1, we found that average performance improves as afunction of group size at a rate greater than predicted by non-inferentialmodels. Experiment 2 introduced artificial agents to evaluate how individualsselectively rely on social information. Experiment 3 generalized these findingsto a more complex reward landscape. Taken together, our findings provideinsight into the relationship between individual social cognition and theflexibility of collective behavior.

Flexible social inference facilitates targeted social learning when rewards are not observable