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We often turn to others' advice to learn and make better decisions. However, social learning does not guarantee accurate learning: Other people's knowledge can be as incomplete and limited as our own, and their advice is not always perfectly helpful. The current study examines how human learners put two "imperfect" heads together to make utility-maximizing decisions. Participants played a card game where they chose to "stay" with a card of known value or "switch" to take an unknown card, given an advisor's advice to stay or switch. Participants used the advice strategically based on which cards the advisor could see (Experiment 1), how helpful the advisor was (Experiment 2), and what strategy the advisor used to select advice (Experiment 3); overall, participants benefited even from imperfect advice based on incomplete information. Participants' responses were consistent with a Bayesian model that jointly infers how the advisor selects advice and the value of the advisor's card, compared to an alternative model that decides whether to follow advice based on the advisor's accuracy. By reasoning about others' minds, human learners can make the best of even noisy, impoverished social information.
CC-By Attribution 4.0 International