  Whereas cognitive models of learning often assume direct experience with both
the features of an event and with a true label or outcome, much of everyday
learning arises from hearing the opinions of others, without direct access to
either the experience or the ground truth outcome. We consider how people can
learn which opinions to trust in such scenarios by extending the hedge
algorithm: a classic solution for learning from diverse information sources. We
first introduce a semi-supervised variant we call the delusional hedge capable
of learning from both supervised and unsupervised experiences. In two
experiments, we examine the alignment between human judgments and predictions
from the standard hedge, the delusional hedge, and a heuristic baseline model.
Results indicate that humans effectively incorporate both labeled and unlabeled
information in a manner consistent with the delusional hedge algorithm --
suggesting that human learners not only gauge the accuracy of information
sources but also their consistency with other reliable sources. The findings
advance our understanding of human learning from diverse opinions, with
implications for the development of algorithms that better capture how people
learn to weigh conflicting information sources.
