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.

翻译：尽管认知学习模型通常假设个体能够直接接触事件特征及其真实标签或结果，但日常学习大多源于听取他人观点，而无法直接获取经验或真实结果。本文通过扩展经典的对冲算法——一种从多元信息来源中学习的经典解决方案，探讨了人们在此类场景中如何学习信任哪些观点。我们首先提出一种半监督变体，称为“妄想对冲”，能够从有监督和无监督经验中学习。通过两项实验，我们检验了人类判断与标准对冲算法、妄想对冲算法以及启发式基线模型预测之间的一致性。结果表明，人类能够有效整合有标签和无标签信息，其方式与妄想对冲算法一致——这表明人类学习者不仅评估信息源的准确性，还评估其与其他可靠信息源的一致性。这些发现深化了我们对人类从多元观点中学习的理解，并对开发更能捕捉人们如何权衡冲突信息源的算法具有启示意义。