Humans and machines rarely have access to explicit external feedback or supervision, yet manage to learn. Most modern machine learning systems succeed because they benefit from unsupervised data. Humans are also expected to benefit and yet, mysteriously, empirical results are mixed. Does unsupervised learning help humans or not? Here, we argue that the mixed results are not conflicting answers to this question, but reflect that humans self-reinforce their predictions in the absence of supervision, which can help or hurt depending on whether predictions and task align. We use this framework to synthesize empirical results across various domains to clarify when unsupervised learning will help or hurt. This provides new insights into the fundamentals of learning with implications for instruction and lifelong learning.

中文翻译：

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揭开无监督学习的神秘面纱：它如何帮助和伤害


人类和机器很少能够获得明确的外部反馈或监督，但能够设法学习。大多数现代机器学习系统之所以成功，是因为它们受益于无监督数据。人们也期望人类从中受益，但神秘的是，实证结果喜忧参半。无监督学习对人类有帮助吗？在这里，我们认为，混合结果并不是对这个问题的冲突答案，而是反映了人类在没有监督的情况下自我强化了他们的预测，这可能是有益的，也可能是有害的，这取决于预测和任务是否一致。我们利用这个框架来综合各个领域的实证结果，以阐明无监督学习何时会有所帮助或有害。这为学习的基本原理提供了新的见解，对教学和终身学习具有影响。