Humans frequently make inferences about uncertain future events with limited data. A growing body of work suggests that infants and other primates make surprisingly sophisticated inferences under uncertainty. First, we ask what underlying cognitive mechanisms allow young learners to make such sophisticated inferences under uncertainty. We outline three possibilities, the logic, probabilistic, and heuristics views, and assess the empirical evidence for each. We argue that the weight of the empirical work favors the probabilistic view, in which early reasoning under uncertainty is grounded in inferences about the relationship between samples and populations as opposed to being grounded in simple heuristics. Second, we discuss the apparent contradiction between this early-emerging sensitivity to probabilities with the decades of literature suggesting that adults show limited use of base-rate and sampling principles in their inductive inferences. Third, we ask how these early inductive abilities can be harnessed for improving later mathematics education and inductive inference. We make several suggestions for future empirical work that should go a long way in addressing the many remaining open questions in this growing research area.

中文翻译：

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婴儿统计学家：不确定性下的推理起源。

人们经常使用有限的数据来推断不确定的未来事件。越来越多的研究表明，在不确定性条件下，婴儿和其他灵长类动物做出了令人惊讶的复杂推断。首先，我们问什么潜在的认知机制可以使年轻的学习者在不确定的情况下做出如此复杂的推断。我们概述了三种可能性，即逻辑，概率和启发式视图，并评估每种可能性的经验证据。我们认为，经验工作的重要性偏重于概率论观点，在这种观点中，不确定性下的早期推理是基于对样本与总体之间关系的推断，而不是基于简单的启发式方法。第二，我们讨论了这种对概率的早期敏感性与数十年的文献之间的明显矛盾，这表明成年人在归纳推理中显示出基本速率和采样原理的使用有限。第三，我们询问如何利用这些早期归纳能力来改善后来的数学教育和归纳推理。我们为未来的经验工作提出了一些建议，这些建议对于解决这个不断发展的研究领域中许多尚待解决的悬而未决的问题大有帮助。