Probability learning and statistical inference in infancy and early childhood

婴幼儿是如何快速学习这么多知识的？我的研究计划的长期目标是通过开发婴儿期和幼儿期的概率学习和推理机制的综合图片来回答这个问题。 近年来，我的学生、合作者和我在描述年轻学习者如何使用基本比率和抽样信息进行跨领域归纳推理方面取得了实质性进展。我的下一个资助周期的短期目标是在婴儿期和幼儿期的概率学习特征方面取得类似的进展。概率学习需要跟踪事件在空间和时间中的频率或比例。在许多概率学习研究中，人们必须通过许多试验来预测事件将在空间的哪一侧（左或右）发生。如果事件70%的时间发生在一侧（你已经了解了这一点），你应该多久预测一次发生在那一侧？个体成年人通常会做两件事中的一件：要么最大化频繁的一面，总是选择它，要么概率匹配频率，选择每一边的发生比例（70：30）。最大化具有更高的预期准确性（70%对概率匹配的58%），因此数十年的成人认知工作已经研究了为什么以及如何一些成年人显示每种模式。 尽管发展数据有真实的机会来衡量这个长期存在的问题，尽管概率和统计学习在发展的早期特别重要，但幼儿期的概率学习几乎没有被研究过。拟议中的实验将揭示潜在的认知过程，引起概率匹配与最大化的行为。所有的实验都采用了简单的概率学习设计，孩子们在每次实验中预测物体会出现在哪一边，测量反应时间，选择和整体学习。 系列1的目的是通过系统地操纵概率分布（50，70，90，100）并检查匹配与最大化来获得3-6岁儿童概率学习的全貌。系列2的目的是测试儿童在不断变化的环境中更新概率的能力。我会问孩子们是否期望更多或更少的稳定性，在人类产生的与物理决定的环境，揭示是否一些行为模式的结果，从预期更大的转变，在某些领域。最后，系列3的目标是收集第一个关于婴儿期概率学习的广泛数据集，以研究婴儿的这些潜在认知过程。 这些数据和研究结果将引起认知科学家的极大兴趣，因为它们将涉及概率学习的潜在认知机制，以及儿童在参与这些学习过程时对我们跨领域环境稳定性的早期概念。

How do infants and young children learn so much so quickly? The long-term goal of my research program is to answer this question by developing a comprehensive picture of probabilistic learning and inference mechanisms in infancy and early childhood. In recent years, my students, collaborators, and I have made substantial progress in characterizing how young learners use base-rates and sampling information to make inductive inferences across domains. The short-term goal of my next grant cycle is to make similar progress in characterizing probability learning in infancy and early childhood. Probability learning entails tracking frequencies or proportions of events in space and time. In many probability learning studies, one must predict on which side of space (left or right) an event will occur, over many, many trials. If the event occurs on one side 70% of the time (and you have learned this), how often should you predict an occurrence on that side? Individual adults often do 1 of 2 things: either maximize on the frequent side, always choosing it, or probability match the frequencies, choosing each side in the proportion with which it occurs (70:30). Maximizing has greater expected accuracy (70% vs 58% for probability matching) and so decades of work in adult cognition has examined why and how some adults show each pattern. Probability learning in early childhood has scarcely been studied, despite the real opportunity for developmental data to weigh in on this longstanding question and despite the fact that probability and statistical learning are particularly critical early in development. The proposed experiments will shed light on the underlying cognitive processes that give rise to probability-matching versus maximizing behaviour. All experiments use a straightforward probability learning design, in which children predict on each trial which side an object will appear, measuring reaction time, choices, and overall learning. The objective of Series 1 is to obtain a full picture of 3-6-year-old children's probability learning by systematically manipulating probability distributions (50,70,90,100) and examining matching versus maximizing. The objective of Series 2 is to test children's ability to update probabilities in shifting environments. I will ask whether children expect more or less stability in human-generated versus physically determined environments to uncover whether some patterns of behaviour result from an expectation of greater shifting in some domains. Finally, the objective of Series 3 is to collect the first extensive dataset on probability learning in infancy to examine these underlying cognitive processes in infants. Together these data and findings will be of great interest to cognitive scientists, as they will speak to the underlying cognitive mechanisms involved in probability learning, as well as children's early conceptions of the stability of our environments across domains when engaging in these learning processes.