Probability learning and statistical inference in infancy and early childhood

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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.