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Traditionally, learning has been modeled as passively obtaining information or actively exploring the environment. Recent research has introduced models of learning from teachers that involve reasoning about why they have selected particular evidence. We introduce a computational framework that takes a critical step toward unifying active learning and teaching by recognizing that meta‐reasoning underlying reasoning about others can be applied to reasoning about oneself. The resulting Self‐Teaching model captures much of the behavior of information‐gain‐based active learning with elements of hypothesis‐testing‐based active learning and can thus be considered as a formalization of active learning within the broader teaching framework. We present simulation experiments that characterize the behavior of the model within three simple and well‐investigated learning problems. We conclude by discussing such theory‐of‐mind‐based learning in the context of core cognition and cognitive development.

 

How can education optimize transmission of knowledge while also fostering further learning? Focusing on children at the cusp of formal schooling (N= 180, age = 4.0–6.0 y), we investigate learning after direct instruction by a knowledgeable teacher, after questioning by a knowledgeable teacher, and after questioning by a naïve informant. Consistent with previous findings, instruction by a knowledgeable teacher allows effective information transmission but at the cost of exploration and further learning. Critically, we find a dual benefit for questioning by a knowledgeable teacher: Suchpedagogical questioningboth effectively transmits knowledge and fosters exploration and further learning, regardless of whether the question was directed to the child or directed to a third party and overheard by the child. These effects are not observed when the same question is asked by a naïve informant. We conclude that a teacher's choice of pedagogical method may differentially influence learning through their choices of how, and how not, to present evidence, with implications for transmission of knowledge and self‐directed discovery.

A video abstract of this article can be viewed at:https://www.youtube.com/watch?v=FJXH2b65wL8

 

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.