A central question in human development is how young children gain knowledge so fast. We propose that analogical generalization drives much of this early learning and allows children to generate new abstractions from experience. In this paper, we review evidence for analogical generalization in both children and adults. We discuss how analogical processes interact with the child's changing knowledge base to predict the course of learning, from conservative to domain-general understanding. This line of research leads to challenges to existing assumptions (...) about learning. It shows that it is not enough to consider the distribution of examples given to learners; one must consider the processes learners are applying; contrary to the general assumption, maximizing variability is not always the best route for maximizing generalization and transfer. (shrink)

Fisher, Jin, and Scott push a central assumption of syntactic bootstrapping: that learners have a universal bias to map each noun in a sentence onto a participant role (i.e., argument of the verb). They propose two enrichments: First, that children use both semantic and syntactic information in representing nouns that accompany a verb; second, that children expect continuity across a discourse. They provide evidence for both learning mechanisms among young children, further spelling out the precise mechanisms underlying syntactic bootstrapping.

This article focuses on philosophical views that attempt to explain widespread belief in indeterministic choice by following a strategy that harkens back at least to Spinoza. According to this Spinozan strategy, people draw an inference from the absence of experiences of determined choice to the belief in indeterministic choice. Accounts of this kind are historically liable to overgeneralization. The pair of accounts defended in Shaun Nichols’ recent book, Bound: Essays on Free Will and Responsibility, are the most complete and empirically (...) plausible representatives of this strategy. I argue that both accounts ultimately fail, and overgeneralization worries remain for Spinozan strategists. (shrink)

Zuidema et al. illustrate how empirical AGL studies can benefit from computational models and techniques. Computational models can help clarifying theories, and thus in delineating research questions, but also in facilitating experimental design, stimulus generation, and data analysis. The authors show, with a series of examples, how computational modeling can be integrated with empirical AGL approaches, and how model selection techniques can indicate the most likely model to explain experimental outcomes.