Abstract Bowers and Davis (2012) criticize Bayesian modelers for telling “just so” stories
about cognition and neuroscience. Their criticisms are weakened by not giving an accurate …

Research on the development of causal reasoning has broadly focused on accomplishing
two goals: understanding the origins of causal reasoning, and examining how causal …

Abstract Reasoning under uncertainty is the bread and butter of everyday life. Many areas of
psychology, from cognitive, developmental, social, to clinical, are interested in how …

How do people make rich inferences from such sparse data? Recent research has explored
this inferential ability by investigating probabilistic reasoning in infancy. For example, 8-and …

We present a proposal—“The Sampling Hypothesis”—suggesting that the variability in
young children's responses may be part of a rational strategy for inductive inference. In …

Bayesian inference provides a unifying framework for addressing problems in machine
learning, artificial intelligence, and robotics, as well as the problems facing the human mind …

How do children learn the causal structure of the environment? We first summarize a set of
theories from the adult literature on causal learning, including associative models …

Young children have a nascent capacity to understand how to control variables to uncover
causal structure. However, they do not always explore their environment in a way that …

Many phenomena in animal learning can be explained by a context-learning process
whereby an animal learns about different patterns of relationship between environmental …

Informative computational modeling can be an invaluable tool for understanding syntactic
acquisition. In this chapter, I describe how modeling can be used to both formalize specific …