We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the "what", the "how", and the "why" of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for…

Probabilistic models of expected information gain require integrating prior knowledge about causal hypotheses with knowledge about possible actions that might generate data relevant to those hypotheses. Here we looked at whether preschoolers (mean: 54 months) recognize "action possibilities" (affordances) in the environment that allow them to…

Binocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should…

Alternative measures of "confirmation" or "evidential support" have been proposed to express the impact of ascertaining one event on the credibility of another. We report an experiment that compares the adequacy of several such measures as descriptions of confirmation judgment in a probabilistic context.

People's reactions to coincidences are often cited as an illustration of the irrationality of human reasoning about chance. We argue that coincidences may be better understood in terms of rational statistical inference, based on their functional role in processes of causal discovery and theory revision. We present a formal definition of…

Syntactic knowledge is widely held to be partially innate, rather than learned. In a classic example, it is sometimes argued that children know the proper use of anaphoric "one," although that knowledge could not have been learned from experience. Lidz et al. [Lidz, J., Waxman, S., & Freedman, J. (2003). What infants know about syntax but couldn't…