We propose a new version of the “theory theory” grounded in the computational framework
of probabilistic causal models and Bayesian learning. Probabilistic models allow a …

How do inference rules for causal learning themselves change developmentally? A model of
the development of causal reasoning must address this question, as well as specify the …

Learning and executive functions such as task-switching share common neural substrates,
notably prefrontal cortex and basal ganglia. Understanding how they interact requires …

Children learn causal relationships quickly and make far-reaching causal inferences from
what they observe. Acquiring abstract causal principles that allow generalization across …

Children's causal learning has been characterized as a rational process, in which children
appropriately evaluate evidence from their observations and actions in light of their existing …

The term statistical learning in infancy research originally referred to sensitivity to transitional
probabilities. Subsequent research has demonstrated that statistical learning contributes to …

There are four key reasons for our interest in probability. First, we wondered whether the
teaching and learning of probability took sufficient account of children's prior knowledge of …

Over the past decade, an active line of research within the field of human causal learning
and inference has converged on a general representational framework: causal models …

The article presents a Bayesian model of causal learning that incorporates generic priors--
systematic assumptions about abstract properties of a system of cause-effect relations. The …

In this book, acclaimed economist Herbert Gintis ranges widely across many fields—
including economics, psychology, anthropology, sociology, moral philosophy, and biology …