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 …

Enormous advances in the domain of statistical machine learning, the availability of large
amounts of training data, and increasing computing power have made Artificial Intelligence …

Developmental psychologists coined the term" theory of mind" to describe how we
understand our shifting mental states in daily life. Over the past twenty years researchers …

An exploration of how computer algorithms can be applied to our everyday lives to solve
common decision-making problems and illuminate the workings of the human mind. What …

Inducing causal relationships from observations is a classic problem in scientific inference,
statistics, and machine learning. It is also a central part of human learning, and a task that …

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 past few decades have seen an explosion of research on causal reasoning in
philosophy, computer science, and statistics, as well as descriptive work in psychology. In …

Compact representations of the environment allow humans to behave efficiently in a
complex world. Reinforcement learning models capture many behavioral and neural effects …

People can behave in a way that is consistent with Bayesian models of cognition, despite
the fact that performing exact Bayesian inference is computationally challenging. What …