We explore the hypothesis that many intuitive physical inferences are based on a mental
physics engine that is analogous in many ways to the machine physics engines used in …

A Bayesian framework helps address, in computational terms, what knowledge children start
with and how they construct and adapt models of the world during childhood. Within this …

Despite the success of neural networks at solving concrete physics problems, their use as a
general-purpose tool for scientific discovery is still in its infancy. Here, we approach this …

Many prediction problems, such as those that arise in the context of robotics, have a
simplifying underlying structure that, if known, could accelerate learning. In this paper, we …

Throughout their lives, humans seem to learn a variety of rules for things like applying
category labels, following procedures, and explaining causal relationships. These rules are …

How do people make causal judgments about physical events? We introduce the
counterfactual simulation model (CSM) which predicts causal judgments in physical settings …

From infancy, humans have expectations about how objects will move and interact. Even
young children expect objects not to move through one another, teleport, or disappear. They …

An open question regarding how people develop their models of the world is how new
candidates are generated for consideration out of infinitely many possibilities. We discuss …

A popular explanation of the human ability for physical reasoning is that it depends on a
sophisticated ability to perform mental simulations. According to this perspective, physical …

Humans can reason about intuitive physics in fully or partially observed environments even
after being exposed to a very limited set of observations. This sample-efficient intuitive …