How can a machine learn from experience? Probabilistic modelling provides a framework
for understanding what learning is, and has therefore emerged as one of the principal …

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 …

Human perceptual decisions are often described as optimal. Critics of this view have argued
that claims of optimality are overly flexible and lack explanatory power. Meanwhile …

Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain
intuition about the physical behavior of the world. In this paper, we explore the ability of deep …

A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well‐suited to predict
and explain mental processes that best exemplify our ability to be rational. However …

For over 200 years, philosophers and mathematicians have be en using probability theory to
describe human cognition. While the theory of prob abilities was first developed as a means …

Large language models can be construed as 'cognitive models', scientific artefacts that help
us to understand the human mind. If made openly accessible, they may provide a valuable …

Recent debates in the psychological literature have raised questions about the assumptions
that underpin Bayesian models of cognition and what inferences they license about human …

The foundation for modern understanding of how we make perceptual decisions about what
we see or where to look comes from considering the optimal way to perform these …

The ability to act on the world with the goal of gaining information is core to human
adaptability and intelligence. Perhaps the most successful and influential account of such …