After growing up together, and mostly growing apart in the second half of the 20th century,
the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging …

Cognitive science aims to reverse-engineer the mind, and many of the engineering
challenges the mind faces involve induction. The probabilistic approach to modeling …

Recent progress in artificial intelligence has renewed interest in building systems that learn
and think like people. Many advances have come from using deep neural networks trained …

Human perception has recently been characterized as statistical inference based on noisy
and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty …

In many learning or inference tasks human behavior approximates that of a Bayesian ideal
observer, suggesting that, at some level, cognition can be described as Bayesian inference …

A key component of research on human sentence processing is to characterize the
processing difficulty associated with the comprehension of words in context. Models that …

Neural responses in the visual cortex are variable, and there is now an abundance of data
characterizing how the magnitude and structure of this variability depends on the stimulus …

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 …

Rational models of cognition typically consider the abstract computational problems posed
by the environment, assuming that people are capable of optimally solving those problems …

Probabilistic models of cognition characterize the abstract computational problems
underlying inductive inferences and identify their ideal solutions. This approach differs from …