We review the idea that Theory of Mind—our ability to reason about other people's mental
states—can be formalized as inverse reinforcement learning. Under this framework …

Bayesian explanations have swept through cognitive science over the past two decades,
from intuitive physics and causal learning, to perception, motor control and language. Yet …

Mental representations remain the central posits of psychology after many decades of
scrutiny. However, there is no consensus about the representational format (s) of biological …

People learning new concepts can often generalize successfully from just a single example,
yet machine learning algorithms typically require tens or hundreds of examples to perform …

Inductive reasoning is a core problem-solving capacity: humans can identify underlying
principles from a few examples, which can then be robustly generalized to novel scenarios …

Abstract Machines have achieved a broad and growing set of linguistic competencies,
thanks to recent progress in Natural Language Processing (NLP). Psychologists have …

To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such
an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive …

Much of learning and reasoning occurs in pedagogical situations—situations in which a
person who knows a concept chooses examples for the purpose of helping a learner …

Modern generative models exhibit unprecedented capabilities to generate extremely
realistic data. However, given the inherent compositionality of real world, reliable use of …

The prominence of Bayesian modeling of cognition has increased recently largely because
of mathematical advances in specifying and deriving predictions from complex probabilistic …