Marr's levels of analysis—computational, algorithmic, and implementation—have served
cognitive science well over the last 30 years. But the recent increase in the popularity of the …

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

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

A variety of studies have demonstrated that organizing stimuli into categories can affect the
way the stimuli are perceived. We explore the influence of categories on perception through …

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 …

Probabilistic models have recently received much attention as accounts of human cognition.
However, most research in which probabilistic models have been used has been focused on …

We present a proposal—“The Sampling Hypothesis”—suggesting that the variability in
young children's responses may be part of a rational strategy for inductive inference. In …

Humans have internal models of robots (like their physical capabilities), the world (like what
will happen next), and their tasks (like a preferred goal). However, human internal models …

Probabilistic models based on Bayes' rule are an increasingly popular approach to
understanding human cognition. Bayesian models allow immense representational latitude …

Determining the knowledge that guides human judgments is fundamental to understanding
how people reason, make decisions, and form predictions. We use an experimental …