Successful speech perception requires that listeners map the acoustic signal to linguistic
categories. These mappings are not only probabilistic, but change depending on the …

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 …

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 …

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 …

Recent studies of probabilistic reasoning have postulated general-purpose inference
algorithms that can be used to answer arbitrary queries. These algorithms are memoryless …

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 …

The human ability to reason about the causes behind other people'behavior is critical for
navigating the social world. Recent empirical research with both children and adults …

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 …

In this article, we present a perspective on the role of the hippocampal system in
generalization, instantiated in a computational model called REMERGE (recurrency and …

People's decisions and judgments are disproportionately swayed by improbable but
extreme eventualities, such as terrorism, that come to mind easily. This article explores …