We examine how people judge the probabilities of real-world
events, such as natural disasters in different countries. We
find that the associations between the words and phrases that
constitute these events, as assessed by vector space semantic
models, strongly correlate with the probabilities assigned to
these events by participants. Thus, for example, the semantic
proximity of “earthquake” and “Japan” accurately predicts
judgments regarding the probability of an earthquake in
Japan. Our results suggest that the mechanisms and
representations at play in language are also active in high-
level domains, such as judgment and decision making, and
that existing insights regarding these representations can be
used to make precise, quantitative, a priori predictions
regarding the probability estimates of individuals
