We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending "bottom up" to...

We propose a framework where perceptions of uncertainty are driven by the interaction between cognitive constraints and the way that people learn about it--whether information is presented sequentially or simultaneously. People can learn about uncertainty by observing the distribution of...