Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving test scoring and better informing public policy. Yet larger…

Acquiring the correct meanings of words expressing quantities ("seven, most") and qualities ("red, spotty") present a challenge to learners. Understanding how children succeed at this requires understanding, not only of what kinds of data are available to them, but also the biases and expectations they bring to the learning…

Learning a phonetic category (or any linguistic category) requires integrating different sources of information. A crucial unsolved problem for phonetic learning is how this integration occurs: how can we update our previous knowledge about a phonetic category as we hear new exemplars of the category? One model of learning is Bayesian Inference,…

The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with…

A Bayesian approach to sensitivity of inferences to possible outliers involves recalculating marginal posterior distributions of parameters of interest under assumptions of heavy tails. This strategy is implemented in the hierarchical model setting through Gibbs sampling, a Monte Carlo technique, and illustrated through a reanalysis of data on…