The questions I’m most interested in have to do with information structure and learning. For example, how do we recognize that someone else must have more information than we do, without having access to everything they know? Do we expect some sorts of knowledge to be accessible only to multiple people working together? Why do some hard questions pique our curiosity and others turn us off? In what sorts of cases are we willing to “explain away” data that doesn’t fit our generalizations?
My research focuses on how children and adults come to understand mechanism and causality as well as our subjective evaluations of what characterizes a good and useful explanation. Specifically, current studies investigate how cues such as jargon and pedagogical insight impact children’s and adults’ judgement of the usefulness of explanations. Other studies investigate how exposure to mechanistic explanations impacts our conceptions of the world. Specially, I am interested in how learning mechanism might make people more confident - and perhaps overconfident - in their willingness to challenge claims.
Xiuyuan Flora Zhang
Flora is fascinated by how humans learn about the meanings of objects, relationships, actions, and concepts in their daily life in a seemingly effortless manner. She draws from computational modeling techniques to explore how humans form beliefs about the social and physical environment around them, update their beliefs, and make predictions in an information-and-noise-rich environment. In one of her ongoing projects, she is exploring how children and adults understand and evaluate diversity in information selection. Two questions of particular interest are: how do we leverage the competing pressures between wanting to maximize efficiency and wanting to maximize information gain? How do we resolve the tension between seeking diverse information and staying on topic?
Her other projects fall into domains of language learning, information processing, and learning social and conventional norms.
Flora can be reached at email@example.com