Mind reading and the mental maps of expertise: Using machine learning to uncover category structure in experts and novices

As cognitive scientists, we aim to understand how the human mind represents the world by integrating behavioral, physiological, and neural measures such as reaction times, eye movements, and fMRI. In this talk, I will introduce a machine-learning approach called PsiZ (Roads & Mozer, 2019) that uses similarity judgments to infer an observer’s category structure as a psychological embedding. We apply this technique to study real-world expertise across domains such as paleontology, geology, and own- and cross-race face recognition. Our work examines expertise at both the group and individual levels, as well as how category representations change as observers develop from novices to experts through instruction and training.

Alternate locations:

Online: https://utoronto.zoom.us/j/81040630906