Ruairidh Battleday will present his FPO "The Role Of Nonparametric Inference In Computational Models Of Categorization And Analogy" on Thursday, June 29, 2023 at 1:00 PM in COS 401 and Zoom. Location: Zoom link: https://princeton.zoom.us/j/91969001576 The members of Ruairidh's committee are as follows: Examiners: Tom Griffiths (Adviser), Ryan Adams, Barbara Engelhardt Readers: Olga Russakovsky, Tania Lombrozo A copy of his thesis is available upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: This dissertation presents a computational investigation into the nature of human learning and inference. Its central thesis is that humans make good predictions in novel environments because they possess flexible abilities for inductive inference that can be used to generalize and update relevant knowledge abstracted from the past. This thesis is first explored in the context of natural image categorization. Here, participants' judgments are best captured by models that use probabilistic strategies to relate novel stimuli to existing category members, and techniques from deep learning to represent these stimuli in high-dimensional mathematical spaces. The second context is generalization, where the underlying structure of different training games is shown to affect participants' predictive generalizations on a final test game. These behaviors are accounted for by a nonparametric Bayesian model that aggregates mathematical abstractions of particular training environments, and then weights their predictions about unobserved interactions by their analogical relevance. Common to both lines of inquiry is the idea of using probabilistic models to account for flexible inferential behaviors, and tools from statistics and machine learning to abstract relevant mathematical representations from past experiences or stimuli.