Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp016w924g13v
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorGriffiths, Thomas L
dc.contributor.authorBattleday, Ruairidh McLennan
dc.contributor.otherComputer Science Department
dc.date.accessioned2023-10-06T20:14:22Z-
dc.date.available2023-10-06T20:14:22Z-
dc.date.created2023-01-01
dc.date.issued2023
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp016w924g13v-
dc.description.abstractThis 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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherPrinceton, NJ : Princeton University
dc.subjectBayesian inference
dc.subjectBehavior
dc.subjectClassification
dc.subjectGeneralization
dc.subjectMachine learning
dc.subjectStatistical modeling
dc.subject.classificationArtificial intelligence
dc.subject.classificationPsychology
dc.subject.classificationStatistics
dc.titleThe Role Of Nonparametric Inference In Computational Models Of Categorization And Analogy
dc.typeAcademic dissertations (Ph.D.)
pu.date.classyear2023
pu.departmentComputer Science
Appears in Collections:Computer Science

Files in This Item:
File Description SizeFormat 
Battleday_princeton_0181D_14689.pdf23.07 MBAdobe PDFView/Download


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.