End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior

Pulkit Singh, Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

Traditional models of category learning in psychology focus on representation at the category level as opposed to the stimulus level, even though the two are likely to interact. The stimulus representations employed in such models are either hand-designed by the experimenter, inferred circuitously from human judgments, or borrowed from pretrained deep neural networks that are themselves competing models of category learning. In this work, we extend classic prototype and exemplar models to learn both stimulus and category representations jointly from raw input. This new class of models can be parameterized by deep neural networks (DNN) and trained end-to-end. Following their namesakes, we refer to them as Deep Prototype Models, Deep Exemplar Models, and Deep Gaussian Mixture Models. Compared to typical DNNs, we find that their cognitively inspired counterparts both provide better intrinsic fit to human behavior and improve ground-truth classification.

Original languageEnglish (US)
Pages634-640
Number of pages7
StatePublished - 2020
Event42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online
Duration: Jul 29 2020Aug 1 2020

Conference

Conference42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020
CityVirtual, Online
Period7/29/208/1/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • category learning
  • deep neural networks
  • exemplar models
  • Gaussian mixture models
  • prototype models

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