Capturing human category representations by sampling in deep feature spaces

Joshua Peterson, Jordan Suchow, Krisha Aghi, Alexander Ku, Thomas Griffiths

Research output: Contribution to conferencePaper

Abstract

Understanding how people represent categories is a core problem in cognitive science, with the flexibility of human learning remaining a gold standard to which modern artificial intelligence and machine learning aspire. Decades of psychological research have yielded a variety of formal theories of categories, yet validating these theories with naturalistic stimuli remains a challenge. The problem is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires having a workable representation of these stimuli. Deep neural networks have recently been successful in a range of computer vision tasks and provide a way to represent the features of images. In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners. We provide qualitative and quantitative results as a proof of concept for the feasibility of the method. Samples drawn from human distributions rival the quality of current state-of-the-art generative models and outperform alternative methods for estimating the structure of human categories.

Original languageEnglish (US)
StatePublished - Jan 1 2018
Externally publishedYes
Event6th International Conference on Learning Representations, ICLR 2018 - Vancouver, Canada
Duration: Apr 30 2018May 3 2018

Conference

Conference6th International Conference on Learning Representations, ICLR 2018
CountryCanada
CityVancouver
Period4/30/185/3/18

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

Fingerprint Dive into the research topics of 'Capturing human category representations by sampling in deep feature spaces'. Together they form a unique fingerprint.

  • Cite this

    Peterson, J., Suchow, J., Aghi, K., Ku, A., & Griffiths, T. (2018). Capturing human category representations by sampling in deep feature spaces. Paper presented at 6th International Conference on Learning Representations, ICLR 2018, Vancouver, Canada.