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 language | English (US) |
---|---|
State | Published - 2018 |
Externally published | Yes |
Event | 6th International Conference on Learning Representations, ICLR 2018 - Vancouver, Canada Duration: Apr 30 2018 → May 3 2018 |
Conference
Conference | 6th International Conference on Learning Representations, ICLR 2018 |
---|---|
Country/Territory | Canada |
City | Vancouver |
Period | 4/30/18 → 5/3/18 |
All Science Journal Classification (ASJC) codes
- Education
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics