Representation and Computation in Cognitive Models
Abstract
One of the central issues in cognitive science is the nature of human representations. We argue that symbolic representations are essential for capturing human cognitive capabilities. We start by examining some common misconceptions found in discussions of representations and models. Next we examine evidence that symbolic representations are essential for capturing human cognitive capabilities, drawing on the analogy literature. Then we examine fundamental limitations of feature vectors and other distributed representations that, despite their recent successes on various practical problems, suggest that they are insufficient to capture many aspects of human cognition. After that, we describe the implications for cognitive architecture of our view that analogy is central, and we speculate on roles for hybrid approaches. We close with an analogy that might help bridge the gap.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 21, 2017
- Source ID
- 10.1111/tops.12277
Entities
People
- Chen Liang
- Irina Rabkina
- Ken Forbus
Organizations
- Air Force Office of Scientific Research
- National Science Foundation
- Northwestern University
- Office of Naval Research