Representation and Computation in Cognitive Models

Kenneth D Forbus*, Chen Liang, Irina Rabkina

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)694-718
Number of pages25
JournalTopics in Cognitive Science
Volume9
Issue number3
DOIs
StatePublished - Jul 2017

Keywords

  • Analogy
  • Computational modeling
  • Learning
  • Machine learning
  • Relational representations
  • Representation
  • Symbolic modeling

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Linguistics and Language
  • Human-Computer Interaction
  • Cognitive Neuroscience
  • Artificial Intelligence

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