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Representation and Computation in Cognitive Models.

Kenneth D Forbus1, Chen Liang1, Irina Rabkina1

  • 1Department of Computer Science, Northwestern University.

Topics in Cognitive Science
|June 22, 2017
PubMed
Summary
This summary is machine-generated.

Human cognition relies on symbolic representations, not just feature vectors. Symbolic models are essential for understanding cognitive capabilities, particularly in analogy-making, challenging limitations of distributed representations.

Keywords:
AnalogyComputational modelingLearningMachine learningRelational representationsRepresentationSymbolic modeling

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • The nature of human representations is a core debate in cognitive science.
  • Misconceptions about representations and models are common.
  • Distributed representations like feature vectors have shown practical success.

Purpose of the Study:

  • To argue for the essential role of symbolic representations in human cognition.
  • To examine the limitations of distributed representations for capturing cognitive capabilities.
  • To explore the implications of analogy-centrality for cognitive architecture.

Main Methods:

  • Analysis of the analogy literature to support the necessity of symbolic representations.
  • Critique of feature vectors and other distributed representations.
  • Conceptual exploration of cognitive architecture and hybrid approaches.

Main Results:

  • Symbolic representations are crucial for modeling human cognitive capabilities.
  • Distributed representations have fundamental limitations for certain cognitive tasks.
  • Analogy plays a central role in cognition, suggesting its importance in cognitive architectures.

Conclusions:

  • Cognitive science must embrace symbolic representations to fully understand human cognition.
  • Hybrid approaches may offer a path forward by integrating symbolic and distributed methods.
  • Analogy serves as a key concept for bridging representational gaps in cognitive models.