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This study reconciles connectionist and symbolic models in cognitive science by introducing the relational bottleneck. This approach uses neural networks to learn abstract concepts efficiently from limited experience, mimicking human learning.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Explaining abstract concept acquisition from limited experience is a core cognitive science challenge.
  • Traditional models present a dichotomy between connectionist and symbolic approaches.
  • Existing frameworks struggle to account for data-efficient learning of abstract concepts.

Purpose of the Study:

  • To propose a novel reconciliation of connectionist and symbolic cognitive models.
  • To introduce and explore the concept of the 'relational bottleneck' as an inductive bias.
  • To demonstrate how this bias facilitates data-efficient acquisition of abstract concepts.

Main Methods:

  • Reviewing a family of neural network models that incorporate the relational bottleneck.
  • Constraining neural network architectures to focus on relationships between inputs, not just attributes.
  • Evaluating models for their ability to induce abstractions from limited data.

Main Results:

  • The relational bottleneck enables neural networks to learn abstract concepts more efficiently.
  • Architectural constraints focusing on relations improve data efficiency in concept learning.
  • This approach offers a unified perspective on connectionist and symbolic learning mechanisms.

Conclusions:

  • The relational bottleneck provides a promising framework for understanding abstract concept acquisition.
  • This approach offers a potential bridge between connectionist and symbolic theories of cognition.
  • Models employing the relational bottleneck may serve as valuable candidates for explaining human learning and brain function.