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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Why concepts are (probably) vectors.

Steven T Piantadosi1, Dyana C Y Muller2, Joshua S Rule3

  • 1Department of Psychology, University of California, Berkeley, CA, USA; Department of Neuroscience, University of California, Berkeley, CA, USA.

Trends in Cognitive Sciences
|August 7, 2024
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Summary
This summary is machine-generated.

Vector representations offer a unified approach to understanding human concepts, accommodating diverse cognitive functions. Advances in large language models and vector symbolic architectures demonstrate their potential for neural encoding.

Keywords:
church encodingconceptsconceptual rolevectorvector symbolic architecture

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Debate on the nature of human concept representation.
  • Requirement for representations to support diverse cognitive computations (similarity, categorization, relations).
  • Need for representations to enable theory development and procedural knowledge.

Purpose of the Study:

  • To argue for vector-based representations as a unified account of human concepts.
  • To highlight the compatibility of vector representations with neural architectures.
  • To discuss recent advances supporting this view.

Main Methods:

  • Conceptual analysis of representation requirements in cognitive science.
  • Review of recent advancements in large language models (LLMs).
  • Examination of vector symbolic architectures (VSAs).

Main Results:

  • Vector representations can account for a wide range of cognitive properties (similarity, features, categories, definitions, relations).
  • Vector representations support complex cognitive processes like theory development and ad hoc categorization.
  • Recent LLMs and VSAs demonstrate practical implementation of vector-based symbolic computation.

Conclusions:

  • Vector-based representations provide a compelling and neurally plausible model for human concepts.
  • Emerging AI technologies validate the power of vectors for symbolic processing and cognitive modeling.
  • This approach unifies diverse cognitive functions under a single representational framework.