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An integrated model of semantics and control.

Tyler Giallanza1, Declan Campbell2, Jonathan D Cohen1

  • 1Department of Psychology, Princeton University.

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|July 25, 2024
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Summary
This summary is machine-generated.

This study introduces a unified model for semantic cognition and cognitive control, explaining how knowledge is learned and flexibly used. The model demonstrates how environmental statistics shape semantic knowledge and task-appropriate control through end-to-end learning.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Semantic cognition and cognitive control research focus on learning and flexible knowledge use.
  • Existing models struggle to explain how control operates over covarying features and how control representations emerge.

Purpose of the Study:

  • To present a unified model of semantics and control.
  • To explain how semantic knowledge and flexible access arise from environmental statistics.
  • To address unresolved issues in semantic cognition and cognitive control literature.

Main Methods:

  • Developed a unified model integrating semantic cognition and cognitive control.
  • Utilized end-to-end learning based on environmental statistics.
  • Conducted behavioral experiments and computational simulations.

Main Results:

  • The model provides a coherent view of semantic knowledge acquisition and flexible deployment.
  • Demonstrated how control operates over covarying features.
  • Showed the emergence of structured control representations through learning.

Conclusions:

  • The unified model offers insights into fundamental questions in cognitive science.
  • The approach has implications for machine learning and artificial intelligence.
  • End-to-end learning from environmental statistics is key to flexible knowledge use.