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Related Experiment Video

Updated: Jun 1, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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Representation recovers information.

Chris Thornton1

  • 1COGS/Informatics, University of Sussex.

Cognitive Science
|May 19, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a mathematical framework to measure the value of representation in sensory processing. It reveals that specific representational architectures are essential for sensory efficiency and information recovery.

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Area of Science:

  • Cognitive Science
  • Information Theory
  • Computational Neuroscience

Background:

  • Debate on the role of representation in cognition, with some questioning its significance.
  • Lack of formal demonstration for the value of representational approaches in cognitive science.
  • Emergence of embedded/embodied approaches exploring new representational architectures.

Purpose of the Study:

  • To develop a formal framework for mathematically measuring the value of representation.
  • To investigate the necessity of specific representational architectures for sensory efficiency.
  • To reframe representation as a medium for efficient, interpretive processing.

Main Methods:

  • Utilizing Bayesian networks to model the distal dimensions of sensory processes.
  • Developing a mathematical framework to quantify the value of representational strategies.
  • Analyzing theoretical results linking representational architecture to sensory efficiency.

Main Results:

  • A formal method for measuring the value of representation in a sensory context has been established.
  • A specific type of representational architecture is shown to be necessary for achieving sensory efficiency.
  • This architecture aligns with embedded representation concepts in embodied cognition.

Conclusions:

  • Representation is crucial for efficient sensory processing and information recovery.
  • The proposed framework provides a formal basis for understanding representation beyond traditional symbolic encoding.
  • Representation serves as a general medium for efficient, interpretive processing in cognitive systems.