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Efficient data compression in perception and perceptual memory.

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Biological systems, like engineered ones, use efficient data compression. Rate-distortion theory (RDT) principles explain human perception and memory, with new deep learning models approximating RDT in complex scenarios.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Information Theory

Background:

  • Efficient data compression is crucial for biological and engineered systems with limited capacity.
  • Rate-distortion theory (RDT) offers a framework for analyzing engineered compression systems.
  • Understanding human perception and memory may benefit from applying RDT principles.

Purpose of the Study:

  • To explore how principles of efficient data compression explain behavioral phenomena in human perception and memory.
  • To investigate the application of rate-distortion theory (RDT) to biological systems.
  • To introduce a novel deep neural network architecture for approximating RDT in high-dimensional perceptual spaces.

Main Methods:

  • Discussed three general principles of efficient data compression and their relation to RDT.
  • Reviewed existing deep neural network approaches for approximating RDT in high-dimensional spaces.
  • Introduced a new end-to-end trained deep neural network architecture operating on raw perceptual input.

Main Results:

  • Three data compression principles account for numerous behavioral phenomena and experimental findings.
  • The new deep neural network architecture approximates RDT in high-dimensional spaces.
  • The architecture is trained end-to-end on raw perceptual data, bypassing intermediate abstractions.

Conclusions:

  • The need for efficient compression likely shapes biological systems similarly to engineered ones.
  • RDT provides a valuable theoretical lens for understanding human perception and memory.
  • Future research can explore compression in memory over time and its relation to attention and visual search.