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Efficient coding explains the universal law of generalization in human perception.

Chris R Sims1

  • 1Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. simsc3@rpi.edu.

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|May 12, 2018
PubMed
Summary
This summary is machine-generated.

Cognitive science explains how animals learn from experience using the universal law of generalization. This study proposes an efficient coding principle as the underlying mechanism for this fundamental learning ability.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Animal Behavior

Background:

  • Perceptual generalization and discrimination are essential cognitive functions enabling organisms to learn from experience.
  • The universal law of generalization posits that generalization follows an exponential function of stimulus distance in psychological space.
  • Existing theories struggle to fully explain the origins of this universal law.

Purpose of the Study:

  • To challenge current theoretical explanations for the universal law of generalization.
  • To propose an alternative framework for understanding generalization based on efficient coding principles.
  • To demonstrate how efficient coding can inevitably lead to the universal law of generalization.

Main Methods:

  • Theoretical analysis of information processing systems.
  • Mathematical modeling of perceptual error minimization under information constraints.
  • Comparison of predictions with existing empirical data on generalization.

Main Results:

  • The universal law of generalization can emerge from systems adhering to efficient coding principles.
  • Efficient coding provides a parsimonious explanation for generalization patterns observed in biological and artificial systems.
  • Minimizing perceptual error under information processing constraints naturally leads to exponential generalization functions.

Conclusions:

  • The principle of efficient coding offers a compelling alternative explanation for the universal law of generalization.
  • This framework unifies understanding of generalization across diverse information processing systems.
  • Future research should explore the neural and computational implementations of efficient coding in perceptual learning.