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

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

Science (New York, N.Y.)

|May 12, 2018

Summary

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.