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Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition

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  • J. Gerard Wolff

Abstract

This paper reviews evidence for the idea that much of human learning, perception, and cognition may be understood as information compression and often more specifically as “information compression via the matching and unification of patterns†(ICMUP). Evidence includes the following: information compression can mean selective advantage for any creature; the storage and utilisation of the relatively enormous quantities of sensory information would be made easier if the redundancy of incoming information was to be reduced; content words in natural languages, with their meanings, may be seen as ICMUP; other techniques for compression of information—such as class-inclusion hierarchies, schema-plus-correction, run-length coding, and part-whole hierarchies—may be seen in psychological phenomena; ICMUP may be seen in how we merge multiple views to make one, in recognition, in binocular vision, in how we can abstract object concepts via motion, in adaptation of sensory units in the eye of Limulus , the horseshoe crab, and in other examples of adaptation; the discovery of the segmental structure of language (words and phrases), grammatical inference, and the correction of over- and undergeneralisations in learning may be understood in terms of ICMUP; information compression may be seen in the perceptual constancies ; there is indirect evidence for ICMUP in human cognition via kinds of redundancy such as the decimal expansion of which are difficult for people to detect; much of the structure and workings of mathematics—an aid to human thinking—may be understood in terms of ICMUP; and there is additional evidence via the SP Theory of Intelligence and its realisation in the SP Computer Model . Three objections to the main thesis of this paper are described, with suggested answers. These ideas may be seen to be part of a “Big Picture†with six components, outlined in the paper.

Suggested Citation

  • J. Gerard Wolff, 2019. "Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition," Complexity, Hindawi, vol. 2019, pages 1-38, February.
  • Handle: RePEc:hin:complx:1879746
    DOI: 10.1155/2019/1879746
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    References listed on IDEAS

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    1. Jacob Feldman, 2000. "Minimization of Boolean complexity in human concept learning," Nature, Nature, vol. 407(6804), pages 630-633, October.
    2. Adrienne L. Fairhall & Geoffrey D. Lewen & William Bialek & Robert R. de Ruyter van Steveninck, 2001. "Efficiency and ambiguity in an adaptive neural code," Nature, Nature, vol. 412(6849), pages 787-792, August.
    3. Biswa Sengupta & Simon Barry Laughlin & Jeremy Edward Niven, 2014. "Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-18, January.
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    1. J. Gerard Wolff, 2019. "Mathematics as Information Compression via the Matching and Unification of Patterns," Complexity, Hindawi, vol. 2019, pages 1-25, December.
    2. J. Gerard Wolff, 2021. "How the SP System May Promote Sustainability in Energy Consumption in IT Systems," Sustainability, MDPI, vol. 13(8), pages 1-21, April.

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