Development of Counting Ability: An Evolutionary Computation Point of View

Gali Barabash Katz, Amit Benbassat, Moshe Sipper

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

Examination of numerical cognition encompasses multiple facets (eg, discrete vs. continuous properties, subitizing, estimation, counting, etc.). Many models have been suggested to explain these features. By looking into the basic ability to perceive size, against the complex one of counting, we hypothesize that counting system evolved on the basis of a primitive size perception system rather than the two systems evolved separately. In this chapter, we present a novel way of using evolutionary computation techniques to evolve artificial neural networks (ANNs) first to perceive size and then to count, and compare their counting skills to a different group of ANNs who evolved to count from scratch. The results revealed better counting skills when evolving first to perceive size (or other classification task) and then to count over those who evolved just to count. In addition, ANNs who evolved with continuous stimuli presented better counting skills than those evolved with discrete stimuli.

Original languageEnglish
Title of host publicationContinuous Issues in Numerical Cognition
Subtitle of host publicationHow Many or How Much
PublisherElsevier Inc.
Pages123-145
Number of pages23
ISBN (Print)9780128016374
DOIs
StatePublished - 1 Jan 2016

Keywords

  • Artificial neural networks
  • Counting
  • Evolutionary algorithms
  • Genetic algorithms
  • Numerical cognition
  • Size perception

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