From size perception to counting: An evolutionary computation point of view

Gali Barabash Katz, Amit Benbassat, Liana Diesendruck, Moshe Sipper, Avishai Henik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The ability to perceive size is shared by humans and animals. Babies present this basic ability from birth, and it improves with age. Counting, on the other hand, is a more complex task than size perception. We examined the theory that the counting system evolved from a more primitive system of size perception (the leading alternative being that the two systems evolved separately). By using evolutionary computation techniques, we generated artificial neural networks (ANNs) that excelled in size perception and presented a significant advantage in evolving the ability to count over those that evolved this ability from scratch. This advantage was observed also when evolving from ANNs that master other simple classification tasks. We also show that ANNs who train to perceive size of continuous stimuli present better counting skills than those that train with discrete stimuli.

Original languageEnglish
Title of host publicationGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
Pages1675-1678
Number of pages4
DOIs
StatePublished - 26 Aug 2013
Event15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 - Amsterdam, Netherlands
Duration: 6 Jul 201310 Jul 2013

Publication series

NameGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion

Conference

Conference15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013
Country/TerritoryNetherlands
CityAmsterdam
Period6/07/1310/07/13

Keywords

  • Genetic algorithms
  • Neat
  • Neural networks
  • Numerical cognition
  • Size perception

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