Varieties of Numerical Estimation: A Unified Framework

Jike Qin, Dan Kim, John Opfer

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

4 Citations (Scopus)

Abstract

There is an ongoing debate over the psychophysical functions that best fit human data from numerical estimation tasks. To test whether one psychophysical function could account for data across diverse tasks, we examined 40 kindergartners, 38 first graders, 40 second graders and 40 adults' estimates using two fully crossed 2 × 2 designs, crossing symbol (symbolic, non-symbolic) and boundedness (bounded, unbounded) on free number-line tasks (Experiment 1) and crossing the same factors on anchored tasks (Experiment 2). Across all 8 tasks, 88.84% of participants provided estimates best fit by a mixed log-linear model, and the weight of the logarithmic component (λ) decreased with age. After controlling for age, the λ significantly predicted arithmetic skills, whereas parameters of other models failed to do so. Results suggest that the logarithmic-to-linear shift theory provides a unified account of numerical estimation and provides uniquely accurate predictions for mathematical proficiency.

Original languageEnglish
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages2943-2948
Number of pages6
ISBN (Electronic)9780991196760
Publication statusPublished - 2017
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: 26 Jul 201729 Jul 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period26/07/1729/07/17

Keywords

  • cognitive development
  • number-line estimation
  • numerical cognition
  • psychophysical function

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