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Configured-groups hypothesis: fast comparison of exact large quantities without counting

Overview of attention for article published in Cognitive Processing, July 2017
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Title
Configured-groups hypothesis: fast comparison of exact large quantities without counting
Published in
Cognitive Processing, July 2017
DOI 10.1007/s10339-017-0826-5
Pubmed ID
Authors

Sébastien Miravete, André Tricot, Slava Kalyuga, Franck Amadieu

Abstract

Our innate number sense cannot distinguish between two large exact numbers of objects (e.g., 45 dots vs 46). Configured groups (e.g., 10 blocks, 20 frames) are traditionally used in schools to represent large numbers. Previous studies suggest that these external representations make it easier to use symbolic strategies such as counting ten by ten, enabling humans to differentiate exactly two large numbers. The main hypothesis of this work is that configured groups also allow for a differentiation of large exact numbers, even when symbolic strategies become ineffective. In experiment 1, the children from grade 3 were asked to compare two large collections of objects for 5 s. When the objects were organized in configured groups, the success rate was over .90. Without this configured grouping, the children were unable to make a successful comparison. Experiments 2 and 3 controlled for a strategy based on non-numerical parameters (areas delimited by dots or the sum areas of dots, etc.) or use symbolic strategies. These results suggest that configured grouping enables humans to distinguish between two large exact numbers of objects, even when innate number sense and symbolic strategies are ineffective. These results are consistent with what we call "the configured group hypothesis": configured groups play a fundamental role in the acquisition of exact numerical abilities.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 21%
Researcher 2 14%
Student > Master 2 14%
Lecturer > Senior Lecturer 1 7%
Professor 1 7%
Other 3 21%
Unknown 2 14%
Readers by discipline Count As %
Psychology 5 36%
Social Sciences 2 14%
Unspecified 1 7%
Medicine and Dentistry 1 7%
Neuroscience 1 7%
Other 1 7%
Unknown 3 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 August 2017.
All research outputs
#20,441,465
of 22,996,001 outputs
Outputs from Cognitive Processing
#293
of 337 outputs
Outputs of similar age
#247,742
of 283,532 outputs
Outputs of similar age from Cognitive Processing
#7
of 11 outputs
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