A new method for calculating individual subitizing ranges
Author(s) / Creator(s)
Leibovich-Raveh, Tali
Lewis, Daniel Jacob
Al-Rubaiey Kadhim, Saja
Ansari, Daniel
Abstract / Description
A large body of research has shown that human adults are fast and accurate at enumerating arrays of ~1-4 items. This phenomenon has been called subitizing. Above this range, enumeration is slower and less accurate. The subitizing range has been related to individual differences in variables such as mathematical abilities, working memory, etc. The two most common methods for calculating subitizing range today – bilinear fit and sigmoid fit – have their strengths and weaknesses. By combining these two methods, we overcome their biggest limitations and come up with a novel way for calculating Individual Subitizing Range (ISR). This paper introduces this new method as well as empirical studies designed to test the new method. We replicated classic effects from the literature and obtain a high correlation with the sigmoid fit method. This paper includes a Matlab code for easy calculation of ISR as well as a ready-to-use experimental file for testing ISR. We hope that these tools would be of use to researchers studying individual differences in the subitizing range.
Keyword(s)
subitizing individual difference numerical cognition counting enumeration bilinear fit sigmoid fitPersistent Identifier
Date of first publication
2018-09-07
Journal title
Journal of Numerical Cognition
Volume
4
Issue
2
Page numbers
429–447
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Leibovich-Raveh, T., Lewis, D. J., Al-Rubaiey Kadhim, S., & Ansari, D. (2018). A new method for calculating individual subitizing ranges. Journal of Numerical Cognition, 4(2), 429–447. https://doi.org/10.5964/jnc.v4i2.74
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Author(s) / Creator(s)Leibovich-Raveh, Tali
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Author(s) / Creator(s)Lewis, Daniel Jacob
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Author(s) / Creator(s)Al-Rubaiey Kadhim, Saja
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Author(s) / Creator(s)Ansari, Daniel
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PsychArchives acquisition timestamp2018-11-21T11:42:58Z
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Made available on2018-11-21T11:42:58Z
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Date of first publication2018-09-07
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Abstract / DescriptionA large body of research has shown that human adults are fast and accurate at enumerating arrays of ~1-4 items. This phenomenon has been called subitizing. Above this range, enumeration is slower and less accurate. The subitizing range has been related to individual differences in variables such as mathematical abilities, working memory, etc. The two most common methods for calculating subitizing range today – bilinear fit and sigmoid fit – have their strengths and weaknesses. By combining these two methods, we overcome their biggest limitations and come up with a novel way for calculating Individual Subitizing Range (ISR). This paper introduces this new method as well as empirical studies designed to test the new method. We replicated classic effects from the literature and obtain a high correlation with the sigmoid fit method. This paper includes a Matlab code for easy calculation of ISR as well as a ready-to-use experimental file for testing ISR. We hope that these tools would be of use to researchers studying individual differences in the subitizing range.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationLeibovich-Raveh, T., Lewis, D. J., Al-Rubaiey Kadhim, S., & Ansari, D. (2018). A new method for calculating individual subitizing ranges. Journal of Numerical Cognition, 4(2), 429–447. https://doi.org/10.5964/jnc.v4i2.74en_US
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ISSN2363-8761
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/1303
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.1495
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Language of contenteng
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PublisherPsychOpen GOLD
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Is version ofhttps://doi.org/10.5964/jnc.v4i2.74
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Keyword(s)subitizingen_US
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Keyword(s)individual differenceen_US
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Keyword(s)numerical cognitionen_US
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Keyword(s)countingen_US
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Keyword(s)enumerationen_US
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Keyword(s)bilinear fiten_US
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Keyword(s)sigmoid fiten_US
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Dewey Decimal Classification number(s)150
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TitleA new method for calculating individual subitizing rangesen_US
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DRO typearticle
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Issue2
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Journal titleJournal of Numerical Cognition
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Page numbers429–447
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Volume4
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Visible tag(s)Version of Record