Hostname: page-component-745bb68f8f-g4j75 Total loading time: 0 Render date: 2025-02-11T15:48:38.411Z Has data issue: false hasContentIssue false

A rational explanation for links between the ANS and math

Published online by Cambridge University Press:  15 December 2021

Melissa E. Libertus
Affiliation:
Department of Psychology, Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA15260, USA. libertus@pitt.edu, shd77@pitt.edu, DSF26@pitt.edu, lek79@pitt.edu, REM166@pitt.edu, andy.ribner@pitt.edu, AMS645@pitt.eduhttps://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=530, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2004, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2039, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=1802, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=3135, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2031, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2010
Shirley Duong
Affiliation:
Department of Psychology, Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA15260, USA. libertus@pitt.edu, shd77@pitt.edu, DSF26@pitt.edu, lek79@pitt.edu, REM166@pitt.edu, andy.ribner@pitt.edu, AMS645@pitt.eduhttps://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=530, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2004, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2039, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=1802, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=3135, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2031, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2010
Danielle Fox
Affiliation:
Department of Psychology, Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA15260, USA. libertus@pitt.edu, shd77@pitt.edu, DSF26@pitt.edu, lek79@pitt.edu, REM166@pitt.edu, andy.ribner@pitt.edu, AMS645@pitt.eduhttps://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=530, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2004, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2039, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=1802, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=3135, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2031, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2010
Leanne Elliott
Affiliation:
Department of Psychology, Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA15260, USA. libertus@pitt.edu, shd77@pitt.edu, DSF26@pitt.edu, lek79@pitt.edu, REM166@pitt.edu, andy.ribner@pitt.edu, AMS645@pitt.eduhttps://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=530, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2004, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2039, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=1802, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=3135, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2031, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2010
Rebecca McGregor
Affiliation:
Department of Psychology, Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA15260, USA. libertus@pitt.edu, shd77@pitt.edu, DSF26@pitt.edu, lek79@pitt.edu, REM166@pitt.edu, andy.ribner@pitt.edu, AMS645@pitt.eduhttps://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=530, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2004, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2039, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=1802, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=3135, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2031, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2010
Andrew Ribner
Affiliation:
Department of Psychology, Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA15260, USA. libertus@pitt.edu, shd77@pitt.edu, DSF26@pitt.edu, lek79@pitt.edu, REM166@pitt.edu, andy.ribner@pitt.edu, AMS645@pitt.eduhttps://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=530, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2004, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2039, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=1802, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=3135, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2031, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2010
Alex M. Silver
Affiliation:
Department of Psychology, Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA15260, USA. libertus@pitt.edu, shd77@pitt.edu, DSF26@pitt.edu, lek79@pitt.edu, REM166@pitt.edu, andy.ribner@pitt.edu, AMS645@pitt.eduhttps://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=530, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2004, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2039, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=1802, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=3135, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2031, https://www.lrdc.pitt.edu/people/researcher-detail.cshtml?id=2010

Abstract

The proposal by Clarke and Beck offers a new explanation for the association between the approximate number system (ANS) and math. Previous explanations have largely relied on developmental arguments, an underspecified notion of the ANS as an “error detection mechanism,” or affective factors. The proposal that the ANS represents rational numbers suggests that it may directly support a broader range of math skills.

Type
Open Peer Commentary
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

We applaud Clarke and Beck (C&B) for their convincing arguments supporting the presence of an approximate number system (ANS). Most importantly, we agree with their notion that the ANS represents numbers, not numerosities or non-numerical confounds, even if its representations can be derived from computations involving perceptual cues. The ANS has attracted increasingly more attention over the last decade as correlational and training studies suggest a link between the ANS and children's and adults' math abilities. Many (but not all) studies report that children and adults with greater ANS acuity tend to perform better on math assessments both concurrently and longitudinally (Chen & Li, Reference Chen and Li2014; Fazio, Bailey, Thompson, & Siegler, Reference Fazio, Bailey, Thompson and Siegler2014; Schneider et al., Reference Schneider, Beeres, Coban, Merz, Schmidt, Stricker and De Smedt2017) and that training the ANS leads to improvements in children's and adults' math abilities (Bugden, DeWind, & Brannon, Reference Bugden, DeWind and Brannon2016). However, none of these studies have been able to provide a definitive mechanistic explanation for the association between the ANS and math abilities. Previous explanations have (1) largely relied on developmental arguments, (2) invoked the function of the ANS as an error-detection mechanism, or (3) cited possible motivational or affective factors.

Several different possibilities may explain the link between the ANS and math throughout development. On the one hand, it is possible that a more precise ANS may better support children's acquisition of exact number representations (Pinheiro-Chagas et al., Reference Pinheiro-Chagas, Wood, Knops, Krinzinger, Lonnemann, Starling-Alves and Haase2014; Wagner & Johnson, Reference Wagner and Johnson2011). For example, children's ability to map between symbolic and non-symbolic quantities is associated with their math achievement, suggesting that ANS representations are involved in the development of children's math skills via their associations with number symbols (Mundy & Gilmore, Reference Mundy and Gilmore2009). On the other hand, it is possible that a more precise ANS may serve as a foundation to understand ordinal relations between quantities and their relation to arithmetic operations, especially as children acquire these math skills (Libertus, Odic, Feigenson, & Halberda, Reference Libertus, Odic, Feigenson and Halberda2016; Mussolin, Nys, Leybaert, & Content, Reference Mussolin, Nys, Leybaert and Content2016; Park, Bermudez, Roberts, & Brannon, Reference Park, Bermudez, Roberts and Brannon2016). For example, the ability to identify ordered sequences of Arabic numerals mediates the relation between the ANS and adults' mental arithmetic (Lyons & Beilock, Reference Lyons and Beilock2011) and steadily increases in its role between first and sixth grades (Lyons, Price, Vaessen, Blomert, & Ansari, Reference Lyons, Price, Vaessen, Blomert and Ansari2014).

Another explanation is that the ANS may provide a sense of certainty about number-related judgments or serve as an “error detection mechanism” providing rough estimates of arithmetic computations and aiding in the detection of gross miscalculations (Baer & Odic, Reference Baer and Odic2019; Vo, Li, Kornell, Pouget, & Cantlon, Reference Vo, Li, Kornell, Pouget and Cantlon2014). For example, individuals' ability to detect errors in symbolic arithmetic problems is related to their ANS acuity (Wong & Odic, Reference Wong and Odic2021).

Finally, the ANS and math may be linked via motivational or affective factors. For example, greater ANS acuity in childhood may increase children's attention to number or engagement with math-related information (Libertus, Reference Libertus, Geary, Berch and Mann Koepke2019). Alternatively, greater ANS acuity may lead to greater confidence in mathematical reasoning (Wang, Odic, Halberda, & Feigenson, Reference Wang, Odic, Halberda and Feigenson2016) or poorer ANS acuity may lead to increased math anxiety (Lindskog, Winman, & Poom, Reference Lindskog, Winman and Poom2017; Maldonado Moscoso, Anobile, Primi, & Arrighi, Reference Maldonado Moscoso, Anobile, Primi and Arrighi2020; Maloney, Ansari, & Fugelsang, Reference Maloney, Ansari and Fugelsang2011).

Many of these explanations rest on the (albeit, implicit) assumption that the ANS represents natural numbers. As such, extant hypotheses cannot fully explain why the ANS may be correlated, for instance, with adults' performance on college entrance exams in math that require far more than whole number arithmetic (Libertus, Odic, & Halberda, Reference Libertus, Odic and Halberda2012). Even if, for example, the ANS is involved in error monitoring during calculations, how could this system operate to detect errors in calculations that do not depend solely on positive integers? Clarke's and Beck's proposal that the ANS represents rational numbers opens up an exciting additional explanation which may provide a missing link in the theoretical pathway from non-symbolic number representations to math abilities. Specifically, their proposal that the ANS represents rational numbers would provide a compelling explanation of how the ANS may directly support a broader range of math skills that transcend the natural numbers and operations thereon, including fraction understanding and proportional reasoning.

However, as C&B mentioned, there is a dearth of research on non-symbolic ratio processing. Future research should test the sensitivity of the ANS to rational numbers and probe the relation between the ANS and the ratio processing system (RPS), which the authors argue is a component of the ANS. An initial step is to examine the associations between individuals' performance on a wide range of tasks tapping into the ANS and the RPS that have previously only been used in separate studies. Although some research has suggested that the ANS is recruited during tasks that require the RPS or proportional reasoning (Matthews & Chesney, Reference Matthews and Chesney2015), no studies have explicitly established a correlation between the precision of these systems. O'Grady and Xu (Reference O'Grady and Xu2020) posit that children's proportional judgments of non-symbolic dot arrays are reliant on the ANS to represent discrete numbers, which are used to calculate probabilities. However, it is unclear whether the relational processing of whole numbers is supported by the ANS and/or facilitated by the RPS.

Extending beyond ratio processing, recent research on risky decision-making involving non-symbolic quantities demonstrates an association between adults' performance on tasks tapping the ANS and probability understanding (Mueller & Brand, Reference Mueller and Brand2018). For instance, individuals' non-symbolic quantity estimation relates to their abilities to estimate risks presented non-symbolically, and both of these abilities relate to adults' ability to transform and compare symbolic probabilities, an important aspect of math abilities beyond whole number operations (Mueller, Schiebener, Delazer, & Brand, Reference Mueller, Schiebener, Delazer and Brand2018). Thus, Clarke's and Beck's view of the ANS may also provide an explanation for these findings and suggest further interesting research directions, including the development of probability understanding and its link to the ANS.

In sum, the proposal that the ANS represents rational numbers helps in further elucidating the link between the ANS and math abilities. This perspective opens up interesting new directions for future research, including probing the relations between the ANS and RPS as well as understanding the relations between the ANS and decision-making processes.

Financial support

ML is supported by a Scholar Award from the James S. McDonnell Foundation. SD is supported by the National Science Foundation (NSF) through the NSF Graduate Research Fellowship Program. AR is supported by NIH NICHD F32 HD102106-01. AS is supported by the National Institutes of Health under grant T32 GM081760.

Conflict of interest

The authors declare no conflicts of interest.

References

Baer, C., & Odic, D. (2019). Certainty in numerical judgments develops independently of the approximate number system. Cognitive Development, 52, 100817.CrossRefGoogle Scholar
Bugden, S., DeWind, N. K., & Brannon, E. M. (2016). Using cognitive training studies to unravel the mechanisms by which the approximate number system supports symbolic math ability. Current Opinion in Behavioral Sciences, 10, 7380.CrossRefGoogle ScholarPubMed
Chen, Q., & Li, J. (2014). Association between individual differences in non-symbolic number acuity and math performance: A meta-analysis. Acta Psychologica, 148, 163172.CrossRefGoogle ScholarPubMed
Fazio, L. K., Bailey, D. H., Thompson, C. A., & Siegler, R. S. (2014). Relations of different types of numerical magnitude representations to each other and to mathematics achievement. Journal of Experimental Child Psychology, 123, 5372.CrossRefGoogle ScholarPubMed
Libertus, M. E. (2019). Understanding the link between the approximate number system and math abilities. In Geary, D., Berch, D., & Mann Koepke, K. (Eds.), Cognitive foundations for improving mathematical learning (pp. 91106). Elsevier.CrossRefGoogle Scholar
Libertus, M. E., Odic, D., Feigenson, L., & Halberda, J. (2016). The precision of mapping between number words and the approximate number system predicts children's formal math abilities. Journal of Experimental Child Psychology, 150, 207226.CrossRefGoogle ScholarPubMed
Libertus, M. E., Odic, D., & Halberda, J. (2012). Intuitive sense of number correlates with math scores on college-entrance examination. Acta Psychologica), 141, 373379.CrossRefGoogle ScholarPubMed
Lindskog, M., Winman, A., & Poom, L. (2017). Individual differences in nonverbal number skills predict math anxiety. Cognition, 159, 156162.CrossRefGoogle ScholarPubMed
Lyons, I. M., & Beilock, S. L. (2011). Numerical ordering ability mediates the relation between number-sense and arithmetic competence. Cognition, 121(2), 256261. doi: S0010-0277(11)00198-3 [pii]10.1016/j.cognition.2011.07.009.CrossRefGoogle ScholarPubMed
Lyons, I. M., Price, G. R., Vaessen, A., Blomert, L., & Ansari, D. (2014). Numerical predictors of arithmetic success in grades 1-6. Developmental Science, 17(5), 714726.CrossRefGoogle ScholarPubMed
Maldonado Moscoso, P. A., Anobile, G., Primi, C., & Arrighi, R. (2020). Math anxiety mediates the link between number sense and math achievements in high math anxiety young adults. Frontiers in Psychology, 11, 1095.CrossRefGoogle ScholarPubMed
Maloney, E. A., Ansari, D., & Fugelsang, J. A. (2011). The effect of mathematics anxiety on the processing of numerical magnitude. Quarterly Journal of Experimental Psychology, 64(1), 1016. doi: 930153501 [pii]10.1080/17470218.2010.533278.CrossRefGoogle ScholarPubMed
Matthews, P. G., & Chesney, D. L. (2015). Fractions as percepts? Exploring cross-format distance effects for fractional magnitudes. Cognitive Psychology, 78, 2856.CrossRefGoogle ScholarPubMed
Mueller, S. M., & Brand, M. (2018). Approximate number processing skills contribute to decision making under objective risk: Interactions with executive functions and objective numeracy. Frontiers in Psychology, 9, 1202.CrossRefGoogle ScholarPubMed
Mueller, S. M., Schiebener, J., Delazer, M., & Brand, M. (2018). Risk approximation in decision making: Approximative numeric abilities predict advantageous decisions under objective risk. Cognitive processing, 19(3), 297315.CrossRefGoogle ScholarPubMed
Mundy, E., & Gilmore, C. K. (2009). Children's mapping between symbolic and nonsymbolic representations of number. Journal of Experimental Child Psychology, 103(4), 490502. doi:S0022-0965(09)00035-6 [pii] 10.1016/j.jecp.2009.02.003.CrossRefGoogle Scholar
Mussolin, C., Nys, J., Leybaert, J., & Content, A. (2016). How approximate and exact number skills are related to each other across development: A review. Developmental Review, 39, 115.CrossRefGoogle Scholar
O'Grady, S., & Xu, F. (2020). The development of nonsymbolic probability judgments in children. Child Development, 91(3), 784798.CrossRefGoogle ScholarPubMed
Park, J., Bermudez, V., Roberts, R. C., & Brannon, E. M. (2016). Non-symbolic approximate arithmetic training improves math performance in preschoolers. Journal of Experimental Child Psychology, 152, 278293.CrossRefGoogle ScholarPubMed
Pinheiro-Chagas, P., Wood, G., Knops, A., Krinzinger, H., Lonnemann, J., Starling-Alves, I., … Haase, V. G. (2014). In how many ways is the approximate number system associated with exact calculation? PLoS One, 9(11), e111155.CrossRefGoogle ScholarPubMed
Schneider, M., Beeres, K., Coban, L., Merz, S., Schmidt, S. S., Stricker, J., & De Smedt, B. (2017). Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: A meta-analysis. Developmental Science, 20(3), e12372. doi: 10.1111/desc.12372CrossRefGoogle ScholarPubMed
Vo, V. A., Li, R., Kornell, N., Pouget, A., & Cantlon, J. F. (2014). Young children bet on their numerical skills: Metacognition in the numerical domain. Psychological Science, 25(9), 17121721. doi: 10.1177/0956797614538458CrossRefGoogle ScholarPubMed
Wagner, J. B., & Johnson, S. C. (2011). An association between understanding cardinality and analog magnitude representations in preschoolers. Cognition, 119(1), 1022. doi:10.1016/j.cognition.2010.11.014CrossRefGoogle ScholarPubMed
Wang, J., Odic, D., Halberda, J., & Feigenson, L. (2016). Changing the precision of preschoolers’ approximate number system representations changes their symbolic math performance. Journal of Experimental Child Psychology, 147, 8299.CrossRefGoogle ScholarPubMed
Wong, H., & Odic, D. (2021). The intuitive number sense contributes to symbolic equation error detection abilities. Journal of Experimental Psychology: Learning, Memory, and Cognition, 47(1), 110.Google ScholarPubMed