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Phenotypic correlations. Magnitudes of the phenotypic correlation between Number Line and the mathematics measures are marked on the Y‐axis. The whiskers represent their 95% confidence intervals (CIs). Mathematics measures are marked on the X‐axis: fluency (Problem Verification in the United Kingdom, Canada and Russia; Fluency WJ‐III in the United States), problem‐solving (Understanding Numbers in the United Kingdom, Canada and Russia; Applied Problems WJ‐III in the United States), and Mathematics Composite. See Table S7 for the exact values of correlations and CIs.
Source publication
Background
The number line task assesses the ability to estimate numerical magnitudes. People vary greatly in this ability, and this variability has been previously associated with mathematical skills. However, the sources of individual differences in number line estimation and its association with mathematics are not fully understood.
Aims
This l...
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In this study we examined 100 pairs of monozygotic (MZ) twins to determine if lifestyle differences between control and experimental twins affected lifespan and health. We used the twin database of the Russian Humanitarian Scientific Foundation. The dependent variables were the difference in lifespan and the number of socially significant diseases...
Citations
... Действительно, в исследованиях способность к репрезентации количества определяется как эволюционно сохраненный признак, повышающий адаптивные возможности человека (Libertus, Feigenson, Halberda, 2011;Wang, Odic, Halberda, Feigenson, 2016), а индивидуальные преимущества в точности и скорости обработки количественной информации открывают дополнительные возможности в области образования и профессиональных достижений (Тихомирова, Малых, 2021;Libertus, Feigenson, Halberda, 2013;Orrantia, Muñez, Matilla, Sanchez, San Romualdo, Verschaffel, 2019). Согласно исследованиям с участием российских респондентов, при выполнении задач на оценку количественной информации зафиксирован широкий спектр индивидуальных различий, который в том числе оказывается связанным с форматом предъявляемой информациитолько с помощью объектов (несимволический формат), только чисел (символический формат) или чисел и объектов одновременно (смешанный формат) (Тихомирова, Малых, 2022;Tosto et al., 2019). Так, максимальный диапазон времени выполнения школьником задания характерен для теста со стимульным материалом в виде чисел, где требуется отметить позицию числа на числовой линии (Тихомирова, Малых, 2023). ...
... Индивидуальные различия по времени выполнения заданий на оценку количества наиболее заметно проявляются в тесте «Числовая линия» с использованием количественных стимулов в виде чисел по сравнению с тестами «Чувства числа» с объектами и «Точки и числа» с объектами и числами (стандартное отклонение 2,4 против 0,3 и 0,3 соответственно). Этот результат согласуется с результатами исследований с участием различных социокультурных и возрастных групп респондентов (см., напр.: Tosto et al., 2019). Так, в кросс-культурном исследовании с участием школьников из России, Великобритании, Канады и США зафиксирован максимально широкий диапазон индивидуальных различий при выполнении теста с числовой линией вне зависимости от социокультурного контекста (Tosto et al., 2019). ...
... Этот результат согласуется с результатами исследований с участием различных социокультурных и возрастных групп респондентов (см., напр.: Tosto et al., 2019). Так, в кросс-культурном исследовании с участием школьников из России, Великобритании, Канады и США зафиксирован максимально широкий диапазон индивидуальных различий при выполнении теста с числовой линией вне зависимости от социокультурного контекста (Tosto et al., 2019). ...
Introduction. Tasks containing quantitative information are included in the educational programs of many school subjects, and the time of their completion becomes one of the criteria for the academic competitiveness of schoolchildren. At the same time, the ratio of the general processing speed and the speed of processing information about quantity can vary depending on the format of tests’ stimuli. Goals & objectives. The aim of this study was analyzing the relationship between general processing speed and speed of processing information about quantity of different formats. The focus is on quantitative information expressed in non-symbolic (only objects), symbolic (only numbers) and mixed (objects and numbers) formats. The relationship between general processing speed and speed of processing non-symbolic quantitative information was analyzed while controlling the accuracy, in groups of schoolchildren with high and low accuracy. Materials and methods. The study involved 589 high schoolchildren ages 14.8 to 18.0 (M = 16.6; SD = 0.9), of whom 54.9% were female. General processing speed was measured using the computerized test “Choice Reaction Time”; and test’s speed of processing quantitative information by the tests “Number Sense”, “Number Line” and “Dot Number” (Tikhomirova, Malykh, 2023). Statistical analysis was performed using the correlation analysis, including in groups identified based on the number of correct answers when representing nonsymbolic quantity. Results. It was shown that general processing speed and speed of processing of the quantity expressed by numbers (symbolic format) or numbers and objects (mixed format) are directly proportionally moderately interrelated. On the contrary, when completing tests with quantitative information containing only objects (non-symbolic format), general processing speed and test’s time of completing were not interrelated. At the same time, this interrelation becomes noticeable only in the group of schoolchildren with very high accuracy in tasks with non-symbolic quantity. Conclusions. The speed of cognitive processes associated with the processing of quantitative information are characterized by specificity, which is determined by the format of the quantity – non-symbolic, symbolic or mixed.
... In a meta-analysis of research data from 45 countries, cross-cultural di erences were associated, rst, with the e ectiveness of the functioning of the national education system at the level of r = 0.25 at p < 0.001 and the socioeconomic status of states at the level of r = 0.16 at p < 0.001 (Brouwers, Van de Vijver, Van Hemert, 2009). It was shown that in a less heterogeneous more e ective educational environment the contribution of cognitive abilities to the formation of individual di erences in academic success increases (Tosto et al., 2019;Tucker-Drob, & Bates, 2016). Additionally, greater school subject orientation of the national education system, such as towards mathematics, can inuence the educational achievements of schoolchildren in this subject (Paik et al., 2011). ...
Background:
The cognitive predictors of academic achievement are associated both with basic cognitive abilities such as the information processing speed, number sense and visuospatial working memory, as well as with general ability including nonverbal intelligence. However, the ratio between cognitive development and school achievement can depend on sociocultural conditions.
Objective:
The results of a cross-cultural analysis of the relationship between cognitive development and academic achievement during primary education are presented. The analysis was conducted sampling schoolchildren from Russia and Kyrgyzstan, two countries that have a similar organization of the national education system but differ in the level of socioeconomic development.
Design:
The study involved 732 schoolchildren aged 7.7 to 11.8 years studying in Russia and Kyrgyzstan. Information processing speed, visuospatial working memory, and number sense were assessed using each of "Choice Reaction Time," "Corsi Block-Tapping Test," and "Number Sense" computerized tests.
Results:
According to the results, empirical data in both samples show that a model where in information processing speed signifies basic cognitive ability is a key predictor of nonverbal intelligence, working memory, and number sense, and each of these may contribute to individual differences in academic achievement. Notwithstanding the universality of this model, cross-cultural differences were seen to engender a reduction of schoolchildren's academic achievements, given possible impacts of less favorable educational conditions.
Conclusion:
In the relationship between cognitive abilities and academic success at the primary school education, there are both similarities and differences between schoolchildren studying in Russia and Kyrgyzstan.
... The present research evaluates the extent to which gender differences arise in confidence on number-line estimation, a task which taps the fundamental ability to estimate numerical magnitude (and is predictive of future math achievement; e.g., Bailey et al. 2014;Siegler 2006, 2008;Fazio et al. 2014;Fuchs et al. 2010;Geary 2011;Schneider et al. 2018;Siegler 2016;Siegler et al. 2011Siegler et al. , 2012Siegler and Thompson 2014;Tosto et al. 2018). In this task, participants are (1) asked to estimate where a provided number (e.g., 25) falls on a horizontal number line and (2) asked to judge their confidence in their estimatean example is presented in Fig. 1. ...
... As compared to girls/women, why did boys/men make more precise number-line estimates? Although considerable debate exists over the causes of such gender differences when they are observed (e.g., Hyde 2014), we imagine that psychological (e.g., differences in math attitudes; Sidney et al. 2019), social (e.g., differences in early spatial experiences, such as exposure to spatial language, media, and toys; Caldera et al. 1989;Doyle et al. 2012;Pruden and Levine 2017; or gendered stereotypes about math and spatial ability, McGlone and Aronson 2006; Moè and Pazzaglia 2006), and possibly even biological factors (e.g., sexual dimorphism in the parietal cortex; Goldstein et al. 2001) could contribute to the gender differences observed in the numberline estimation task (as is the case for performance; e.g., Tosto et al. 2018). We leave it to future research to evaluate the contribution of these factors to the present gender differences observed. ...
Prior research has found gender differences in spatial tasks in which men perform better, and are more confident, than women. Do gender differences also occur in people’s confidence as they perform number-line estimation, a common spatial-numeric task predictive of math achievement? To investigate this question, we analyzed outcomes from six studies (N = 758 girls/women and boys/men with over 20,000 observations; grades 1–5 and adults) that involved a similar method: Participants estimated where a provided number (e.g., ¾, 37) was located on a bounded number line (e.g., 0–1; 0–100), then judged their confidence in that estimate. Boys/men were more precise (g = .52) and more confident (g = .30) in their estimates than were girls/women. Linear mixed model analyses of the trial-level data revealed that girls’/women’s estimates had about 31% more error than did boys’/men’s estimates, and even when controlling for precision, girls/women were about 7% less confident in their estimates than were boys/men. These outcomes should encourage researchers to consider gender differences for studies on math cognition and provide pathways for future research to address potential mechanisms underlying the present gender gaps.
... The present research evaluates the extent to which gender differences arise in confidence on number-line estimation, a task which taps the fundamental ability to estimate numerical magnitude (and is predictive of future math achievement; e.g., Bailey et al. 2014;Siegler 2006, 2008;Fazio et al. 2014;Fuchs et al. 2010;Geary 2011;Schneider et al. 2018;Siegler 2016;Siegler et al. 2011Siegler et al. , 2012Siegler and Thompson 2014;Tosto et al. 2018). In this task, participants are (1) asked to estimate where a provided number (e.g., 25) falls on a horizontal number line and (2) asked to judge their confidence in their estimatean example is presented in Fig. 1. ...
... As compared to girls/women, why did boys/men make more precise number-line estimates? Although considerable debate exists over the causes of such gender differences when they are observed (e.g., Hyde 2014), we imagine that psychological (e.g., differences in math attitudes; Sidney et al. 2019), social (e.g., differences in early spatial experiences, such as exposure to spatial language, media, and toys; Caldera et al. 1989;Doyle et al. 2012;Pruden and Levine 2017; or gendered stereotypes about math and spatial ability, McGlone and Aronson 2006; Moè and Pazzaglia 2006), and possibly even biological factors (e.g., sexual dimorphism in the parietal cortex; Goldstein et al. 2001) could contribute to the gender differences observed in the numberline estimation task (as is the case for performance; e.g., Tosto et al. 2018). We leave it to future research to evaluate the contribution of these factors to the present gender differences observed. ...
Prior research has found gender differences in spatial tasks in which men perform better, and are more confident, than women. Do gender differences also occur in people’s confidence as they perform number-line estimation, a common spatial-numeric task predictive of math achievement? To investigate this question, we analyzed outcomes from six studies (N = 758 girls/women and boys/men with over 20,000 observations; grades 1-5 and adults) that involved a similar method: Participants estimated where a provided number (e.g., ¾, 37) was located on a bounded number line (e.g., 0-1; 0-100), then judged their confidence in that estimate. Boys/men were more precise (g = .52) and more confident (g = .30) in their estimates than were girls/women. Linear mixed model analyses of the trial-level data revealed that girls’/women’s estimates had about 31% more error than did boys’/men’s estimates, and even when controlling for precision, girls/women were about 7% less confident in their estimates than were boys/men. These outcomes should encourage researchers to consider gender differences for studies on math cognition and provide pathways for future research to address potential mechanisms underlying the present gender gaps.
... In contrast, in more homogeneous school settings, genetic effects drive the covariation between basic numerical cognition and mathematics. (Tosto et al., 2018). ...
... Cuando la investigación se realiza en contextos internacionales o zonas con contextos escolares heterogéneos, el ambiente parece ser más importante para generar variación en las habilidades numéricas y su asociación con las matemáticas. En contraste, en contextos escolares más homogéneos, los efectos genéticos generan la co-variación entre la cognición numérica básica y las matemáticas (Tosto et al., 2018). ...
In this revised and updated edition of Hunt's classic textbook, Human Intelligence, two research experts explain how key scientific studies have revealed exciting information about what intelligence is, where it comes from, why there are individual differences, and what the prospects are for enhancing it. The topics are chosen based on the weight of evidence, allowing readers to evaluate what ideas and theories the data support. Topics include IQ testing, mental processes, brain imaging, genetics, population differences, sex, aging, and likely prospects for enhancing intelligence based on current scientific evidence. Readers will confront ethical issues raised by research data and learn how scientists pursue answers to basic and socially relevant questions about why intelligence is important in everyday life. Many of the answers will be surprising and stimulate readers to think constructively about their own views.
Background
Number line accuracy (NL accuracy) shows improvement over the course of a school education. However, there are practically no cross‐country longitudinal studies of NL accuracy over the whole course of elementary school.
Aims
This study investigated the developmental trajectories of NL accuracy and its types across the elementary school years in two countries—Russia and Kyrgyzstan.
Sample(s)
The analyses were carried out on the data collected from the sample of 508 schoolchildren at Grades 1, 2, 3 and 4 (aged 6.4–11.9 years) from Russia and Kyrgyzstan, who were surveyed as part of the ‘Cross‐cultural Longitudinal Analysis of Student Success’ project.
Methods
The participants were administered the ‘Number Line’ computerized test task and a paper‐and‐pencil ‘Standard Progressive Matrices’ test at the end of each academic year.
Results
During the course of the elementary school education, NL accuracy increases nonlinearly in both samples from Grade 1 to Grade 4, with a pronounced increase in the rate of improvement from the first to the second year. Cross‐country differences in NL accuracy were observed during each year of schooling as well as in the growth of NL accuracy. The development of NL accuracy is described by a model with two developmental types: (1) ‘high start and growth’ (93% of the pooled sample) and (2) ‘low start and no growth’ (7%).
Conclusions
Both NL accuracy and the rate of its growth during elementary school depend on educational conditions. Cross‐country differences in the distribution of schoolchildren by these two developmental types were statistically insignificant.
The number line estimation task is an often-used measure of numerical magnitude understanding. The task also correlates substantially with broader measures of mathematical achievement. This raises the question of whether the task would be a useful component of mathematical achievement tests and instruments to diagnose dyscalculia or mathematical giftedness and whether a stand-alone version of the task can serve as a short screener for mathematical achievement. Previous studies on the relation between number line estimation accuracy and broader mathematical achievement were limited in that they used relatively small nonrepresentative samples and usually did not account for potentially confounding variables. To close this research gap, we report findings from a population-level study with nearly all Luxembourgish ninth-graders (N = 6484). We used multilevel regressions to test how a standardized mathematical achievement test relates to the accuracy in number line estimation on bounded number lines with whole numbers and fractions. We also investigated how these relations were moderated by classroom characteristics, person characteristics, and trial characteristics. Mathematical achievement and number line estimation accuracy were associated even after controlling for potentially confounding variables. Subpopulations of students showed meaningful differences in estimation accuracy, which can serve as benchmarks in future studies. Compared with the number line estimation task with whole numbers, the number line estimation task with fractions was more strongly related to mathematical achievement in students across the entire mathematical achievement spectrum. These results show that the number line estimation task is a valid and useful tool for diagnosing and monitoring mathematical achievement.
