Trajectories in development of information processing speed across primary school years: longitudinal study

 
PIIS020595920008507-3-1
DOI10.31857/S020595920008507-3
Publication type Article
Status Published
Authors
Occupation: Professor, Leading Researcher
Affiliation: Federal State Budget Scientific Institution “Psychological Institute of Russian Academy of Education”
Address: Moscow, Mokhovaya, 9, 4, Russia
Occupation: Researcher
Affiliation: Federal State Budget Scientific Institution “Psychological Institute of Russian Academy of Education”
Address: Moscow, Mokhovaya, 9, 4, Russia
Occupation: Professor, Director of Laboratory
Affiliation: Federal State Budgetary Institution “Psychological Institute of RAO”
Address: Moscow, Mokhovaya, 11, 9, Russia
Journal namePsikhologicheskii zhurnal
EditionVolume 41 Issue 2
Pages26-38
Abstract

The results of a four-year longitudinal study of the development of processing speed throughout primary school years are presented. The task of the study is to analyze the average path of development of the processing speed at primary school age, to assess individual-specific deviations from the average path for each participant in the study, as well as to identify gender differences in average values, the path of development of the speed of information processing and groups of children with different severity reaction times ― slow and fast.

The sample consisted of 224 schoolchildren (46% of girls) of grades 1–4 of the educational institution participating in the Russian longitudinal study of the academic success of schoolchildren. The average age of schoolchildren during the first measurement (first grade) was 7.85 years (standard deviation = 0.34) and 10.77 years (standard deviation = 0.36) during the fourth measurement (fourth grade). Participants completed the computerized task, “The Response Time of Choice,” at the end of each school year throughout the entire period of primary education.

To study the developmental trajectory of the processing speed, we used the growth analysis method with mixed effects, which allows us to estimate the average trajectory for the entire sample and individually-specific deviations from the average trajectory for each person. It is shown that across primary school years, the reaction time decreases, and the speed of processing, respectively, increases. At the same time, the trajectory of the reaction time changes nonlinearly: the most intense decline is observed from the first to second year of education in elementary school, then ― until the fourth grade, the rate of decline slows down. Boys are ahead of girls in average processing speeds for each year of primary school education. However, the pace and nature of changes in the speed of processing are not statistically significantly different for boys and girls during across primary school years.

To analyze gender differences in the processing speed in groups of schoolchildren with different reaction times, a quantile regression analysis was used to evaluate differences at opposite ends of the distribution of the variable. It is shown that girls and boys do not differ in the speed of information processing in groups of fast and slow primary school children.

 

KeywordsProcessing speed, developmental trajectories, primary school years, mixed effect growth models, gender differences, quantile regression.
AcknowledgmentStudy was supported by Russian Science Foundation, project № 17-78-30028
Received16.02.2020
Publication date04.03.2020
Number of characters31836
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1. 1. Mikhejkina O.V. Epidemiologiya umstvennoj otstalosti (obzor literatury) // Obozrenie psihiatrii i medicinskoj psihologii. 2012. № 3. P. 24–33. (in Russian)

2. Tikhomirova T.N., Malykh S.B. Kognitivnye osnovy individual'nykh razlichij v uspeshnosti obucheniya. Moscow; Saint Petersburg.: Nestor-Istoriya, 2017. (in Russian)

3. Tikhomirova T.N., Malykh S.B. Chuvstvo chisla i uspeshnost' v obuchenii matematike v mladshem shkol'nom vozraste: perekrestno-longityudnyj analiz // Psikhologicheskii zhurnal. 2018. V. 39. № 6. P. 47–58. (in Russian)

4. Tikhomirova T.N., Modyaev А.D., Leonova N.M., Malykh S.B. Faktory uspeshnosti v obuchenii na nachal'noj stupeni obshhego obrazovaniya: polovye razlichiya // Psikhologicheskii zhurnal. 2015. V.36. № 5. P. 43–54. (in Russian)

5. Farber D.A., Machinskaya R.I., Kurganskij A.V., & Petrenko N.E. Funkcional'naya organizaciya kory bol'shih polusharij pri podgotovke k opoznaniyu nepolnyh izobrazhenij u detej 7–8 let i vzroslyh // Fiziologiya cheloveka. 2014. № 40(5). P. 5–13. (in Russian)

6. Beaudreau S.A., O'Hara R. Late-life anxiety and cognitive impairment: a review // The American Journal of Geriatric Psychiatry. 2008. V. 16 (10). P. 790–803.

7. Brown L.A., Brockmole J.R., Gow A.J., Deary I.J. Processing speed and visuospatial executive function predict visual working memory ability in older adults // Experimental aging research. 2012. V. 38 (1). P. 1–19.

8. Camarata S., Woodcock R. Sex differences in processing speed: Developmental effects in males and females // Intelligence. 2006. V. 34 (3). P. 231–252.

9. Chen T., Li D. The roles of working memory updating and processing speed in mediating age-related differences in fluid intelligence // Aging, Neuropsychology, and Cognition. 2007. V.14 (6). P. 631–646.

10. Coyle T.R., Pillow D.R., Snyder A.C., Kochunov P. Processing speed mediates the development of general intelligence (g) in adolescence // Psychol. Sci. 2011. V. 22 (10). P. 1265–1269.

11. Deary I.J., Johnson W., Starr J. Are processing speed tasks biomarkers of cognitive aging? // Psychology and Aging. 2010. V. 25 (1). P. 219–228.

12. Der G., Deary I.J. Age and sex differences in reaction time in adulthood: results from the United Kingdom Health and Lifestyle Survey // Psychology and aging. 2006. V. 21 (1). P. 62–73.

13. Halpern D.F., Beninger A.S., Straight C.A. Sex differences in intelligence // The Cambridge handbook of intelligence. 2011. P. 253–272.

14. Hao L., Naiman D.Q. Quantile regression. Sage, 2007.

15. Johnson W., Carothers A., Deary I.J. Sex differences in variability in general intelligence: A new look at the old question // Perspectives on Psychological Science. 2008. V. 3 (6). P. 518–531.

16. Kail R. Processing time declines exponentially during childhood and adolescence // Developmental Psychology. 1991. V. 27 (2). P. 259–266.

17. Kail R.V., Ferrer E. Processing speed in childhood and adolescence: Longitudinal models for examining developmental change // Child development. 2007. V. 78 (6). P. 1760–1770.

18. Kerchner G.A., Racine C.A., Hale S., Wilheim R., Laluz V., Miller B.L., Kramer J.H. Cognitive processing speed in older adults: relationship with white matter integrity // PloS one. 2012. V.7 (11). P. e50425.

19. McArdle J.J., Ferrer-Caja E., Hamagami F., Woodcock R.W. Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span // Developmental psychology. 2002. V. 38 (1). P. 115–142.

20. Roivainen E. Gender differences in processing speed: A review of recent research // Learning and Individual differences. 2011. V. 21 (2). P. 145–149.

21. Rose S.A., Feldman J.F., Jankowski J.J. Modeling a cascade of effects: The role of speed and executive functioning in preterm/full‐term differences in academic achievement // Developmental science. 2011. V.14 (5). P. 1161–1175.

22. Rushton J.P., Jensen A.R. Thirty years of research on race differences in cognitive ability // Psychology, public policy, and law. 2005. V. 11 (2). P. 235–294.

23. Salthouse T. A theory of cognitive aging. Elsevier, 2000.

24. Sheppard L.D., Vernon P.A. Intelligence and speed of information-processing: A review of 50 years of research // Personality and Individual Differences. 2008. V. 44 (3). P. 535–551.

25. Silverman I.W. Sex differences in simple visual reaction time: A historical meta-analysis //Sex roles. 2006. V. 54 (1–2). P. 57–68.

26. Suades-González E., Forns J., García-Esteban R., López-Vicente M., Esnaola M., Álvarez-Pedrerol M., Sunyer J. et al. A longitudinal study on attention development in primary school children with and without teacher-reported symptoms of ADHD // Frontiers in psychology. 2017. V. 8. P. 655.

27. Tikhomirova T., Kuzmina Y. , Lysenkova I., Malykh S. Development of Approximate Number Sense across the Elementary School Years: a Cross‐cultural Longitudinal Study // Developmental Science. 2019. V. 22 (4). p. e12823.

28. Tosto M.G., Tikhomirova T., Galajinsky E., Akimova K., Kovas Y. Development and Validation of a Mathematics-number sense Web-based Test Battery // Procedia ― Social and Behavioral Sciences. 2013. V. 86. P. 423–428.

29. Zimprich D., Martin M. Can longitudinal changes in processing speed explain longitudinal age changes in fluid intelligence? // Psychology and aging. 2002. V. 17(4). P. 690–695.

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