The Relative Strengths of Relationships Between Fine Motor Skills,Working Memory,Processing Speed and Fluid Intelligence in Early Elementary School Children

Abstract

The goal in the present paper was to examine the combined and relative impact of fine motor ability, auditory working memory, and processing speed on fluid intelligence in a sample of early elementary school students. Our participant sample was 145 children (Mage = 9.1 years, SD = 1.1; 80 boys, 65 girls). We used the Raven’s Colored Progressive Matrices Test as a measure of fluid intelligence and five other measures to represent the three predictor variables: the Grooved Pegboard Test as a measure of fine motor skills, Digit Span Forwards and Digit Span Backwards tests as measures of working memory, and Rapid Automatized Naming and Letter-Digit Substitution tasks as measures of processing speed. Regression analyses indicated that only two of these measures had a statistically significant association with the fluid intelligence test scores, namely, scores on the Grooved Pegboard (fine motor skills) and Digit Span Backwards (working memory) tests, with these two measures explaining 35% of the variance in the fluid intelligence test scores. Thus, fine motor skills and working memory were correlated with fluid intelligence in early elementary-grade students. Until the directions of these relationships are better understood, we might assume that interventions aiming to increase young children’s fluid intelligence, or at least their intelligence scores, might partly target working memory and fine motor skills.

Keywords

fluid intelligence memory processing speed fine motor skills elementary school students

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References

Ackerman

P. L.

Beier

M. E.

Boyle

M. O.

(2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131, 30–60. https://doi.org/10.1037/0033-2909.131.1.30

Baddeley

(1992). Working memory. Science, 255(5044), 556–559. https://doi.org/10.1126/science.1736359

Borella

Pezzuti

De Beni

Cornoldi

(2020). Intelligence and working memory: Evidence from administering the WAIS-IV to Italian adults and elderly. Psychological Research, 84(6), 1622–1634. https://doi.org/10.1007/s00426-019-01173-7

Cattell

R. B.

(1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54, 1–22. https://doi.org/10.1037/h0046743

Chooi

W.-T.

Thompson

L. A.

(2012). Working memory training does not improve intelligence in healthy young adults. Intelligence, 40(6), 531–542. https://doi.org/10.1016/j.intell.2012.07.004

Colom

Flores-Mendoza

Rebollo

(2003). Working memory and intelligence. Personality and Individual Differences, 34(1), 33–39. https://doi.org/10.1016/S0191-8869(02)00023-5

Cotton

S. M.

Kiely

P. M.

Crewther

D. P.

Thomson

Laycock

Crewther

S. G.

(2005). A normative and reliability study for the Raven’s coloured progressive matrices for primary school aged children from Victoria, Australia. Personality and Individual Differences, 39(3), 647–659. https://doi.org/10.1016/j.paid.2005.02.015

Davis

E. E.

Pitchford

N. J.

Limback

(2011). The interrelation between cognitive and motor development in typically developing children aged 4–11 years is underpinned by visual processing and fine manual control. British Journal of Psychology, 102(3), 569–584. https://doi.org/10.1111/j.2044-8295.2011.02018.x

Denckla

M. B.

Cutting

L. E.

(1999). History and significance of rapid automatized naming. Annals of Dyslexia, 49(1), 29–42. https://doi.org/10.1007/s11881-999-0018-9

10.

de Paula

J. J.

Malloy-Diniz

L. F.

Romano-Silva

M. A.

(2016). Reliability of working memory assessment in neurocognitive disorders: A study of the digit span and Corsi block-tapping tasks. Brazilian Journal of Psychiatry, 38, 262–263. https://doi.org/10.1590/1516-4446-2015-1879

11.

Emami Kashfi

Sohrabi

Saberi Kakhki

Mashhadi

Jabbari Nooghabi

(2019). Effects of a motor intervention program on motor skills and executive functions in children with learning disabilities. Perceptual and Motor Skills, 126(3), 477–498. https://doi.org/10.1177/0031512519836811

12.

Engel de Abreu

P. M. J.

Conway

A. R. A.

Gathercole

S. E.

(2010). Working memory and fluid intelligence in young children. Intelligence, 38(6), 552–561. https://doi.org/10.1016/j.intell.2010.07.003

13.

Fry

A. F.

Hale

(2000). Relationships among processing speed, working memory, and fluid intelligence in children. Biological Psychology, 54(1), 1–34. https://doi.org/10.1016/S0301-0511(00)00051-X

14.

Harrison

T. L.

Shipstead

Hicks

K. L.

Hambrick

D. Z.

Redick

T. S.

Engle

R. W.

(2013). Working memory training may increase working memory capacity but not fluid intelligence. Psychological Science, 24(12), 2409–2419. https://doi.org/10.1177/0956797613492984

15.

Hilbert

Nakagawa

T. T.

Puci

Zech

Bühner

(2015). The digit span backwards task. European Journal of Psychological Assessment, 31(3), 174–180. https://doi.org/10.1027/1015-5759/a000223

16.

Jaeggi

S. M.

Buschkuehl

Jonides

Perrig

W. J.

(2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105(19), 6829–6833. https://doi.org/10.1073/pnas.0801268105

17.

Jensen

A. R.

(1998). The g factor: The science of mental ability Praeger. Greenwoood.

18.

Kazem

A. M.

Alzubiadi

A. S.

Yousif

Y. H.

Aljamali

F. A.

Al-Mashdany

S. I.

Alkharusi

H. A.

Al-Busaidi

O. B.

Al-Bulushi

S. S.

Al-Bahrani

W. A.

Al-Fori

S. M.

(2007). Psychometric properties of Raven’s colored progressive matrices for Omani children aged 5 through 11 years. Social Behavior and Personality: An International Journal, 35(10), 1385–1398. https://doi.org/10.2224/sbp.2007.35.10.1385

19.

Kent

(2017). Fluid intelligence: A brief history. Applied Neuropsychology: Child, 6(3), 193–203. https://doi.org/10.1080/21622965.2017.1317480

20.

Klupp

Möhring

Lemola

Grob

(2021). Relations between fine motor skills and intelligence in typically developing children and children with attention deficit hyperactivity disorder. Research in Developmental Disabilities, 110, 103855. https://doi.org/10.1016/j.ridd.2021.103855

21.

Lezak

M. D.

Howieson

D. B.

Loring

D. W.

Fischer

J. S.

(2012). Neuropsychological assessment. Oxford University Press.

22.

Memisevic

Sinanovic

(2013). Executive functions as predictors of visual-motor integration in children with intellectual disability. Perceptual and Motor Skills, 117(3), 913–922. https://doi.org/10.2466/15.25.PMS.117x25z4

23.

Mueller

S. T.

Piper

B. J.

(2014). The psychology experiment Building Language (PEBL) and PEBL test battery. Journal of Neuroscience Methods, 222, 250–259. https://doi.org/10.1016/j.jneumeth.2013.10.024

24.

Naglieri

J. A.

(1989). A cognitive processing theory for the measurement of intelligence. Educational Psychologist, 24(2), 185–206. https://doi.org/10.1207/s15326985ep2402_4

25.

Pangelinan

M. M.

Zhang

VanMeter

J. W.

Clark

J. E.

Hatfield

B. D.

Haufler

A. J.

(2011). Beyond age and gender: Relationships between cortical and subcortical brain volume and cognitive-motor abilities in school-age children. NeuroImage, 54(4), 3093–3100. https://doi.org/10.1016/j.neuroimage.2010.11.021

26.

Pessoa

Ungerleider

L. G.

(2004). Top-down mechanisms for working memory and attentional processes. In Gazzaniga

M. S.

(Ed.), The cognitive neurosciences (pp. 919–930). Boston Review.

27.

Piaget

Inhelder

(2008). The psychology of the child. Basic Books.

28.

Raven

(2003). Raven progressive matrices. In: McCallum

R. S.

(eds) Handbook of nonverbal assessment. Springer. https://doi.org/10.1007/978-1-4615-0153-4_11

29.

Raven

J. C.

(1986). Coloured progressive matrices, sets A, Ab, B. Lewis & Co.

30.

Reynolds

C. R.

(1997). Forward and backward memory span should not be combined for clinical analysis. Archives of Clinical Neuropsychology, 12(1), 29–40. https://doi.org/10.1016/S0887-6177(96)00015-7

31.

Roebers

C. M.

Jäger

(2014). The relative importance of fine motor skills, intelligence, and executive functions for first graders’ reading and spelling skills. Perspectives on Language and Literacy, 40(2), 13–17.

32.

Ruff

R. M.

Parker

S. B.

(1993). Gender-and age-specific changes in motor speed and eye-hand coordination in adults: Normative values for the finger tapping and grooved pegboard tests. Perceptual and Motor Skills, 76(3_suppl), 1219–1230. https://doi.org/10.2466/pms.1993.76.3c.1219

33.

Salthouse

T. A.

(1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428. https://doi.org/10.1037/0033-295X.103.3.403

34.

Schofield

N. J.

Ashman

A. F.

(1986). The relationship between digit span and cognitive processing across ability groups. Intelligence, 10(1), 59–73. https://doi.org/10.1016/0160-2896(86)90027-9

35.

Schweizer

Moosbrugger

(2004). Attention and working memory as predictors of intelligence. Intelligence, 32(4), 329–347. https://doi.org/10.1016/j.intell.2004.06.006

36.

Skogan

A. H.

Oerbeck

Christiansen

Lande

H. L.

Egeland

(2018). Updated developmental norms for fine motor functions as measured by finger tapping speed and the Grooved Pegboard Test. Developmental Neuropsychology, 43(7), 551–565. https://doi.org/10.1080/87565641.2018.1495724

37.

Smirni

(2020). The Raven’s coloured progressive matrices in healthy children: A qualitative approach.Brain Sciences, 10(11), 877. https://doi.org/10.3390/brainsci10110877

38.

Sternberg

R. J.

(2008). Increasing fluid intelligence is possible after all. Proceedings of the National Academy of Sciences, 105(19), 6791–6792. https://doi.org/10.1073/pnas.0803396105

39.

Tolle

K. A.

Rahman-Filipiak

A. M.

Hale

A. C.

Kitchen Andren

K. A.

Spencer

R. J.

(2020). Grooved Pegboard Test as a measure of executive functioning. Applied Neuropsychology: Adult, 27(5), 414–420. https://doi.org/10.1080/23279095.2018.1559165

40.

Tourva

Spanoudis

(2020). Speed of processing, control of processing, working memory and crystallized and fluid intelligence: Evidence for a developmental cascade. Intelligence, 83, 101503. https://doi.org/10.1016/j.intell.2020.101503

41.

Van der Elst

Dekker

Hurks

Jolles

(2012). The letter digit substitution test: Demographic influences and regression-based normative data for school-aged children. Archives of Clinical Neuropsychology, 27(4), 433–439. https://doi.org/10.1093/arclin/acs045

42.

Waters

G. S.

Caplan

(2003). The reliability and stability of verbal working memory measures. Behavior Research Methods, Instruments, & Computers, 35(4), 550–564. https://doi.org/10.3758/BF03195534

43.

Wechsler

(1981a). The psychometric tradition: Developing the Wechsler adult intelligence scale. Contemporary Educational Psychology, 6, 82–85. https://doi.org/10.1016/0361-476X(81)90035-7

44.

Wechsler

(1981b). Wechsler adult intelligence scale-revised manual. New York: Psychological Corporation.

45.

Wolf

Denckla

M. B.

(2005). RAN/RAS: Rapid automatized naming and rapid alternating stimulus tests. Pro-ed.

46.

Zaval

Johnson

E. J.

Weber

E. U.

(2015). Chapter 8 - Complementary contributions of fluid and crystallized intelligence to decision making across the life span. In Hess

T. M.

Strough

Löckenhoff

C. E.

(Eds.), Aging and decision making (pp. 149–168). Academic Press. https://doi.org/10.1016/B978-0-12-417148-0.00008-X

47.

Zimprich

Martin

(2002). Can longitudinal changes in processing speed explain longitudinal age changes in fluid intelligence? Psychology and Aging, 17, 690–695. https://doi.org/10.1037/0882-7974.17.4.690