Volume 15, Issue 30 (12-2025)                   JRSM 2025, 15(30): 185-210 | Back to browse issues page

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Salehi S K, Hatami F, Norouzi F. The Effect Of Cyberspace Dependence On Explicit And Implicit Motor Sequence Learning Task. JRSM 2025; 15 (30) :185-210
URL: http://jrsm.khu.ac.ir/article-1-3255-en.html
1- Assistant Professor of Motor Behavior, Faculty of Sport Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran. , Sk.salehi@yahoo.com
2- Associate Professor of Motor Behavior, Faculty of Sport Sciences, Shahid Rajaee Teacher Training University Tehran, Iran.
3- MSc of Motor Behavior, Faculty of Sport Sciences, Shahid Rajaee Teacher Training University Tehran, Iran.
Abstract:   (3763 Views)
Background: Cyberspace dependence can affect cognitive and motor functions, including learning and memory.
Aim: The present study aimed to investigate the effect of cyberspace dependence on explicit and implicit learning of a motor sequence task.
Methods: Participants were 48 high school students aged 16 to 18 years, selected through convenience sampling and divided into four groups of 12: cyberspace dependent–explicit learning, cyberspace dependent–implicit learning, non-dependent–explicit learning, and non-dependent–implicit learning. Young’s Internet Addiction Test was used to distinguish cyberspace-dependent from non-dependent individuals, and the Serial Reaction Time Task (SRTT) software was applied to assess motor learning. Data were analyzed using mixed-design ANOVA with repeated measures.
Results: The findings revealed that cyberspace dependence had a significant effect on explicit and implicit learning of the motor sequence task (P<0/05). Specifically, non-dependent participants outperformed their cyberspace-dependent peers in both explicit and implicit learning conditions.
Conclusion: The results suggest that cyberspace dependence may weaken both explicit and implicit learning. Accordingly, it is recommended that schools
and other educational environments implement engaging motor activity programs to reduce excessive cyberspace use among students and to promote motor learning.
Full-Text [PDF 2974 kb]   (228 Downloads)    
Type of Study: Research | Subject: motor behavior
Received: 2024/09/16 | Accepted: 2025/09/14 | ePublished ahead of print: 2025/09/14 | Published: 2025/12/31

References
1. Galván A. Neural plasticity of development and learning. Human brain mapping. 2010;31(6):879-90. [DOI:10.1002/hbm.21029]
2. Schmidt RA, Lee TD, Winstein C, Wulf G, Zelaznik HN. Motor control and learning: A behavioral emphasis: Human kinetics; 2018. https://www.amazon.com/Motor-Control-Learning-Behavioral-Emphasis/dp/0736079610
3. Salehi SK, Miri-Lavasani N, Hajipour A, Talebrokni FS. Explicit and implicit motor sequence learning: motor learning analysis in children with Down syndrome. RICYDE Revista Internacional de Ciencias del Deporte. 2019;15(57):266-79. [DOI:10.5232/ricyde2019.05705]
4. Jongbloed-Pereboom M, Janssen AJ, Steiner K, Steenbergen B, Nijhuis-van der Sanden MW. Implicit and explicit motor sequence learning in children born very preterm. Research in Developmental Disabilities. 2017;60:145-52. [DOI:10.1016/j.ridd.2016.11.014]
5. Izadi-Najafabadi S, Mirzakhani-Araghi N, Miri-Lavasani N, Nejati V, Pashazadeh-Azari Z. Implicit and explicit motor learning: Application to children with Autism Spectrum Disorder (ASD). Research in developmental disabilities. 2015;47:284-96. [DOI:10.1016/j.ridd.2015.09.020]
6. Janacsek K, Nemeth D. Implicit sequence learning and working memory: correlated or complicated? Cortex. 2013;49(8):2001-6. [DOI:10.1016/j.cortex.2013.02.012]
7. Nejati V, Garusi Farshi M, Ashayeri H, Aghdasi M. Dual task interference in implicit sequence learning by young and old adults. International journal of geriatric psychiatry. 2008;23(8):801-4. [DOI:10.1002/gps.1976]
8. Williams JN. The neuroscience of implicit learning. Language Learning. 2020;70(S2):255-307. [DOI:10.1111/lang.12405]
9. Dahms C, Brodoehl S, Witte OW, Klingner CM. The importance of different learning stages for motor sequence learning after stroke. Human brain mapping. 2020;41(1):270-86. [DOI:10.1002/hbm.24793]
10. Lum JA, Clark GM, Barhoun P, Hill AT, Hyde C, Wilson PH. Neural basis of implicit motor sequence learning: Modulation of cortical power. Psychophysiology. 2023;60(2):e14179. [DOI:10.1111/psyp.14179]
11. Min B-K, Hämäläinen MS, Pantazis D. New cognitive neurotechnology facilitates studies of cortical-subcortical interactions. Trends in biotechnology. 2020;38(9):952-62. [DOI:10.1016/j.tibtech.2020.03.003]
12. Graham M. Geography / internet: ethereal alternate dimensions of cyberspace or grounded augmented realities? The Geographical Journal. 2013;179(2):177-82. [DOI:10.1111/geoj.12009]
13. Statista. Internet and social media users in the world 2023. Available from: https://wwwstatistacom/statistics/617136/digital-population-worldwide/.
14. Young KS. Internet addiction: The emergence of a new clinical disorder. Cyberpsychology & behavior. 2009;1(3). [DOI:10.1089/cpb.1998.1.237]
15. Marciano L, Camerini A-L, Schulz PJ. Neuroticism and internet addiction: What is next? A systematic conceptual review. Personality and individual differences. 2022;185:111260. [DOI:10.1016/j.paid.2021.111260]
16. Tang ACY, Lee RLT. Effects of a group mindfulness-based cognitive programme on smartphone addictive symptoms and resilience among adolescents: study protocol of a cluster-randomized controlled trial. BMC nursing. 2021;20(1):86. [DOI:10.1186/s12912-021-00611-5]
17. Dehyadegari L, Khajehasani S. The impact of using social networks on students' learning in Sirjan Uuniversity of Technology. Technology of Education Journal (TEJ). 2020;14(3):583-90. [DOI:10.22061/jte.2019.4521.2081]
18. Seo HS, Jeong E-K, Choi S, Kwon Y, Park H-J, Kim I. Changes of neurotransmitters in youth with internet and smartphone addiction: A comparison with healthy controls and changes after cognitive behavioral therapy. American Journal of Neuroradiology. 2020;41(7):1293-301. [DOI:10.3174/ajnr.A6632]
19. Cao F, Su L, Liu T, Gao X. The relationship between impulsivity and Internet addiction in a sample of Chinese adolescents. European Psychiatry. 2007; 22(7):466-71. [DOI:10.1016/j.eurpsy.2007.05.004]
20. Pan N, Yang Y, Du X, Qi X, Du G, Zhang Y, et al. Brain structures associated with internet addiction tendency in adolescent online game players. Frontiers in psychiatry. 2018:67. [DOI:10.3389/fpsyt.2018.00067]
21. Sariyska R, Lachmann B, Markett S, Reuter M, Montag C. Individual differences in implicit learning abilities and impulsive behavior in the context of Internet addiction and Internet Gaming Disorder under the consideration of gender. Addictive behaviors reports. 2017;5:19-28. [DOI:10.1016/j.abrep.2017.02.002]
22. Zhou Y, Lin F-c, Du Y-s, Zhao Z-m, Xu J-R, Lei H. Gray matter abnormalities in Internet addiction: a voxel-based morphometry study. European journal of radiology. 2011;79(1):92-5. [DOI:10.1016/j.ejrad.2009.10.025]
23. Faraci P, Craparo G, Messina R, Severino S. Internet Addiction Test (IAT): which is the best factorial solution? Journal of medical Internet research. 2013;15(10):e2935. [DOI:10.2196/jmir.2935]
24. Keser H, Eşgi N, Kocadağ T, Bulu Ş. Validity and Reliability Study of the Internet Addiction Test. Mevlana International Journal of Education. 2013;3(4). [DOI:10.13054/mije.13.51.3.4]
25. Ghasemzadeh L, Shahraray M, Moradi A. The study of degree of prevalence to internet addiction and its relation with loneliness and self esteem in high schools students of Tehran. J Educ. 2007;1(89):41-68. [DOI:10.1089/cpb.2007.0243]
26. Bahri N, Sadegh ML, Khodadost L, Mohammadzadeh J, Banafsheh E. Internet Addict Ion Status And Its Relation With Students General Health At Gonabad Medical University. Modern Care Journal. 2011; 8(3):166-73. Available from: https://sid.ir/paper/206010/en
27. Jongbloed-Pereboom M, Nijhuis-van Der Sanden M, Steenbergen B. Explicit and implicit motor sequence learning in children and adults; the role of age and visual working memory. Human movement science. 2019;64:1-11. [DOI:10.1016/j.humov.2018.12.007]
28. Salehi SK, Moradi A. The Effect of Early Instruction on Performance and Retention of Motor Sequence Task: Evidence for Sensitive Period in Motor Learning. Journal of Applied Psychological Research. 2020;11(3):133-52.
30. Roodenrys S, Dunn N. Unimpaired implicit learning in children with developmental dyslexia. Dyslexia. 2008;14(1):1-15. [DOI:10.1002/dys.340]
31. Savion-Lemieux T, Bailey JA, Penhune VB. Developmental contributions to motor sequence learning. Experimental brain research. 2009;195:293-306. [DOI:10.1007/s00221-009-1786-5]
32. Salehi SK, Sheikh M, Hemayattala R, Humaneyan D. The effect of different ages levels and explicit-implicit knowledge on motor sequence learning. International Journal of Environmental & Science Education. 2016 ;11(18) :13157-65. Available from:
33. https://api.semanticscholar.org/CorpusID:21742114
34. Ko C-H, Yen J-Y, Chen S-H, Yang M-J, Lin H-C, Yen C-F. Proposed diagnostic criteria and the screening and diagnosing tool of Internet addiction in college students. Comprehensive psychiatry. 2009;50(4):378-84. [DOI:10.1016/j.comppsych.2007.05.019]
36. Yildiz MA. Emotion regulation strategies as predictors of internet addiction and smartphone addiction in adolescents. Journal of Educational Sciences and Psychology. 2017;7(1). Available from: https://sciencescholar.us/journal/index.php/ijhs/article/view/13629
37. Sun D-L, Chen Z-J, Ma N, Zhang X-C, Fu X-M, Zhang D-R. Decision-making and prepotent response inhibition functions in excessive internet users. CNS spectrums. 2009;14(2):75-81. https://doi.org/10.1017/S1092852900000225 [DOI:10.1017/s1092852900000225]
38. Yao Y-W, Chen P-R, Chen C, Wang L-J, Zhang J-T, Xue G, et al. Failure to utilize feedback causes decision-making deficits among excessive Internet gamers. Psychiatry research. 2014;219(3):583-8. [DOI:10.1016/j.psychres.2014.06.033]
39. Brand M, Young KS, Laier C, Wölfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neuroscience & Biobehavioral Reviews. 2016;71:252-66. [DOI:10.1016/j.neubiorev.2016.08.033]

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