OpenLB
Open Library of Bioscience
Advanced Search
PsyCh journal
Aug, 2021;10 (4): 566-573.

Neural correlates of improved inductive reasoning ability in abacus-trained children: A resting state fMRI study.

Xiuqin Jia, Yi Zhang, Yuzhao Yao, Feiyan Chen, Peipeng Liang
Author Information
  1. Xiuqin Jia: School of Psychology, Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China. ORCID
  2. Yi Zhang: Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, China.
  3. Yuzhao Yao: Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, China.
  4. Feiyan Chen: Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, China.
  5. Peipeng Liang: School of Psychology, Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China.
PMID: 33709543 DOI: 10.1002/pchj.439

Abstract

Abacus-based mental calculation (AMC) training may improve mathematics-related abilities and transfer to other cognitive domains. Thus, it was hypothesized that inductive reasoning abilities can be improved by AMC training given the overlapping cognitive processes and neural correlates between AMC and inductive reasoning. The aim of the current study was to examine the underlying neurobiological mechanisms of this possible adaption by resting-state functional magnetic resonance imaging (rs-fMRI). Sixty-three children were randomly assigned to either the AMC-trained or the nontrained group. The AMC-trained group was required to perform abacus training for 2 hours per week for 5 years whereas the nontrained group was not required to perform any abacus training. Each participant's rs-fMRI data were collected after abacus training, and regional homogeneity (ReHo) analysis was performed to determine the neural activity differences between groups. The participants' posttraining mathematical ability, intelligence quotients, and inductive reasoning ability were recorded and evaluated. The results revealed that AMC-trained children exhibited a significantly higher mathematical ability and inductive reasoning performance and higher ReHo in the rostrolateral prefrontal cortex (RLPFC) compared to the nontrained group. In particular, the increased ReHo in the RLPFC was found to be positively correlated with improved inductive reasoning performance. Our findings suggest that rs-fMRI may reflect the modulation of training in task-related networks.

Keywords

abacus-based mental calculation inductive reasoning number-series completion regional homogeneity rostrolateral prefrontal cortex (RLPFC)

References

  1. Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven progressive matrices test. Psychological Review, 97, 404-431. https://doi.org/10.1037/0033-295x.97.3.404
  2. Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1-22. https://doi.org/10.1037/h0046743
  3. Chen, F., Hu, Z., Zhao, X., Wang, R., Yang, Z., Wang, X., & Tang, X. (2006). Neural correlates of serial abacus mental calculation in children: A functional MRI study. Neuroscience Letters, 403, 46-51. https://doi.org/10.1016/j.neulet.2006.04.041
  4. Christoff, K., & Gabrieli, J. D. E. (2000). The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychobiology, 28(2), 168-186. https://doi.org/10.3758/BF03331976
  5. Christoff, K., Prabhakaran, V., Dorfman, J., Zhao, Z., Kroger, J. K., Holyoak, K. J., & Gabrieli, J. D. (2001). Rostrolateral prefrontal cortex involvement in relational integration during reasoning. NeuroImage, 14, 1136-1149. https://doi.org/10.1006/nimg.2001.0922
  6. Christoff, K., Ream, J. M., & Gabrieli, J. D. E. (2004). Neural basis of spontaneous thought processes. Cortex, 40(4-5), 623-630. https://doi.org/10.1016/s0010-9452(08)70158-8
  7. Crone, E. A., Wendelken, C., van Leijenhorst, L., Honomichl, R. D., Christoff, K., & Bunge, S. A. (2009). Neurocognitive development of relational reasoning. Developmental Science, 12, 55-66. https://doi.org/10.1111/j.1467-7687.2008.00743.x
  8. Dahlin, E., Neely, A. S., Larsson, A., Backman, L., & Nyberg, L. (2008). Transfer of learning after updating training mediated by the striatum. Science, 320, 1510-1512. https://doi.org/10.1126/science.1155466
  9. Dong, S., Wang, C., Xie, Y., Hu, Y., Weng, J., & Chen, F. (2016). The impact of abacus training on working memory and underlying neural correlates in young adults. Neuroscience, 332, 181-190. https://doi.org/10.1016/j.neuroscience.2016.06.051
  10. Frank, M. C., & Barner, D. (2012). Representing exact number visually using mental abacus. Journal of Experimental Psychology: General, 141(1), 134-149. https://doi.org/10.1037/a0024427
  11. Girelli, L., Semenza, C., & Delazer, M. (2004). Inductive reasoning and implicit memory: Evidence from intact and impaired memory systems. Neuropsychologia, 42(7), 926-938. https://doi.org/10.1016/j.neuropsychologia.2003.11.016
  12. Haffner, J., Baro, K., Parzer, P., & Resch, F. (2005). HRT 1-4 - Heidelberger Rechentest - Erfassung Mathematischer Basiskompetenzen im Grundschulalter. Gottingen, Germany: Hogrege. (in German).
  13. Hanakawa, T., Honda, M., Okada, T., Fukuyama, H., & Shibasaki, H. (2003). Neural correlates underlying mental calculation in abacus experts: A functional magnetic resonance imaging study. NeuroImage, 19, 296-307. https://doi.org/10.1016/s1053-8119(03)00050-8
  14. Hatano, G., Miyake, Y., & Binks, M. G. (1977). Performance of expert abacus operators. Cognition, 5(1), 47-55. https://doi.org/10.1016/0010-0277(77)90016-6
  15. Horowitz-kraus, T., Difrancesco, M., Kay, B., Wang, Y., & Holland, S. K. (2015). Increased resting-state functional connectivity of visual- and cognitive-control brain networks after training in children with reading difficulties. NeuroImage: Clinical, 8, 619-630. https://doi.org/10.1016/j.nicl.2015.06.010
  16. Hu, Y., Geng, F., Tao, L., Hu, N., Du, F., Fu, K., & Chen, F. (2011). Enhanced white matter tracts integrity in children with abacus training. Human Brain Mapping, 32, 10-21. https://doi.org/10.1002/hbm.20996
  17. Irwing, P., Hamza, A., Khaleefa, O., & Lynn, R. (2008). Effects of Abacus training on the intelligence of Sudanese children. Personality and Individual Differences, 45, 694-696. https://doi.org/10.1016/j.paid.2008.06.011
  18. Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Shah, P. (2011). Short- and long-term benefits of cognitive training. Proceedings of the National Academy of Sciences of the United States of America, 108, 10081-10086. https://doi.org/10.1073/pnas.1103228108
  19. Jia, X., Liang, P., Lu, J., Yang, Y., Zhong, N., & Li, K. (2011). Common and dissociable neural correlates associated with component processes of inductive reasoning. NeuroImage, 56, 2292-2299. https://doi.org/10.1016/j.neuroimage.2011.03.020
  20. Jia, X., Liang, P., Shi, L., Wang, D., & Li, K. (2015). Prefrontal and parietal activity is modulated by the rule complexity of inductive reasoning and can be predicted by a cognitive model. Neuropsychologia, 66, 67-74. https://doi.org/10.1016/j.neuropsychologia.2014.10.015
  21. Klingberg, T. (2010). Training and plasticity of working memory. Trends in Cognitive Sciences, 14(7), 317-324. https://doi.org/10.1016/j.tics.2010.05.002
  22. Li, F., Cao, B., Luo, Y., Lei, Y., & Li, H. (2013). Functional imaging of brain responses to different outcomes of hypothesis testing: Revealed in a category induction task. NeuroImage, 66, 368-375. https://doi.org/10.1016/j.neuroimage.2012.10.031
  23. Li, Y., Hu, Y., Zhao, M., Wang, Y., Huang, J., & Chen, F. (2013). The neural pathway underlying a numerical working memory task in abacus-trained children and associated functional connectivity in the resting brain. Brain Research, 1539, 24-33. https://doi.org/10.1016/j.brainres.2013.09.030
  24. Liang, P., Jia, X., Taatgeen, N. A., Zhong, N., & Li, K. (2014). Different strategies in solving series completion inductive reasoning problems: An fMRI and computational study. International Journal of Psychophysiology, 93, 253-260. https://doi.org/10.1016/j.ijpsycho.2014.05.006
  25. Liang, P., Jia, X., Taatgen, N. A., Borst, J. P., & Li, K. (2016). Activity in the fronto-parietal network indicates numerical inductive reasoning beyond calculation: An fMRI study combined with a cognitive model. Scientific Reports, 6, 25976. https://doi.org/10.1038/srep25976
  26. Na, K. S., Lee, S., Park, J. H., Jung, H. Y., & Ryu, J. H. (2015). Association between abacus training and improvement in response inhibition: A case-control study. Clinical Psychopharmacology and Neuroscience, 13, 163-167. https://doi.org/10.9758/cpn.2015.13.2.163
  27. Schlaffke, L., Schweizer, L., Ruther, N. N., Luerding, R., Tegenthoff, M., Bellebaum, C., & Schmidt-Wilcke, T. (2017). Dynamic changes of resting state connectivity related to the acquisition of a lexico-semantic skill. NeuroImage, 146, 429-437. https://doi.org/10.1016/j.neuroimage.2016.08.065
  28. Simon, H., & Kotovsky, K. (1963). Human acquisition of concepts for sequential patterns. Psychological Review, 70(6), 534-546. https://doi.org/10.1037/h0043901
  29. Stigler, J. W. (1984). “Mental abacus”: The effect of abacus training on Chinese children's mental calculation. Cognitive Psychology, 16(2), 145-176. https://doi.org/10.1016/0010-0285(84)90006-9
  30. Tanaka, S., Seki, K., Hanakawa, T., Harada, M., Sugawara, S. K., Sadato, N., … Honda, M. (2012). Abacus in the brain: A longitudinal functional MRI study of a skilled abacus user with a right hemispheric lesion. Frontiers in Psychology, 3, 315. https://doi.org/10.3389/fpsyg.2012.00315
  31. Wang, C., Geng, F., Yao, Y., Weng, J., Hu, Y., & Chen, F. (2015). Abacus training affects math and task switching abilities and modulates their relationships in Chinese children. PLoS One, 10, e0139930. https://doi.org/10.1371/journal.pone.0139930
  32. Wang, C., Weng, J., Yao, Y., Dong, S., Liu, Y., & Chen, F. (2017). Effect of abacus training on executive function development and underlying neural correlates in Chinese children. Human Brain Mapping, 38, 5234-5249. https://doi.org/10.1002/hbm.23728
  33. Wang, C., Xu, T., Geng, F., Hu, Y., Wang, Y., Liu, H., & Chen, F. (2019). Training on abacus-based mental calculation enhances visuospatial working memory in children. Journal of Neuroscience, 39, 6439-6448. https://doi.org/10.1523/JNEUROSCI.3195-18.2019
  34. Weng, J., Xie, Y., Wang, C., & Chen, F. (2017). The effects of long-term abacus training on topological properties of brain functional networks. Scientific Reports, 7, 8862. https://doi.org/10.1038/s41598-017-08955-2
  35. Wu, H. R., & Li, L. (2006). Norm establishment for Chinese rating scale of pupil's mathematics abilities. Chinese Journal of Clinical Rehabilitation, 10(30), 168-171.(in Chinese).
  36. Wu, T. H., Chen, C. L., Huang, Y. H., Liu, R. S., Hsieh, J. C., & Lee, J. J. S. (2009). Effects of long-term practice and task complexity on brain activities when performing abacus-based mental calculations: A PET study. European Journal of Nuclear Medicine and Molecular Imaging, 36, 436-445. https://doi.org/10.1007/s00259-008-0949-0
  37. Xiao, F., Li, P., Long, C., Lei, Y., & Li, H. (2014). Relational complexity modulates activity in the prefrontal cortex during numerical inductive reasoning: An fMRI study. Biological Psychology, 101, 61-68. https://doi.org/10.1016/j.biopsycho.2014.06.005
  38. Xie, Y., Weng, J., Wang, C., Xu, T., Peng, X., & Chen, F. (2018). The impact of long-term abacus training on modular properties of functional brain network. NeuroImage, 183, 811-817. https://doi.org/10.1016/j.neuroimage.2018.08.057
  39. Yan, C., Wang, X., Zuo, X., & Zang, Y. (2016). DPABI: Data Processing & Analysis for (resting-state) Brain Imaging. Neuroinformatics, 14, 339-351. https://doi.org/10.1007/s12021-016-9299-4
  40. Yang, Y., Liang, P., Lu, S., Li, K., & Zhong, N. (2009). The role of the DLPFC in inductive reasoning of MCI patients and normal aging: An fMRI study. Science China Life Sciences, 52, 789-795. https://doi.org/10.1007/s11427-009-0089-1
  41. Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. NeuroImage, 22, 394-400. https://doi.org/10.1016/j.neuroimage.2003.12.030
  42. Zhang, H., & Wang, X. (1985). The Chinese Version of the Raven's Standard Progressive Matrices. Beijing: Beijing Normal University.
  43. Zhong, N., Liang, P., Qin, Y., Lu, S., Yang, Y., & Li, K. (2011). Neural substrates of data-driven scientific discovery: An fMRI study during performance of number series completion task. Science China Life Sciences, 54, 466-473. https://doi.org/10.1007/s11427-011-4166-x
  44. Zhou, H., Geng, F., Wang, Y., Wang, C., Hu, Y., & Chen, F. (2019). Transfer effects of abacus training on transient and sustained brain activation in the frontal-parietal network. Neuroscience, 408, 135-146. https://doi.org/10.1016/j.neuroscience.2019.04.001

Grants

  1. 2016000021223TD07/Beijing Nova Program
  2. 17ZDA323/National Social Science Foundation
  3. 31270026/National Natural Science Foundation of China
  4. 62076169/National Natural Science Foundation of China
  5. 2020YFC2007300/National Key Research and Development Project of China
  6. 2020YFC2007302/National Key Research and Development Project of China
  7. /the Beijing Brain Initiative of Beijing Municipal Science & Technology Commission, and Academy for Multidisciplinary Studies, Capital Normal University

MeSH Term

Brain
Brain Mapping
Child
Humans
Magnetic Resonance Imaging
Problem Solving
Journal Article Randomized Controlled Trial
Article has an altmetric score of 1

Word Cloud

Created with Highcharts 10.0.0inductivereasoningtraininggroupabilityAMCimprovedrs-fMRIAMC-trainednontrainedabacusReHoRLPFCmentalcalculationmayabilitiescognitiveneuralcorrelatesstudychildrenrequiredperformregionalhomogeneitymathematicalhigherperformancerostrolateralprefrontalcortexAbacus-basedimprovemathematics-relatedtransferdomainsThushypothesizedcangivenoverlappingprocessesaimcurrentexamineunderlyingneurobiologicalmechanismspossibleadaptionresting-statefunctionalmagneticresonanceimagingSixty-threerandomlyassignedeither2 hoursperweek5yearswhereasparticipant'sdatacollectedanalysisperformeddetermineactivitydifferencesgroupsparticipants'posttrainingintelligencequotientsrecordedevaluatedresultsrevealedexhibitedsignificantlycomparedparticularincreasedfoundpositivelycorrelatedfindingssuggestreflectmodulationtask-relatednetworksNeuralabacus-trainedchildren:restingstatefMRIabacus-basednumber-seriescompletion

Similar Articles

Cited By