删除或更新信息,请邮件至freekaoyan#163.com(#换成@)

数学焦虑个体近似数量加工的神经机制:一项EEG研究

本站小编 Free考研考试/2022-01-01

刘洁1,2,3, 李瑾琪1,2, 申超然4, 胡小惠1,2, 赵庭浩5, 关青1,2, 罗跃嘉1,2,3()
1深圳大学脑疾病与认知科学研究中心
2深圳大学心理与社会学院
3深圳市神经科学研究院, 深圳 518060
4长治学院教育学院, 山西 长治 046000
5美国凯斯西储大学认知科学系, 俄亥俄州克利夫兰
收稿日期:2019-09-12出版日期:2020-08-25发布日期:2020-06-28
通讯作者:罗跃嘉E-mail:luoyj@szu.edu.cn

基金资助:* 国家自然科学基金项目(31900779);国家自然科学基金项目(31871109);深港脑科学创新研究院资助(2019SHIBS000)

The neural mechanism of approximate number processing for mathematical anxious individuals: An EEG study

LIU Jie1,2,3, LI Jinqi1,2, SHEN Chaoran4, HU Xiaohui1,2, ZHAO Tinghao5, GUAN Qing1,2, LUO Yuejia1,2,3()
1Center for Brain Disorders and Cognitive Neuroscience, Shenzhen University, Shenzhen 518060, China
2School of Psychology, Shenzhen University, Shenzhen 518060, China
3Shenzhen Institute of Neuroscience, Shenzhen 518060, China
4Department of Education, Changzhi University, Changzhi 046000, China
5Department of Cognitive Science, Case Western Reserve University, Cleveland, Ohio, USA
Received:2019-09-12Online:2020-08-25Published:2020-06-28
Contact:LUO Yuejia E-mail:luoyj@szu.edu.cn






摘要/Abstract


摘要: 近似数量加工是对大数目物体数量在不依赖逐个数数前提下的估计。行为学研究提示高数学焦虑人群近似数量加工能力下降, 但神经机制未明。本研究探讨高数学焦虑个体近似数量加工的神经机制, 比较高低数学焦虑脑电活动的差异:(1)行为上无显著组间差异; (2)高数学焦虑组的P2p成分波幅增加; (3) δ频段ERS及β频段ERD无显著数量比例效应, 而低数学焦虑组在上述指标的数量比例效应显著。本研究为高数学焦虑人群近似数量加工能力下降提供了电生理学的证据。


表1高低数学焦虑组的年龄、性别、数学焦虑、一般焦虑、视觉加工速度、视觉注意广度及的平均得分及标准差
组别 年龄(岁) 性别(男/女) 数学焦虑 一般焦虑 视觉加工速度 视觉注意广度 智力
高数学焦虑
(High math anxious, HMA)
20.68 (1.62) 15/16 94.35 (7.37) 44.70 (5.35) 74.06 (17.24) 58.71 (18.78) 5.03 (1.38)
低数学焦虑
(Low math anxious, LMA)
20.69 (1.85) 17/12 41.86 (6.56) 44.45 (4.84) 75.86 (16.19) 63.96 (18.91) 5.14 (0.88)
t -- -- 29.07 0.2 -0.42 -1.08 -0.35
p -- -- <.001 0.84 0.68 0.28 0.72

表1高低数学焦虑组的年龄、性别、数学焦虑、一般焦虑、视觉加工速度、视觉注意广度及的平均得分及标准差
组别 年龄(岁) 性别(男/女) 数学焦虑 一般焦虑 视觉加工速度 视觉注意广度 智力
高数学焦虑
(High math anxious, HMA)
20.68 (1.62) 15/16 94.35 (7.37) 44.70 (5.35) 74.06 (17.24) 58.71 (18.78) 5.03 (1.38)
低数学焦虑
(Low math anxious, LMA)
20.69 (1.85) 17/12 41.86 (6.56) 44.45 (4.84) 75.86 (16.19) 63.96 (18.91) 5.14 (0.88)
t -- -- 29.07 0.2 -0.42 -1.08 -0.35
p -- -- <.001 0.84 0.68 0.28 0.72



图1各项认知测验任务示意图
图1各项认知测验任务示意图



图2两种任务的刺激示例
图2两种任务的刺激示例


表2各条件下不同组别的平均准确率和反应时
指标 组别 主动-大比例 主动小比例 被动-大比例 被动-小比例
ACC HMA 0.91 (0.04) 0.75 (0.05) 0.80 (0.07) 0.78 (0.07)
LMA 0.92 (0.05) 0.76 (0.06) 0.78 (0.07) 0.74 (0.07)
RT HMA 620 (112) 736 (132) 619 (116) 648 (110)
LMA 622 (120) 752 (133) 642 (103) 662 (101)

表2各条件下不同组别的平均准确率和反应时
指标 组别 主动-大比例 主动小比例 被动-大比例 被动-小比例
ACC HMA 0.91 (0.04) 0.75 (0.05) 0.80 (0.07) 0.78 (0.07)
LMA 0.92 (0.05) 0.76 (0.06) 0.78 (0.07) 0.74 (0.07)
RT HMA 620 (112) 736 (132) 619 (116) 648 (110)
LMA 622 (120) 752 (133) 642 (103) 662 (101)



图3图中的折线图代表不同条件下行为成绩的均值
图3图中的折线图代表不同条件下行为成绩的均值



图4P2p成分的地形图和波形图
图4P2p成分的地形图和波形图



图5图中标记在P5, PO7, O1和Oz电极的ROI (1~5 Hz, 83~217 ms)上的平均时频分布图、地形图及统计图
图5图中标记在P5, PO7, O1和Oz电极的ROI (1~5 Hz, 83~217 ms)上的平均时频分布图、地形图及统计图



图6图中标记在P5和PO7电极(29~34 Hz, 206~285 ms)上的平均时频分布图、地形图及统计图
图6图中标记在P5和PO7电极(29~34 Hz, 206~285 ms)上的平均时频分布图、地形图及统计图







[1] Alexander, L., & Martray, C. (1989). The development of an abbreviated version of the Mathematics Anxiety Rating Scale. Measurement and Evaluation in Counseling and Development, 22(3), 143-150.
[2] Ashcraft, M. H. (2002). Math anxiety: Personal, educational, and cognitive consequences. Current Directions in Psychological Science, 11 (5), 181-185.
[3] Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130(2), 224-237.
[4] Ashcraft, M. H., & Krause, J. A. (2007). Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review, 14(2), 243-248.
doi: 10.3758/bf03194059URLpmid: 17694908
[5] Bates, M. E., & Lemay, E. P. (2004). The d2 test of attention: construct validity and extensions in scoring techniques. Journal of the International Neuropsychological Society, 10(3), 392-400.
URLpmid: 15147597
[6] Brannon, E. M. (2006). The representation of numerical magnitude. Current Opinion in Neurobiology, 16(2), 222-229.
doi: 10.1016/j.conb.2006.03.002URLpmid: 16546373
[7] Brannon, E. M., Jordan, K. E., & Jones, S. M., (2010). Behavioral signatures of numerical cognition. In M. L. Platt, A. A. Ghazanfa (Eds.).Primate neuroethology (pp.144-159), Oxford University Press.
[8] Carey, E., Hill, F., Devine, A., & Szücs, D. (2016). The chicken or the egg? The direction of the relationship between mathematics anxiety and mathematics performance. Frontiers in Psychology, 6, 1987.
doi: 10.3389/fpsyg.2015.01987URLpmid: 26779093
[9] Cohen, M. X. (2014). Analyzing neural time series data: Theory and practice. Cambrige, Massachusetts, the MIT press.
[10] Colomé, à. (2019). Representation of numerical magnitude in math-anxious individuals. Quarterly Journal of Experimental Psychology, 72(3), 424-435.
[11] Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21.
doi: 10.1016/j.jneumeth.2003.10.009URLpmid: 15102499
[12] Dietrich, J. F., Huber, S., Moeller, K., & Klein, E. (2015). The influence of math anxiety on symbolic and non-symbolic magnitude processing. Frontiers in Psychology, 6, 1621.
doi: 10.3389/fpsyg.2015.01621URLpmid: 26579012
[13] Di Russo, F., Martínez, A., Sereno, M. I., Pitzalis, S., & Hillyard, S. A. (2002). Cortical sources of the early components of the visual evoked potential. Human Brain Mapping, 15 (2), 95-111.
doi: 10.1002/hbm.10010URLpmid: 11835601
[14] Ekstrom, R. B., Dermen, D., & Harman, H. H. (1976). Manual for kit of factor-referenced cognitive tests (Vol. 102). Princeton, NJ: Educational Testing Service.
[15] Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: Oscillations and synchrony in top-down processing. Nature Reviews Neuroscience, 2 (10), 704-716.
doi: 10.1038/35094565URLpmid: 11584308
[16] Engel, A. K., & Fries, P. (2010). Beta-band oscillations-signalling the status quo? Current Opinion in Neurobiology, 20(2), 156-165.
doi: 10.1016/j.conb.2010.02.015URLpmid: 20359884
[17] Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191.
doi: 10.3758/BF03193146URL
[18] Fornaciai, M., Brannon, E. M., Woldorff, M. G., & Park, J. (2017). Numerosity processing in early visual cortex. NeuroImage, 157, 429-438.
doi: 10.1016/j.neuroimage.2017.05.069URLpmid: 28583882
[19] Halberda, J., Mazzocco, M. M., & Feigenson, L. (2008). Individual differences in non-verbal number acuity correlate with maths achievement. Nature, 455(7213), 665-668.
doi: 10.1038/nature07246URLpmid: 18776888
[20] Hauser, M. D., Tsao, F., Garcia, P., & Spelke, E. S. (2003). Evolutionary foundations of number: spontaneous representation of numerical magnitudes by cotton-top tamarins. Proceedings of the Royal Society of London B: Biological Sciences, 270(1523), 1441-1446.
[21] Hyde, D. C., & Spelke, E. S. (2009). All numbers are not equal: an electrophysiological investigation of small and large number representations. Journal of Cognitive Neuroscience, 21(6), 1039-1053.
doi: 10.1162/jocn.2009.21090URLpmid: 18752403
[22] Hyde, D. C., & Spelke, E. S. (2012). Spatiotemporal dynamics of processing nonsymbolic number: An event‐related potential source localization study. Human Brain Mapping, 33(9), 2189-2203.
doi: 10.1002/hbm.21352URL
[23] Hyde, D. C., & Wood, J. N. (2011). Spatial attention determines the nature of nonverbal number representation. Journal of Cognitive Neuroscience, 23 (9), 2336-2351.
doi: 10.1162/jocn.2010.21581URLpmid: 20961170
[24] Izard, V., Sann, C., Spelke, E. S., & Streri, A. (2009). Newborn infants perceive abstract numbers. Proceedings of the National Academy of Sciences, 106(25), 10382-10385.
[25] Libertus, M. E., & Brannon, E. M. (2009). Behavioral and neural basis of number sense in infancy. Current Directions in Psychological Science, 18(6), 346-351.
doi: 10.1111/j.1467-8721.2009.01665.xURLpmid: 20419075
[26] Libertus, M. E., Woldorff, M. G., & Brannon, E. M. (2007). Electrophysiological evidence for notation independence in numerical processing. Behavioral and Brain Functions, 3(1), 1.
[27] Lindskog, M., Winman, A., & Poom, L. (2017). Individual differences in nonverbal number skills predict math anxiety. Cognition, 159, 156-162.
URLpmid: 27960118
[28] Liu, J., Li, J., Peng, W., Feng, M., & Luo, Y. (2019). EEG correlates of math anxiety during arithmetic problem solving: Implication for attention deficits. Neuroscience Letters, 703, 191-197.
doi: 10.1016/j.neulet.2019.03.047URLpmid: 30928479
[29] Lyons, I. M., & Beilock, S. L. (2011). Mathematics anxiety: separating the math from the anxiety. Cerebral Cortex, 22 (9), 2102-2110.
doi: 10.1093/cercor/bhr289URLpmid: 22016480
[30] Maloney, E. A., Ansari, D., & Fugelsang, J. A. (2011). Rapid communication: the effect of mathematics anxiety on the processing of numerical magnitude. Quarterly Journal of Experimental Psychology, 64(1), 10-16.
[31] Maloney, E. A., Risko, E. F., Ansari, D., & Fugelsang, J. (2010). Mathematics anxiety affects counting but not subitizing during visual enumeration. Cognition, 114 (2), 293-297.
doi: 10.1016/j.cognition.2009.09.013URLpmid: 19896124
[32] Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG and MEG data. Journal of Neuroscience Methods, 164 (1), 177-190.
doi: 10.1016/j.jneumeth.2007.03.024URLpmid: 17517438
[33] Nú?ez-Pe?a, M. I., & Suárez-Pellicioni, M. (2014). Less precise representation of numerical magnitude in high math-anxious individuals: An ERP study of the size and distance effects. Biological Psychology, 103, 1767-183.
[34] Nú?ez-Pe?a, M. I., & Suárez-Pellicioni, M. (2015). Processing of multi-digit additions in high math-anxious individuals: psychophysiological evidence. Frontiers in Psychology, 6, 1268.
doi: 10.3389/fpsyg.2015.01268URLpmid: 26347705
[35] OECD, . (2013). PISA 2012 Results: Ready to learn: Students' engagement drive and self-beliefs (Volume III). PISA, OECD Publishing.
[36] Park, J. (2018). A neural basis for the visual sense of number and its development: A steady-state visual evoked potential study in children and adults. Developmental Cognitive Neuroscience, 30, 333-343.
doi: 10.1016/j.dcn.2017.02.011URLpmid: 28342780
[37] Park, J., DeWind, N. K., Woldorff, M. G., & Brannon, E. M. (2015). Rapid and direct encoding of numerosity in the visual stream. Cerebral Cortex, 26(2), 748-763.
doi: 10.1093/cercor/bhv017URLpmid: 25715283
[38] Raven, J., Raven, J. C., & Court, J. H. (1998). Manual for Raven's Progressive Matrices and Vocabulary Scales. Section 3, The Standard Progressive Matrices. Oxford, England: Oxford Psychologists Press/San Antonio, TX: The Psychological Corporation.
[39] Sorvo, R., Koponen, T., Viholainen, H., Aro, T., R?ikk?nen, E., Peura, P., … Aro, M. (2019). Development of math anxiety and its longitudinal relationships with arithmetic achievement among primary school children. Learning and Individual Differences, 69, 173-181.
[40] Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983) Manual for the state-trait anxiety scale. CA: Consulting Psychologists Press.
[41] Sullivan, J., Frank, M. C., & Barner, D. (2016). Intensive math training does not affect approximate number acuity: Evidence from a three-year longitudinal curriculum intervention. Journal of Numerical Cognition, 2 (2), 57-76.
[42] Wang, Z., Hart, S. A., Kovas, Y., Lukowski, S., Soden, B., Thompson, L. A., … Petrill, S. A. (2014). Who is afraid of math? Two sources of genetic variance for mathematical anxiety. Journal of Child Psychology and Psychiatry, 55 (9), 1056-1064.
doi: 10.1111/jcpp.12224URLpmid: 24611799
[43] Xu, F., Spelke, E. S., & Goddard, S. (2005). Number sense in human infants. Developmental Science, 8(1), 88-101.
doi: 10.1111/j.1467-7687.2005.00395.xURLpmid: 15647069
[44] Young, C. B., Wu, S. S., & Menon, V. (2012). The neurodevelopmental basis of math anxiety. Psychological Science, 23(5), 492-501.
doi: 10.1177/0956797611429134URLpmid: 22434239
[45] Zhang, Z. G., Hu, L., Hung, Y. S., Mouraux, A., & Iannetti, G. D. (2012). Gamma-band oscillations in the primary somatosensory cortex—a direct and obligatory correlate of subjective pain intensity. Journal of Neuroscience, 32 (22), 7429-7438.
doi: 10.1523/JNEUROSCI.5877-11.2012URLpmid: 22649223
[46] Zhou, X., Wei, W., Zhang, Y., Cui, J., & Chen, C. (2015). Visual perception can account for the close relation between numerosity processing and computational fluency. Frontiers in Psychology, 6, 1364.
doi: 10.3389/fpsyg.2015.01364URLpmid: 26441740




[1]侯璐璐, 陈莅蓉, 周仁来. 经前期综合征与奖赏进程失调——来自脑电的证据[J]. 心理学报, 2020, 52(6): 742-757.
[2]蒋宇宸, 蔡笑, 张清芳. θ频段(4~8 Hz)的活动反映了汉语口语产生中音节信息的加工[J]. 心理学报, 2020, 52(10): 1199-1211.
[3]卢伟, 黄尔齐, 原晋霞. 基于定量脑电图的音乐和灯光颜色对情绪的影响 *[J]. 心理学报, 2018, 50(8): 880-891.
[4]付超, 张振, 何金洲, 黄四林, 仇剑崟, 王益文. 普遍信任博弈决策的动态过程 ——来自脑电时频分析的证据[J]. 心理学报, 2018, 50(3): 317-326.
[5]谷传华;王亚丽;吴财付;谢祥龙;崔承珠;王亚娴;王婉贞;胡碧颖;周宗奎. 社会创造性的脑机制:状态与特质的EEG α波活动特点[J]. 心理学报, 2015, 47(6): 765-773.
[6]夏瑞雪;周爱保;李世峰;徐科朋;任德云;朱婧. 观点采择在内隐情绪加工中的调节作用[J]. 心理学报, 2014, 46(8): 1094-1102.
[7]修利超;谈诚;蒋依涵;周仁来;陈善广. 45天-6°头低位卧床对个体额区EEG偏侧化和情绪的影响[J]. 心理学报, 2014, 46(7): 942-950.
[8]司继伟;徐艳丽;封洪敏;许晓华;周超. 不同数学焦虑成人的算术策略运用差异:ERP研究[J]. 心理学报, 2014, 46(12): 1835-1849.





PDF全文下载地址:

http://journal.psych.ac.cn/xlxb/CN/article/downloadArticleFile.do?attachType=PDF&id=4777
相关话题/数学 视觉 心理 比例 成绩