心理与行为研究 ›› 2024, Vol. 22 ›› Issue (2): 258-265.DOI: 10.12139/j.1672-0628.2024.02.015
收稿日期:
2023-09-24
出版日期:
2024-03-20
发布日期:
2024-03-20
通讯作者:
杨海波
基金资助:
Jingjing CUI1,2, Yang WANG1, Xiao LI3, Haibo YANG*,1()
Received:
2023-09-24
Online:
2024-03-20
Published:
2024-03-20
Contact:
Haibo YANG
摘要:
采用定向偏向抑制任务,结合眼动追踪技术,考察问题性社交媒体使用大学生对社交信息注意抑制的加工特点。结果发现,问题性使用组在社交信息干扰下的中断频率显著高于健康使用组;问题性使用组加工社交信息时的眼跳平均速度显著高于非社交信息。结果表明,问题性社交媒体使用大学生在社交信息的干扰下难以维持对目标的专注,对社交信息存在特异性的注意定向和高渴求,反映出问题性社交媒体使用大学生的注意抑制受损具有特异性。
崔晶晶, 汪洋, 李笑, 杨海波. 问题性社交媒体使用大学生注意抑制受损的特异性[J]. 心理与行为研究, 2024, 22(2): 258-265.
Jingjing CUI, Yang WANG, Xiao LI, Haibo YANG. Specificity of Impaired Attentional Inhibition of Problematic Social Media User Among College Students[J]. Studies of Psychology and Behavior, 2024, 22(2): 258-265.
问题性使用组 (n=37) | 健康使用组 (n=30) | t/χ2 | p | |
男性人数/女性人数 | 10/27 | 8/22 | <0.01 | 0.974 |
年龄(M±SD) | 19.14±0.79 | 19.27±1.01 | −0.60 | 0.552 |
BSMAS得分(M±SD) | 25.11±2.87 | 8.20±1.99 | 28.43 | <0.001 |
表1 被试的人口学特征
问题性使用组 (n=37) | 健康使用组 (n=30) | t/χ2 | p | |
男性人数/女性人数 | 10/27 | 8/22 | <0.01 | 0.974 |
年龄(M±SD) | 19.14±0.79 | 19.27±1.01 | −0.60 | 0.552 |
BSMAS得分(M±SD) | 25.11±2.87 | 8.20±1.99 | 28.43 | <0.001 |
被试类型 | 信息类型 | 中断频率 | 眼跳平均速度(°/s) |
社交 | 0.29 (0.01) | 186.40 (4.24) | |
问题性使用组 | 非社交 | 0.29 (0.01) | 170.51 (3.49) |
中性 | 0.27 (0.01) | 182.97 (4.29) | |
社交 | 0.21 (0.01) | 184.68 (4.56) | |
健康使用组 | 非社交 | 0.19 (0.01) | 194.47 (5.55) |
中性 | 0.24 (0.01) | 183.15 (4.61) |
表2 两组被试在三种信息条件下的中断频率和眼跳平均速度[M(SE)]
被试类型 | 信息类型 | 中断频率 | 眼跳平均速度(°/s) |
社交 | 0.29 (0.01) | 186.40 (4.24) | |
问题性使用组 | 非社交 | 0.29 (0.01) | 170.51 (3.49) |
中性 | 0.27 (0.01) | 182.97 (4.29) | |
社交 | 0.21 (0.01) | 184.68 (4.56) | |
健康使用组 | 非社交 | 0.19 (0.01) | 194.47 (5.55) |
中性 | 0.24 (0.01) | 183.15 (4.61) |
变量 | b | SE | z/t | p | 95%CI |
中断频率 | |||||
截距 | −1.41 | 0.15 | −9.28 | <0.001 | [−1.70, −1.11] |
被试类型 | 0.53 | 0.30 | 1.76 | 0.078 | [−0.06, 1.11] |
社交−非社交 | 0.05 | 0.10 | 0.50 | 0.620 | [−0.15, 0.24] |
社交−中性 | −0.05 | 0.10 | −0.55 | 0.584 | [−0.25, 0.14] |
非社交−中性 | −0.10 | 0.10 | −1.04 | 0.298 | [−0.30, 0.09] |
被试类型× 社交−非社交 | −0.09 | 0.17 | −0.53 | 0.595 | [−0.42, 0.24] |
被试类型× 社交−中性 | 0.39 | 0.17 | 2.34 | 0.019 | [0.06, 0.71] |
被试类型× 非社交−中性 | 0.48 | 0.17 | 2.86 | 0.004 | [0.15, 0.80] |
眼跳平均速度 | |||||
截距 | 182.37 | 3.83 | 47.68 | <0.001 | [174.88, 189.87] |
被试类型 | −8.80 | 5.96 | −1.48 | 0.147 | [−20.48, 2.88] |
社交−非社交 | 2.36 | 6.10 | 0.39 | 0.700 | [−9.60, 14.31] |
社交−中性 | 3.41 | 6.05 | 0.56 | 0.574 | [−8.45, 15.27] |
非社交−中性 | 1.06 | 6.26 | 0.17 | 0.866 | [−11.22, 13.34] |
被试类型× 社交−非社交 | 24.71 | 7.95 | 3.11 | 0.002 | [9.13, 40.28] |
被试类型× 社交−中性 | 2.83 | 7.65 | 0.37 | 0.712 | [−12.17, 17.83] |
被试类型× 非社交−中性 | −21.88 | 7.92 | −2.76 | 0.006 | [−37.40, −6.36] |
表3 两组被试在三种信息条件下的模型分析结果
变量 | b | SE | z/t | p | 95%CI |
中断频率 | |||||
截距 | −1.41 | 0.15 | −9.28 | <0.001 | [−1.70, −1.11] |
被试类型 | 0.53 | 0.30 | 1.76 | 0.078 | [−0.06, 1.11] |
社交−非社交 | 0.05 | 0.10 | 0.50 | 0.620 | [−0.15, 0.24] |
社交−中性 | −0.05 | 0.10 | −0.55 | 0.584 | [−0.25, 0.14] |
非社交−中性 | −0.10 | 0.10 | −1.04 | 0.298 | [−0.30, 0.09] |
被试类型× 社交−非社交 | −0.09 | 0.17 | −0.53 | 0.595 | [−0.42, 0.24] |
被试类型× 社交−中性 | 0.39 | 0.17 | 2.34 | 0.019 | [0.06, 0.71] |
被试类型× 非社交−中性 | 0.48 | 0.17 | 2.86 | 0.004 | [0.15, 0.80] |
眼跳平均速度 | |||||
截距 | 182.37 | 3.83 | 47.68 | <0.001 | [174.88, 189.87] |
被试类型 | −8.80 | 5.96 | −1.48 | 0.147 | [−20.48, 2.88] |
社交−非社交 | 2.36 | 6.10 | 0.39 | 0.700 | [−9.60, 14.31] |
社交−中性 | 3.41 | 6.05 | 0.56 | 0.574 | [−8.45, 15.27] |
非社交−中性 | 1.06 | 6.26 | 0.17 | 0.866 | [−11.22, 13.34] |
被试类型× 社交−非社交 | 24.71 | 7.95 | 3.11 | 0.002 | [9.13, 40.28] |
被试类型× 社交−中性 | 2.83 | 7.65 | 0.37 | 0.712 | [−12.17, 17.83] |
被试类型× 非社交−中性 | −21.88 | 7.92 | −2.76 | 0.006 | [−37.40, −6.36] |
白学军, 刘娟, 臧传丽, 张慢慢, 郭晓峰, 闫国利. 中文阅读过程中的副中央凹预视效应. 心理科学进展, 2011, 19 (12): 1721- 1729.
|
|
贺金波, 聂余峰, 周宗奎, 柴瑶. 网络游戏成瘾与海洛因成瘾存在相同的神经机制吗?——基于MRI的证据. 心理科学进展, 2017, 25 (8): 1327- 1336.
|
|
刘勤学, 张聚媛, 林悦. 大学生智能手机成瘾与抑制控制能力的关系: 手机位置和认知负荷的调节作用. 心理发展与教育, 2021, 37 (2): 257- 265.
|
|
Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 2016, 30 (2): 252- 262.
DOI |
|
Andreassen, C. S., & Pallesen, S. Social network site addiction—An overview. Current Pharmaceutical Design, 2014, 20 (25): 4053- 4061.
DOI |
|
Arness, D. C., & Ollis, T. A mixed-methods study of problematic social media use, attention dysregulation, and social media use motives. Current Psychology, 2023, 42 (28): 24379- 24398.
DOI |
|
Aydın, O., Obuća, F., Boz, C., & Ünal-Aydın, P. Associations between executive functions and problematic social networking sites use. Journal of Clinical and Experimental Neuropsychology, 2020, 42 (6): 634- 645.
DOI |
|
Bates, D., Mächler, M., Bolker, B. M., & Walker, S. C. Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 2015, 67 (1): 1- 48.
|
|
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. Conflict monitoring and cognitive control. Psychological Review, 2001, 108 (3): 624- 652.
DOI |
|
Brand, M. Can Internet use become addictive. Science, 2022, 376 (6595): 798- 799.
DOI |
|
Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. The interaction of person-affect-cognition-execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience & Biobehavioral Reviews, 2019, 104, 1- 10.
|
|
Brand, M., Young, K. S., Laier, C., Wölfling, K., & Potenza, M. N. 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- 266.
|
|
Chen, J. W., Liang, Y. S., Mai, C. M., Zhong, X. Y., & Qu, C. General deficit in inhibitory control of excessive smartphone users: Evidence from an event-related potential study. Frontiers in Psychology, 2016, 7, 511.
|
|
Clauss, K., Bardeen, J. R., Gordon, R. D., & Daniel, T. A. Increasing cognitive load attenuates the moderating effect of attentional inhibition on the relationship between posttraumatic stress symptoms and threat-related attention bias variability. Journal of Anxiety Disorders, 2021, 81, 102416.
DOI |
|
Di Stasi, L. L., Catena, A., Cañas, J. J., Macknik, S. L., & Martinez-Conde, S. Saccadic velocity as an arousal index in naturalistic tasks. Neuroscience & Biobehavioral Reviews, 2013, 37 (5): 968- 975.
|
|
Dong, G. H., Lin, X., Hu, Y. B., Xie, C. M., & Du, X. X. Imbalanced functional link between executive control network and reward network explain the online-game seeking behaviors in internet gaming disorder. Scientific Reports, 2015, 5 (1): 9197.
DOI |
|
Field, M., Munafò, M. R., & Franken, I. H. A. A meta-analytic investigation of the relationship between attentional bias and subjective craving in substance abuse. Psychological Bulletin, 2009, 135 (4): 589- 607.
DOI |
|
Gao, L. F., Zhang, J. F., Xie, H. P., Nie, Y. F., Zhao, Q. B., & Zhou, Z. K. Effect of the mobile phone-related background on inhibitory control of problematic mobile phone use: An event-related potentials study. Addictive Behaviors, 2020, 108, 106363.
DOI |
|
Kim, M., Lee, T. H., Choi, J. S., Kwak, Y. B., Hwang, W. J., Kim, T., … Kwon, J. S. Dysfunctional attentional bias and inhibitory control during anti-saccade task in patients with internet gaming disorder: An eye tracking study. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2019, 95, 109717.
DOI |
|
Leung, H., Pakpour, A. H., Strong, C., Lin, Y. C., Tsai, M. C., Griffiths, M. D., … Chen, I. H. Measurement invariance across young adults from Hong Kong and Taiwan among three internet-related addiction scales: Bergen Social Media Addiction Scale (BSMAS), Smartphone Application-Based Addiction Scale (SABAS), and Internet Gaming Disorder Scale-Short Form (IGDS-SF9) (study part A). Addictive Behaviors, 2020, 101, 105969.
DOI |
|
Luo, T., Qin, L. X., Cheng, L. M., Wang, S., Zhu, Z. J., Xu, J. B., … Liao, Y. H. Determination the cut-off point for the Bergen Social Media Addiction (BSMAS): Diagnostic contribution of the six criteria of the components model of addiction for social media disorder. Journal of Behavioral Addictions, 2021, 10 (2): 281- 290.
DOI |
|
Maza, M. T., Fox, K. A., Kwon, S. J., Flannery, J. E., Lindquist, K. A., Prinstein, M. J., & Telzer, E. H. Association of habitual checking behaviors on social media with longitudinal functional brain development. JAMA Pediatrics, 2023, 177 (2): 160- 167.
DOI |
|
Müller, S. M., Wegmann, E., Arias, M. G., Brotóns, E. B., Giráldez, C. M., & Brand, M. Deficits in executive functions but not in decision making under risk in individuals with problematic social-network use. Comprehensive Psychiatry, 2021, 106, 152228.
DOI |
|
Nikolaidou, M., Fraser, D. S., & Hinvest, N. Attentional bias in internet users with problematic use of social networking sites. Journal of Behavioral Addictions, 2019, 8 (4): 733- 742.
DOI |
|
R Development Core Team. (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
|
|
Robinson, T. E., & Berridge, K. C. The incentive sensitization theory of addiction: Some current issues. Philosophical Transactions of the Royal Society B: Biological Sciences, 2008, 363 (1507): 3137- 3146.
DOI |
|
Sedaghat-Nejad, E., Herzfeld, D. J., & Shadmehr, R. Reward prediction error modulates saccade vigor. The Journal of Neuroscience, 2019, 39 (25): 5010- 5017.
DOI |
|
Wegmann, E., & Brand, M. Cognitive correlates in gaming disorder and social networks use disorder: A comparison. Current Addiction Reports, 2020, 7 (3): 356- 364.
DOI |
|
Wegmann, E., & Brand, M. Affective and cognitive processes involved in behavioral addictions. Addictive Behaviors, 2021, 118, 106885.
DOI |
|
Wilcockson, T. D. W., Pothos, E. M., Osborne, A. M., & Crawford, T. J. Top-down and bottom-up attentional biases for smoking-related stimuli: Comparing dependent and non-dependent smokers. Addictive Behaviors, 2021, 118, 106886.
DOI |
|
Xie, J. Q., Rost, D. H., Wang, F. X., Wang, J. L., & Monk, R. L. The association between excessive social media use and distraction: An eye movement tracking study. Information & Management, 2021, 58 (2): 103415.
|
|
Zilverstand, A., Huang, A. S., Alia-Klein, N., & Goldstein, R. Z. Neuroimaging impaired response inhibition and salience attribution in human drug addiction: A systematic review. Neuron, 2018, 98 (5): 886- 903.
DOI |
[1] | 王影超, 李赛男, 宋子明, 闫国利. 不同阅读方式对汉语句子阅读中词频效应的影响[J]. 心理与行为研究, 2024, 22(2): 183-188, 226. |
[2] | 陈汝淇, 包亚倩, 黄林洁琼, 李兴珊. 中文阅读中词语加工与眼动控制整合模型简介[J]. 心理与行为研究, 2023, 21(6): 725-735. |
[3] | 于秒, 王文娣, 陈晓霄. 汉语“N的V”结构加工的韵律制约[J]. 心理与行为研究, 2023, 21(6): 744-750. |
[4] | 鹿子佳, 张志超, 符颖, 张慢慢, 臧传丽, 白学军. 重复词无法帮助中文读者获得副中央凹词类信息[J]. 心理与行为研究, 2023, 21(5): 577-584. |
[5] | 刘致宏, 张野, 王凯, 许晴. 校园排斥与青少年问题性社交媒体使用的关系:基于潜调节结构方程模型[J]. 心理与行为研究, 2023, 21(4): 533-540. |
[6] | 阿依古丽·艾尼, 买合甫来提·坎吉, 刘贵雄, 帕里扎·布拉提汗. 词间空格对维吾尔族大学生词汇加工的影响[J]. 心理与行为研究, 2023, 21(2): 163-168. |
[7] | 张俐娟, 张凤筠, 赵赛男, 王敬欣. 合理性对汉语阅读中双字词语义预视效益的优势作用:眼动研究[J]. 心理与行为研究, 2023, 21(1): 12-19. |
[8] | 丁辉, 张志超, 张慢慢, 臧传丽. 语境影响反语理解的眼动研究元分析[J]. 心理与行为研究, 2023, 21(1): 28-35. |
[9] | 金雪莲, 姜英杰. 孤独症儿童自我−他人来源记忆监测损伤:学习时间分配的作用[J]. 心理与行为研究, 2022, 20(6): 768-774. |
[10] | 贾宁, 容丽卓, 代景华. 社会性线索对内隐和外显元认知监控的影响[J]. 心理与行为研究, 2022, 20(5): 593-599. |
[11] | 李士一, 谢岩枫, 赵光, 白学军. 媒体多任务经验对不同注意模式下内隐记忆的影响[J]. 心理与行为研究, 2022, 20(4): 433-440. |
[12] | 张慢慢, 胡惠兰, 边菡, 李芳, 张志超, 臧传丽. 中文阅读中快速读者与慢速读者的词频效应[J]. 心理与行为研究, 2022, 20(3): 304-310. |
[13] | 何立媛, 赵星, 白玉, 刘妮娜. 汉语老年读者的多词单元加工:来自眼动研究的证据[J]. 心理与行为研究, 2022, 20(2): 160-166. |
[14] | 秦钊, 王影超, 叶佳滢, 袁小源, 闫国利. 聋人句子阅读中的视觉功能补偿现象:副中央凹−中央凹效应的证据[J]. 心理与行为研究, 2022, 20(2): 167-173. |
[15] | 顾俊娟, 高志华, 马绍扬. 嵌套词汉字位置加工的亚词边界效应[J]. 心理与行为研究, 2022, 20(1): 1-7. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||