Specificity of Impaired Attentional Inhibition of Problematic Social Media User Among College Students

doi:10.12139/j.1672-0628.2024.02.015

Abstract

Abstract:

Using the orienting bias inhibition task and eye-tracking techniques, the present study investigated the processing characteristics of social information attentional inhibition among college students exhibiting problematic social media use. The results found that the break frequency of problematic users under social information interference was significantly higher than that of normal users, while the saccade average velocity of problematic users towards social information was significantly higher than that of non-social information. In conclusion, college students with problematic social media use have difficulty maintaining their focus on the target under the interference of social information. Additionally, they exhibit specific attentional orientation and a high craving for social information, reflecting the specificity of impaired attentional inhibition in college students with problematic social media use.

Key words: problematic social media use, attentional inhibition, orienting bias inhibition task, craving, eye movement

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。

关键词: 问题性社交媒体使用, 注意抑制, 定向偏向抑制任务, 渴求, 眼动

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.

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.

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References 0

	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

	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

	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)

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]