短视频成瘾的信息加工认知神经机制综述
A Review of the Cognitive and Neural Mechanisms Underlying Information Processing in Short-Video Addiction
DOI: 10.12677/ap.2026.169429, PDF,    科研立项经费支持
作者: 陈 卓*, 郑程之:华北理工大学心理与精神卫生学院,河北 唐山;河北省心理健康与脑科学重点实验室,河北 唐山
关键词: 短视频成瘾信息加工前额叶皮层认知资源分配Short-Video Addiction Information Processing Prefrontal Cortex Cognitive Resource Allocation
摘要: 短视频成瘾对大学生信息加工能力与认知资源分配产生了显著影响。本文基于Brand等人的网络成瘾前额叶控制模型,总结了短视频成瘾的神经基础,重点阐述背外侧前额叶功能减弱与眶额叶对成瘾线索过度反应的机制。结合信息加工双路径理论,分析了传播线索与信息复杂度如何动态调节个体的加工深度与认知资源分配。同时,综述了功能性近红外光谱技术在自然情境认知研究中的优势及其在短视频成瘾领域的已有发现。最后,提出未来应构建“环境–个体–大脑–行为”整合模型,发展多模态神经成像技术,并关注纵向追踪与干预研究。本文为理解短视频成瘾影响信息加工的神经机制提供了理论框架,并为媒介素养教育与健康传播实践提供科学依据。
Abstract: Short-video addiction has a significant impact on college students’ information processing abilities and cognitive resource allocation. Based on Brand et al.’s prefrontal control model of internet addiction, this paper summarizes the neural basis of short-video addiction, focusing on the mechanisms underlying reduced dorsolateral prefrontal cortex function and excessive orbital frontal cortex reactivity to addictive cues. Combining the dual-pathway theory of information processing, the study analyzes how communication cues and information complexity dynamically modulate an individual’s processing depth and cognitive resource allocation. Additionally, it reviews the advantages of functional near-infrared spectroscopy (fNIRS) in naturalistic cognitive research and summarizes existing findings in the field of short video addiction. Finally, the paper proposes that future research should construct an integrated “environment-individual-brain-behavior” model, develop multimodal neuroimaging techniques, and prioritize longitudinal tracking and intervention studies. This paper provides a theoretical framework for understanding the neural mechanisms through which short video addiction influences information processing, and offers a scientific basis for media literacy education and health communication practices.
文章引用:陈卓, 郑程之 (2026). 短视频成瘾的信息加工认知神经机制综述. 心理学进展, 16(9), 70-79. https://doi.org/10.12677/ap.2026.169429

参考文献

[1] 贺文华, 李纯青(2026). 短视频碎片化对用户享乐和功能体验双元的影响机制——基于信息加工理论. 经济与管理, 40(2), 45-54.
[2] 谭佳英(2025). 短视频过度使用对认知控制的影响. 硕士学位论文, 天津: 天津师范大学.
[3] 王瑞琰(2026). 短视频沉迷对心智游移的影响——来自行为与fNIRS的证据. 硕士学位论文, 兰州: 西北师范大学.
[4] 薛莉(2025). 青少年网络成瘾、自我控制、焦虑对学业拖延的影响研究. 硕士学位论文, 成都: 成都医学院.
[5] 中国互联网络信息中心(2026). 57中国互联网络发展状况统计报告. 中国互联网络信息中心.
[6] 周贵凤(2025). 农村青少年拖延倾向特质对手机依赖的影响: 一个有调节的中介及干预研究. 硕士学位论文, 桂林: 广西师范大学.
[7] Bayer, J. B., Triệu, P., & Ellison, N. B. (2020). Social Media Elements, Ecologies, and Effects. Annual Review of Psychology, 71, 471-497.
https://doi.org/10.1146/annurev-psych-010419-050944
[8] Bechara, A. (2005). Decision Making, Impulse Control and Loss of Willpower to Resist Drugs: A Neurocognitive Perspective. Nature Neuroscience, 8, 1458-1463.
https://doi.org/10.1038/nn1584
[9] Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W. et al. (2019). 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, 104, 1-10.
https://doi.org/10.1016/j.neubiorev.2019.06.032
[10] Brand, M., Young, K. S., & Laier, C. (2014). Prefrontal Control and Internet Addiction: A Theoretical Model and Review of Neuropsychological and Neuroimaging Findings. Frontiers in Human Neuroscience, 8, Article ID: 375.
https://doi.org/10.3389/fnhum.2014.00375
[11] Cacioppo, J. T., & Petty, R. E. (1982). The Need for Cognition. Journal of Personality and Social Psychology, 42, 116-131.
https://doi.org/10.1037/0022-3514.42.1.116
[12] Chaiken, S. (1980). Heuristic versus Systematic Information Processing and the Use of Source versus Message Cues in Persuasion. Journal of Personality and Social Psychology, 39, 752-766.
https://doi.org/10.1037/0022-3514.39.5.752
[13] Chen, Y., Li, M., Guo, F., & Wang, X. (2023). The Effect of Short-Form Video Addiction on Users’ Attention. Behaviour & Information Technology, 42, 2893-2910.
https://doi.org/10.1080/0144929x.2022.2151512
[14] Cui, X., Bray, S., Bryant, D. M., Glover, G. H., & Reiss, A. L. (2011). A Quantitative Comparison of NIRS and fMRI across Multiple Cognitive Tasks. NeuroImage, 54, 2808-2821.
https://doi.org/10.1016/j.neuroimage.2010.10.069
[15] Dong, G., & Potenza, M. N. (2014). A Cognitive-Behavioral Model of Internet Gaming Disorder: Theoretical Underpinnings and Clinical Implications. Journal of Psychiatric Research, 58, 7-11.
https://doi.org/10.1016/j.jpsychires.2014.07.005
[16] Dong, G., Wang, L., Du, X., & Potenza, M. N. (2017). Gaming Increases Craving to Gaming-Related Stimuli in Individuals with Internet Gaming Disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2, 404-412.
https://doi.org/10.1016/j.bpsc.2017.01.002
[17] Falk, E. B., Cascio, C. N., & Coronel, J. C. (2015). Neural Prediction of Communication-Relevant Outcomes. Communication Methods and Measures, 9, 30-54.
https://doi.org/10.1080/19312458.2014.999750
[18] Flanagin, A. J., & Metzger, M. J. (2007). The Role of Site Features, User Attributes, and Information Verification Behaviors on the Perceived Credibility of Web-Based Information. New Media & Society, 9, 319-342.
https://doi.org/10.1177/1461444807075015
[19] Gao, Y., Gong, L., Liu, H., Kong, Y., Wu, X., Guo, Y. et al. (2022). Research on the Influencing Factors of Users’ Information Processing in Online Health Communities Based on Heuristic-Systematic Model. Frontiers in Psychology, 13, Article ID: 966033.
https://doi.org/10.3389/fpsyg.2022.966033
[20] Gazzo Castañeda, L. E., Sklarek, B., Dal Mas, D. E., & Knauff, M. (2023). Probabilistic and Deductive Reasoning in the Human Brain. NeuroImage, 275, Article 120180.
https://doi.org/10.1016/j.neuroimage.2023.120180
[21] Goldstein, R. Z., & Volkow, N. D. (2011). Dysfunction of the Prefrontal Cortex in Addiction: Neuroimaging Findings and Clinical Implications. Nature Reviews Neuroscience, 12, 652-669.
https://doi.org/10.1038/nrn3119
[22] Hong, T., Su, C., Zhou, H., Geng, F., & Hu, Y. (2026). Brain Activity Inhibition during Short Video Viewing: Neurochemical Insights. NeuroImage, 327, Article 121722.
https://doi.org/10.1016/j.neuroimage.2026.121722
[23] Ko, C. H., Yen, J. Y., Yen, C. F., Chen, C. S., & Chen, C. C. (2012). The Association between Internet Addiction and Psychiatric Disorder: A Review of the Literature. European Psychiatry, 27, 1-8.
https://doi.org/10.1016/j.eurpsy.2010.04.011
[24] Ma, L., & Jiang, Q. (2024). Swiping More, Thinking Less: Using TikTok Hinders Analytic Thinking. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 18, Article 3.
https://doi.org/10.5817/cp2024-3-1
[25] Markus, A., & Shamay-Tsoory, S. G. (2024). Hyperscanning: From Inter-Brain Coupling to Causality. Frontiers in Human Neuroscience, 18, Article ID: 1497034.
https://doi.org/10.3389/fnhum.2024.1497034
[26] Meinert, J., & Krämer, N. C. (2022). How the Expertise Heuristic Accelerates Decision-Making and Credibility Judgments in Social Media by Means of Effort Reduction. PLOS ONE, 17, e0264428.
https://doi.org/10.1371/journal.pone.0264428
[27] Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and Heuristic Approaches to Credibility Evaluation Online. Journal of Communication, 60, 413-439.
https://doi.org/10.1111/j.1460-2466.2010.01488.x
[28] Miller, E. K., & Cohen, J. D. (2001). An Integrative Theory of Prefrontal Cortex Function. Annual Review of Neuroscience, 24, 167-202.
https://doi.org/10.1146/annurev.neuro.24.1.167
[29] Montag, C., Yang, H., & Elhai, J. D. (2021). On the Psychology of TikTok Use: A First Glimpse from Empirical Findings. Frontiers in Public Health, 9, Article ID: 641673.
https://doi.org/10.3389/fpubh.2021.641673
[30] Nisbett, R. E., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and Systems of Thought: Holistic versus Analytic Cognition. Psychological Review, 108, 291-310.
https://doi.org/10.1037/0033-295x.108.2.291
[31] Petty, R. E., & Cacioppo, J. T. (1986). The Elaboration Likelihood Model of Persuasion. Advances in Experimental Social Psychology, 19, 123-205.
https://doi.org/10.1016/s0065-2601(08)60214-2
[32] Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S. et al. (2020). The Present and Future Use of Functional Near‐Infrared Spectroscopy (fNIRS) for Cognitive Neuroscience. Annals of the New York Academy of Sciences, 1464, 5-29.
https://doi.org/10.1111/nyas.13948
[33] Ruzzante, F., Gugushvili, N., & Verduyn, P. (2026). Social Media Addiction and Internet Gaming Disorder in Adolescents: Testing the Spectrum and Connectivity Hypotheses Using a Network Approach. International Journal of Mental Health and Addiction, 1-21.
https://doi.org/10.1007/s11469-026-01641-3
[34] Shi, J., Chen, Z., & Tian, M. (2011). Internet Self-Efficacy, the Need for Cognition, and Sensation Seeking as Predictors of Problematic Use of the Internet. Cyberpsychology, Behavior, and Social Networking, 14, 231-234.
https://doi.org/10.1089/cyber.2009.0462
[35] Sirois, F., & Pychyl, T. (2013). Procrastination and the Priority of Short‐Term Mood Regulation: Consequences for Future Self. Social and Personality Psychology Compass, 7, 115-127.
https://doi.org/10.1111/spc3.12011
[36] Steel, P. (2007). The Nature of Procrastination: A Meta-Analytic and Theoretical Review of Quintessential Self-Regulatory Failure. Psychological Bulletin, 133, 65-94.
https://doi.org/10.1037/0033-2909.133.1.65
[37] Strangman, G., Boas, D. A., & Sutton, J. P. (2002). Non-Invasive Neuroimaging Using Near-Infrared Light. Biological Psychiatry, 52, 679-693.
https://doi.org/10.1016/s0006-3223(02)01550-0
[38] Su, C., Zhou, H., Gong, L., Teng, B., Geng, F., & Hu, Y. (2021). Viewing Personalized Video Clips Recommended by Tiktok Activates Default Mode Network and Ventral Tegmental Area. NeuroImage, 237, Article 118136.
https://doi.org/10.1016/j.neuroimage.2021.118136
[39] Sundar, S. S. (2008). The MAIN Model: A Heuristic Approach to Understanding Technology Effects on Credibility. In M. J. Metzger, & A. J. Flanagin (Eds.), Digital Media, Youth, and Credibility (pp. 73-100). The MIT Press.
[40] Varlet, M., & Grootswagers, T. (2024). Measuring Information Alignment in Hyperscanning Research with Representational Analyses: Moving Beyond Interbrain Synchrony. Frontiers in Human Neuroscience, 18, Article ID: 1385624.
https://doi.org/10.3389/fnhum.2024.1385624
[41] Volkow, N. D., Wang, G., Fowler, J. S., & Tomasi, D. (2012). Addiction Circuitry in the Human Brain. Annual Review of Pharmacology and Toxicology, 52, 321-336.
https://doi.org/10.1146/annurev-pharmtox-010611-134625
[42] Yan, T., Su, C., Xue, W., Hu, Y., & Zhou, H. (2024). Mobile Phone Short Video Use Negatively Impacts Attention Functions: An EEG Study. Frontiers in Human Neuroscience, 18, Article ID: 1383913.
https://doi.org/10.3389/fnhum.2024.1383913
[43] Zhang, S., & Li, S. (2025). How Short Video Addiction Affects Risk Decision-Making Behavior in College Students Based on FNIRs Technology. Frontiers in Human Neuroscience, 19, Article ID: 1542271.
https://doi.org/10.3389/fnhum.2025.1542271