奖惩动机诱发对网络游戏成瘾高风险大学生工作记忆刷新功能的影响

doi:10.3724/SP.J.1041.2026.1517

摘要/Abstract

摘要： 基于对网络游戏成瘾高风险大学生学业不佳问题的关注, 本研究通过两项多因素行为实验, 分别考察了奖励和惩罚动机诱发对网络游戏成瘾高风险大学生工作记忆刷新功能的影响。结果发现, 网络游戏成瘾高风险大学生的工作记忆刷新反应时较长且正确率较低; 奖励动机诱发下, 成瘾高风险与低风险大学生工作记忆刷新的反应时减慢、正确率提高, 加工程度增强; 惩罚动机诱发下, 二者的正确率提高, 且成瘾高风险大学生的惩罚效应量小于低风险大学生。跨实验分析发现, 就工作记忆刷新正确率而言, 成瘾高风险大学生在奖励动机诱发时显著高于惩罚动机诱发时, 成瘾低风险大学生在奖励和惩罚两种动机诱发时无显著差异。研究表明, 网络游戏成瘾高风险大学生工作记忆刷新功能受到一定程度的损伤, 在工作记忆刷新任务中对惩罚较不敏感。

关键词: 工作记忆刷新功能, 网络游戏成瘾, 奖励动机, 惩罚动机, 大学生

Abstract: Previous studies have found that individuals with internet gaming disorder (IGD) have relatively low cognitive functioning and commonly suffers from poor academic performance. As a key aspect of executive functioning, working memory plays an important role in the academic growth of college students, not only as a foundation for cognitive development, learning, and education, but also as a prerequisite to cope with the complex cognitive challenges of daily life. Several studies have shown that IGD risk negatively affects working memory updating, and this impairment likely contributes to poor academic performance in college students at high risk for IGD. Therefore, based on the social cognition theory, this study aims to explore the influences of IGD risk on college students’ working memory updating, as well as the effects and differences in reward and punishment motivation induced by monetary rewards and losses on working memory updating in high-risk (IGD) and low-risk (non-IGD) college students.
Participants were recruited using posters. Forty-two high-risk and 45 low-risk college students were identified using the Internet (Gaming) Addiction Test. In Experiment 1, participants completed three reward-based N-back tasks with different memory loads to examine differences in working memory updating between high- and low-risk college students in conditions with or without reward motivation. In Experiment 2, a punishment-based N-back task was used to examine differences in working memory updating between high- and low-risk college students in conditions with or without punishment motivation across three different memory loads.
The results of Experiment 1 showed that in the rewarded version of the working memory updating task, high-risk college students had weaker processing, that is, longer reaction times and lower accuracy, than low-risk college students. With reward motivation, high- and low-risk college students’ reaction time increased, accuracy improved, and the degree of processing was enhanced. There was no significant difference in the role of reward motivation in facilitating response time and accuracy in the working memory updating task between high- and low-risk college students. The results of Experiment 2 showed that in the punishment version of the working memory updating task, high-risk college students had weaker processing than low-risk college students. With punishment motivation, the accuracy of high- and low-risk college students increased, and it contributed less to facilitating the working memory updating accuracy of high-risk college students than low-risk college students. After conducting a cross-experimental analysis, it was found that the accuracy of reward motivation in working memory updating was significantly higher than that of punishment motivation among high-risk students, and there was no significant difference in the accuracy of reward and punishment motivation among low-risk students. In conclusion, this study showed that the working memory updating speed and accuracy of high-risk college students are significantly slower and lower than low-risk college students. Rewarding motivation helped improve working memory updating performance in both high- and low-risk college students by increasing accuracy and reducing speed, whereas punishment motivation only improved accuracy and had a weaker facilitating effect for high-risk college students than for low-risk college students.
Overall, the facilitating effect of reward motivation on the accuracy of working memory updating in high-risk students is greater than that of punishment motivation, whereas reward and punishment motivation show the same utility value for accuracy of working memory updating in low-risk college students. This implies that college students at high risk for IGD exhibit certain impairment in working memory updating and reduced sensitivity to punishment in cognitive tasks. Additionally, the mechanism of working memory updating in college students at high risk for IGD provides new insights into their poor academic performance, and offers suggestions for reference to address this issue.

Key words: working memory updating, internet gaming disorder, reward motivation, punishment motivation, college students

高源霞, 王江洋. (2026). 奖惩动机诱发对网络游戏成瘾高风险大学生工作记忆刷新功能的影响. 心理学报, 58(8), 1517-1531.

GAO Yuanxia, WANG Jiangyang. (2026). Effects of reward and punishment motivation on working memory updating among college students at high risk for internet gaming disorder. Acta Psychologica Sinica, 58(8), 1517-1531.

参考文献

[1] Belayachi S., Majerus S., Gendolla G., Salmon E., Peters F., & Van der Linden, M. (2015). Are the carrot and the stick the two sides of same coin? A neural examination of approach/avoidance motivation during cognitive performance.Behavioural Brain Research, 293, 217-226.
[2] Berta K., Pesthy Z. V., Vékony T., Farkas B. C., Király O., Demetrovics Z., Németh D., & Kun B. (2025). Game on or gone too far? Executive functioning and implicit sequence learning in problematic vs. recreational gamers.Computers in Human Behavior, 169, 108878.
[3] 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, 375.
[4] Canessa N., Basso G., Poggi P., & Gianelli C. (2022). Altered striatal-opercular intrinsic connectivity reflects decreased aversion to losses in alcohol use disorder.Neuropsychologia, 172, 108258.
[5] Cederblad A. M. H., Visokomogilski A., Andersen S. K., MacLeod M. J., & Sahraie A. (2021). Conscious awareness modulates processing speed in the redundant signal effect.Experimental Brain Research, 239(6), 1877-1893.
[6] China Internet Network Information Center. (2025). The 56th statistical report on China’s Internet development. 2025- 07-21 Retrieved from https://cnnic.net.cn/n4/2025/0721/c88-11328.html
[中国互联网络信息中心. (2025). 第56次中国互联网络发展状况统计报告. 2025-07-21取自https://cnnic.net.cn/n4/2025/0721/c88-11328.html]
[7] Cho T. H., Nah Y., Park S. H., & Han S. (2022). Prefrontal cortical activation in internet gaming disorder scale high scorers during actual real-time internet gaming: A preliminary study using fNIRS.Journal of Behavioral Addictions, 11(2), 492-505.
[8] Combrisson E., Basanisi R., Gueguen M. C. M., Rheims S., Kahane P., Bastin J., & Brovelli A. (2024). Neural interactions in the human frontal cortex dissociate reward and punishment learning. eLife, 12, RP92938.
[9] Corr, P. J. (2001). Testing problems in J. A. Gray’s personality theory: A commentary on Matthews and Gilliland (1999).Personality and Individual Differences, 30(2), 333-352.
[10] Cowan, N. (2017). The many faces of working memory and short-term storage.Psychonomic Bulletin & Review, 24(4), 1158-1170.
[11] Cubillo A., Makwana A. B., & Hare T. A. (2019). Differential modulation of cognitive control networks by monetary reward and punishment.Social Cognitive and Affective Neuroscience, 14(3), 305-317.
[12] Cutting J., Copeland B., & McNab F. (2023). Higher working memory capacity and distraction-resistance associated with strategy (not action) game playing in younger adults, but puzzle game playing in older adults.Heliyon, 9(8), e19098.
[13] Dong G., Li H., Wang L., & Potenza M. N. (2017). Cognitive control and reward/loss processing in internet gaming disorder: Results from a comparison with recreational internet game-users.European Psychiatry, 44, 30-38.
[14] Dong G., Lin X., Zhou H., & Lu Q. (2014). Cognitive flexibility in internet addicts: fMRI evidence from difficult-to-easy and easy-to-difficult switching situations.Addictive Behaviors, 39(3), 677-683.
[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.
[16] Duehlmeyer L., Levis B., & Hester R. (2018). Effects of reward and punishment on learning from errors in smokers.Drug and Alcohol Dependence, 188, 32-38.
[17] Galea J. M., Mallia E., Rothwell J., & Diedrichsen J. (2015). The dissociable effects of punishment and reward on motor learning.Nature Neuroscience, 18(4), 597-602.
[18] Gao Y. X., Wang J. Y., & Dong G. H. (2022). The prevalence and possible risk factors of internet gaming disorder among adolescents and young adults: Systematic reviews and meta-analyses.Journal of Psychiatric Research, 154, 35-43.
[19] Gray J. A.,& McNaughton, N. (2000). The neuropsychology of anxiety: An enquiry into the functions of the septo- hippocampal system (2nd ed.). Oxford University Press.
[20] Grogan J. P., Randhawa G., Kim M., & Manohar S. G. (2022). Motivation improves working memory by two processes: Prioritisation and retrieval thresholds.Cognitive Psychology, 135, 101472.
[21] Grogan J. P., Sandhu T. R., Hu M. T., & Manohar S. G. (2020). Dopamine promotes instrumental motivation, but reduces reward-related vigour.eLife, 9, e58321.
[22] Hawi N. S., Samaha M., & Griffiths M. D. (2018). Internet gaming disorder in Lebanon: Relationships with age, sleep habits, and academic achievement.Journal of Behavioral Addictions, 7(1), 70-78.
[23] Hippmann B., Tzvi E., Göttlich M., Weiblen R., Münte T. F., & Jessen S. (2021). Effective connectivity underlying reward-based executive control.Human Brain Mapping, 42(14), 4555-4567.
[24] Hong W., Liang P., Pan Y., Jin J., Luo L., Li Y., … Zhang X. (2023). Reduced loss aversion in value-based decision-making and edge-centric functional connectivity in patients with internet gaming disorder.Journal of Behavioral Addictions, 12(2), 458-470.
[25] Jang J. H., Chung S. J., Choi A., Lee J. Y., Kim B., Park M., … Choi J. S. (2021). Association of general cognitive functions with gaming use in young adults: A comparison among excessive gamers, regular gamers and non-gamers.Journal of Clinical Medicine, 10(11), 2293.
[26] Klink P. C., Jeurissen D., Theeuwes J., Denys D., & Roelfsema P. R. (2017). Working memory accuracy for multiple targets is driven by reward expectation and stimulus contrast with different time-courses.Scientific Reports, 7(1), 9082.
[27] Kuss D. J., Pontes H. M., & Griffiths M. D. (2018). Neurobiological correlates in internet gaming disorder: A systematic literature review.Frontiers in Psychiatry, 9, 166.
[28] Lee J. Y., Choi C. H., Park M., Park S., & Choi J. S. (2022). Enhanced resting-state EEG source functional connectivity within the default mode and reward-salience networks in internet gaming disorder.Psychological Medicine, 52(11), 2189-2197.
[29] Leng X., Yee D., Ritz H., & Shenhav A. (2021). Dissociable influences of reward and punishment on adaptive cognitive control.PLoS Computational Biology, 17(12), e1009737.
[30] Massar S. A. A., Pu Z., Chen C., & Chee, M. W. L. (2020). Losses motivate cognitive effort more than gains in effort-based decision making and performance.Frontiers in Human Neuroscience, 14, 287.
[31] Miller, C. D., & Byrnes, J. P. (2020). What’s the best way to characterize the relationship between working memory and achievement? An initial examination of competing theories.Journal of Educational Psychology, 112(5), 1074-1084.
[32] Ngetich R., Burleigh T. L., Czakó A., Vékony T., Németh D., & Demetrovics Z. (2023). Working memory performance in disordered gambling and gaming: A systematic review.Comprehensive Psychiatry, 126, 152408.
[33] Nie Q., Teng Z., Yang C., Griffiths M. D., & Guo C. (2024). Longitudinal relationships between school climate, academic achievement, and gaming disorder symptoms among Chinese adolescents.Journal of Youth and Adolescence, 53(7), 1646-1665.
[34] Peng P., Chen Z., Ren S., He Y., Li J., Liao A., … Liao Y. (2025). Trajectory of internet gaming disorder among Chinese adolescents: Course, predictors, and long-term mental health outcomes.Journal of Behavioral Addictions, 14(2), 846-860.
[35] Peng, P., & Kievit, R. A. (2020). The development of academic achievement and cognitive abilities: A bidirectional perspective.Child Development Perspectives, 14(1), 15-20.
[36] Ricker T. J., Cagna C. J., Tong T. T., Dobryakova E., & Sandry J. (2025). Reward-based prioritization in working memory is distinct from recency and due to a resource trade-off.Psychonomic Bulletin & Review, 33(1), 8.
[37] Rodas J. A., Asimakopoulou A. A., & Greene C. M. (2024). Can we enhance working memory? Bias and effectiveness in cognitive training studies.Psychonomic Bulletin & Review, 31(5), 1891-1914.
[38] Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory.Contemporary Educational Psychology, 60, 101832.
[39] Statsenko Y., Habuza T., Gorkom K. N., Zaki N., & Almansoori T. M. (2020). Applying the inverse efficiency score to visual-motor task for studying speed-accuracy performance while aging.Frontiers in Aging Neuroscience, 12, 574401.
[40] Thurm F., Zink N., & Li S. C. (2018). Comparing effects of reward anticipation on working memory in younger and older adults.Frontiers in Psychology, 9, 2318.
[41] Townsend J. T.,& Ashby, F. G. (1983). Stochastic modelling of elementary psychological processes. New York, NY: Cambridge University Press.
[42] Wang H., Sun Y., Lan F., & Liu Y. (2020). Altered brain network topology related to working memory in Internet addiction.Journal of Behavioral Addictions, 9(2), 325-338.
[43] Wang L., Wu L., Lin X., Zhang Y., Zhou H., Du X., & Dong G. H. (2016). Altered brain functional networks in people with internet gaming disorder: Evidence from resting- state fMRI.Psychiatry Research Neuroimaging, 254, 156-163.
[44] Weinstein, A. M. (2017). An update overview on brain imaging studies of internet gaming disorder.Frontiers in Psychiatry, 8, 185.
[45] Weinstein, A., & Lejoyeux, M. (2020). Neurobiological mechanisms underlying internet gaming disorder.Dialogues in Clinical Neuroscience, 22(2), 113-126.
[46] Wen X., Yue L., Du Z., Zhao J., Ge M., Yuan C., … Yuan K. (2025). Functional connectome gradient of prefrontal cortex as biomarkers of high risk for internet gaming disorder.NeuroImage, 306, 121010.
[47] Wölfling K., Duven E., Wejbera M., Beutel M. E., & Müller K. W. (2020). Discounting delayed monetary rewards and decision making in behavioral addictions: A comparison between patients with gambling disorder and internet gaming disorder.Addictive Behaviors, 108, 106446.
[48] Xu S., Qi S., Duan H., Zhang J., Akioma M., Gao F., Wu A., & Yuan Z. (2022). Task difficulty regulates how conscious and unconscious monetary rewards boost the performance of working memory: An event-related potential study.Frontiers in Systems Neuroscience, 15, 716961.
[49] Yang Q., Si S., & Pourtois G. (2023). Parsing the contributions of negative affect vs. aversive motivation to cognitive control: An experimental investigation.Frontiers in Behavioral Neuroscience, 17, 1209824.
[50] Yao Y. W., Liu L., Worhunsky P. D., Lichenstein S., Ma S. S., Zhu L., … Yip S. W. (2020). Is monetary reward processing altered in drug-naïve youth with a behavioral addiction? Findings from internet gaming disorder.Neuroimage Clinical, 26, 102202.
[51] Yee, D. M., & Braver, T. S. (2018). Interactions of motivation and cognitive control.Current Opinion in Behavioral Sciences, 19, 83-90.
[52] Young, K. S. (1999). The research and controversy surrounding Internet addiction.Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 2(5), 381-383.
[53] Zhang M. X., Wang X., Yu S. M., & Wu A. (2019). Purpose in life, social support, and internet gaming disorder among Chinese university students: A 1-year follow-up study.Addictive Behaviors, 99, 106070.
[54] Zhang X., Liu D., Li J., Zheng X., Zhou S., Elhai J. D., Montag C., & Yang H. (2025). Prefrontal cortex responses to game rewards and losses in individuals with internet gaming disorder: Insights from fNIRS during mobile gameplay.Journal of Behavioral Addictions, 14(1), 347-360.
[55] Zhang Y., Lin X., Zhou H., Xu J., Du X., & Dong G. H. (2016). Brain activity toward gaming-related cues in internet gaming disorder during an addiction Stroop task.Frontiers in Psychology, 7, 714.
[56] Zhou W. R., Wang M., Dong H. H., Zhang Z., Du X., Potenza M. N., & Dong G. H. (2021). Imbalanced sensitivities to primary and secondary rewards in internet gaming disorder.Journal of Behavioral Addictions, 10(4), 990-1004.
[57] Zhou X., Zeng Y., Wen Y., Dong X., Gola M., & Li Y. (2025). Love at first glance: Imbalanced processing to gaming and natural rewards in internet gaming disorder.Journal of Behavioral Addictions, 14(2), 805-816.