Thought Suppression as a Psychological Amplifier of Motion Sickness: A Contextual Literature Review
DOI:
https://doi.org/10.62177/amit.v2i4.1544Keywords:
Thought Suppression, Motion Sickness, Visually Induced Motion Sickness, Interoception, Cognitive Load, NauseaAbstract
This literature review examines whether thought suppression may intensify motion sickness and visually induced motion sickness. The central claim is deliberately cautious. The recent literature does not yet provide a large body of direct experiments showing that suppressing thoughts about nausea reliably worsens motion sickness. However, several adjacent evidence streams make the claim theoretically plausible and empirically testable. Contemporary motion sickness research defines motion sickness as a physiological response to provocative physical or visual motion that includes nausea, dizziness, thermoregulatory disruption, altered arousal, headache, and ocular strain. Recent work also emphasizes individual susceptibility, cognitive load, interoceptive processing, anxiety, expectation, autonomic regulation, and gut-brain communication. Contemporary thought-suppression research has also become more nuanced. Suppression does not always cause rebound, but suppression can become maladaptive when it creates continuous monitoring, high cognitive demand, threat appraisal, and rigid avoidance of internal experience. This review integrates these strands to propose a contextual model. Thought suppression may worsen motion sickness when it increases monitoring of nausea-related sensations, competes with limited cognitive resources, strengthens negative expectancy, and amplifies autonomic or gastrointestinal feedback loops. The review concludes that future research should test suppression, acceptance, distraction, and reappraisal instructions during motion-sickness exposure using symptom ratings, physiological measures, eye tracking, and longitudinal habituation designs.
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Alcantara-Thome, M., Miguel-Puga, J. A., & Jauregui-Renaud, K. (2021). Anxiety and motion sickness susceptibility may influence the ability to update orientation in the horizontal plane of healthy subjects. Frontiers in Integrative Neuroscience, 15, Article 742100. https://doi.org/10.3389/fnint.2021.742100
Biswas, N., Mukherjee, A., & Bhattacharya, S. (2024). “Are you feeling sick?” - A systematic literature review of cybersickness in virtual reality. ACM Computing Surveys, 56(11), 1-38. https://doi.org/10.1145/3670008
Cha, Y. H., Golding, J. F., Keshavarz, B., Furman, J., Kim, J.-S., Lopez-Escamez, J. A., Magnusson, M., Yates, B. J., Lawson, B. D., Staab, J. P., & Bisdorff, A. (2021). Motion sickness diagnostic criteria: Consensus document of the Classification Committee of the Bárány Society. Journal of Vestibular Research, 31(5), 327-344. https://doi.org/10.3233/VES-200005
Golding, J. F., Rafiq, A., & Keshavarz, B. (2021). Predicting individual susceptibility to visually induced motion sickness by questionnaire. Frontiers in Virtual Reality, 2, Article 576871. https://doi.org/10.3389/frvir.2021.576871
Keshavarz, B., & Golding, J. F. (2022). Motion sickness: Current concepts and management. Current Opinion in Neurology, 35(1), 107-112. https://doi.org/10.1097/WCO.0000000000001018
Keshavarz, B., Murovec, B., Mohanathas, N., & Golding, J. F. (2023). The Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ): Estimating individual susceptibility to motion sickness-like symptoms when using visual devices. Human Factors, 65(1), 107-124. https://doi.org/10.1177/00187208211008687
Kourtesis, P., Linnell, J., Amir, R., Argelaguet, F., & MacPherson, S. E. (2023). Cybersickness in virtual reality questionnaire (CSQ-VR): A validation and comparison against SSQ and VRSQ. Virtual Worlds, 2(1), 16-35. https://doi.org/10.3390/virtualworlds2010002
Kreddig, N., Hasenbring, M. I., & Keogh, E. (2022). Comparing the effects of thought suppression and focused distraction on pain-related attentional biases in men and women. The Journal of Pain, 23(11), 1958-1972. https://doi.org/10.1016/j.jpain.2022.07.004
Li, Y., Li, Y., Li, Y., Luo, B., Tang, B., & Yue, Q. (2025). A study on the mitigating effect of different music types on motion sickness based on EEG analysis. Frontiers in Human Neuroscience, 19, Article 1636109. https://doi.org/10.3389/fnhum.2025.1636109
Lukacova, I., Keshavarz, B., & Golding, J. F. (2023). Measuring the susceptibility to visually induced motion sickness and its relationship with vertigo, dizziness, migraine, syncope and personality traits. Experimental Brain Research, 241, 1381-1391. https://doi.org/10.1007/s00221-023-06603-y
Mamat, Z., & Anderson, M. C. (2023). Improving mental health by training the suppression of unwanted thoughts. Science Advances, 9(38), Article eadh5292. https://doi.org/10.1126/sciadv.adh5292
Mamat, Z., Levy, D. A., & Bayley, P. J. (2024). Reconsidering thought suppression and ironic processing: Implications for clinical treatment of traumatic memories. Frontiers in Psychology, 15, Article 1496134. https://doi.org/10.3389/fpsyg.2024.1496134
Park, S., Kim, L., Kwon, J., Choi, S. J., & Whang, M. (2022). Evaluation of visual-induced motion sickness from head-mounted display using heartbeat evoked potential: A cognitive load-focused approach. Virtual Reality, 26, 979-1000. https://doi.org/10.1007/s10055-021-00600-8
Rahimzadeh, G., Tay, A., Travica, N., Lacy, K., Mohamed, S., Nahavandi, D., Pławiak, P., Chalak Qazani, M., & Asadi, H. (2023). Nutritional and behavioral countermeasures as medication approaches to relieve motion sickness: A comprehensive review. Nutrients, 15(6), Article 1320. https://doi.org/10.3390/nu15061320
Shen, Z., Liu, X., Li, W., Li, X., & Wang, Q. (2024). Classification of visually induced motion sickness based on phase-locked value functional connectivity matrix and CNN-LSTM. Sensors, 24(12), Article 3936. https://doi.org/10.3390/s24123936
Talsma, T. M. W., & de Winkel, K. N. (2025). The gut feeling in motion sickness. Communications Biology, 8, Article 1497. https://doi.org/10.1038/s42003-025-08958-0
Yang, A. H. X., Kasabov, N., & Cakmak, Y. O. (2022). Machine learning methods for the study of cybersickness: A systematic review. Brain Informatics, 9, Article 24. https://doi.org/10.1186/s40708-022-00172-6
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Copyright (c) 2026 Xiaoli Zhang, Zhicheng Li

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Accepted: 2026-07-15
Published: 2026-07-31








