Recalibrating Interoceptive Prediction in Substance Use Disorders: A Theoretical Framework for Second-Generation Interoceptive Exposure

Substance use disorders (SUDs) are increasingly conceptualized, in part, as disorders of deficient self-regulation associated with interoceptive dysregulation, an impaired ability to process internal bodily signals. During withdrawal or stress, addicted individuals often misinterpret physical sensations (like tachycardia) as urgent, dangerous, and intolerable signals that heighten cravings and reinforce substance use as a quick mechanism for relief. Predictive-coding and neurocognitive models suggest that learned expectations in the insula and anterior cingulate cortex drive these responses. Despite advancements in addiction treatments, high relapse rates indicate that standard therapies often miss the embodied aspects of craving; this article proposes a second-generation interoceptive exposure framework as a clinical adjunct. Adapted from panic disorder treatment, the method uses clinician-guided exposure to feared physical sensations to reshape patient expectations through individualized mapping, prediction-focused learning, and belief reclassification. Future research is needed to evaluate its safety, clinical feasibility, and underlying neurobiological mechanisms.

Substance use disorder, addiction, interoceptive exposure, somatic distress, relapse-prevention

1. American Society of Addiction Medicine. (2020). The ASAM national practice guideline for the treatment of opioid use disorder: 2020 focused update. Journal of Addiction Medicine, 14(2S), 1–91. https://doi.org/10.1097/ADM.0000000000000633 [Google Scholar] [Crossref]

2. Boettcher, H., Brake, C. A., & Barlow, D. H. (2016). Origins and outlook of interoceptive exposure. Journal of Behavior Therapy and Experimental Psychiatry, 53, 41–51. https://doi.org/10.1016/j.jbtep.2015.10.009 [Google Scholar] [Crossref]

3. Cheng, Y.-C., Huang, Y.-C., & Huang, W.-L. (2019). Heart rate variability as a potential biomarker for alcohol use disorders: A systematic review and meta-analysis. Drug and Alcohol Dependence, 204, 107502. [Google Scholar] [Crossref]

4. Craske, M. G., Treanor, M., Conway, C. C., Zbozinek, T., & Vervliet, B. (2014). Maximizing exposure therapy: An inhibitory learning approach. Behaviour Research and Therapy, 58, 10–23. https://doi.org/10.1016/j.brat.2014.04.006 [Google Scholar] [Crossref]

5. Hoffmann, T. C., Glasziou, P. P., Boutron, I., Milne, R., Perera, R., Moher, D., Altman, D. G., Barbour, V., Macdonald, H., Johnston, M., Lamb, S. E., Dixon-Woods, M., McCulloch, P., Wyatt, J. C., Chan, A.-W., & Michie, S. (2014). Better reporting of interventions: Template for intervention description and replication (TIDieR) checklist and guide. BMJ, 348, Article g1687. https://doi.org/10.1136/bmj.g1687 [Google Scholar] [Crossref]

6. Korecki, J. R., Schwebel, F. J., Votaw, V. R., & Witkiewitz, K. (2020). Mindfulness-based programs for substance use disorders: A systematic review of manualized treatments. Substance Abuse Treatment, Prevention, and Policy, 15(1), Article 51. https://doi.org/10.1186/s13011-020-00293-3 [Google Scholar] [Crossref]

7. Linehan, M. M., Schmidt, H., III, Dimeff, L. A., Craft, J. C., Kanter, J., & Comtois, K. A. (1999). Dialectical behavior therapy for patients with borderline personality disorder and drug-dependence. The American Journal on Addictions, 8(4), 279–292. https://doi.org/10.1080/105504999305686 [Google Scholar] [Crossref]

8. May, A. C., et al. (2022). Interoception and substance use disorders: Bridging brain, body, and behavior. Addiction Neuroscience, 3, Article 100017. https://doi.org/10.1016/j.adn.2022.100017 [Google Scholar] [Crossref]

9. Moon, S. J. E., Schlenk, E. A., & Lee, H. (2024). Heart rate variability in adults with substance use disorder: A comprehensive narrative review. Journal of the American Psychiatric Nurses Association, 30(2), 240–251. [Google Scholar] [Crossref]

10. Noël, X., Brevers, D., & Bechara, A. (2013). A neurocognitive approach to understanding the neurobiology of addiction. Current Opinion in Neurobiology, 23(4), 632–638. [Google Scholar] [Crossref]

11. Onken, L. S., Carroll, K. M., Shoham, V., Cuthbert, B. N., & Riddle, M. (2014). Reenvisioning clinical science: Unifying the discipline to improve the public health. Clinical Psychological Science, 2(1), 22–34. https://doi.org/10.1177/2167702613497932 [Google Scholar] [Crossref]

12. Otto, M. W., Hearon, B. A., McHugh, R. K., Calkins, A. W., Pratt, E., Murray, H. W., Safren, S. A., & Pollack, M. H. (2014). A randomized, controlled trial of the efficacy of an interoceptive exposure-based CBT for treatment-refractory outpatients with opioid dependence. Journal of Psychoactive Drugs, 46(5), 402–411. https://doi.org/10.1080/02791072.2014.960110 [Google Scholar] [Crossref]

13. Otto, M. W., Powers, M. B., & Fischmann, D. (2005). Emotional exposure in the treatment of substance use disorders: Conceptual model, evidence, and future directions. Clinical Psychology Review, 25(6), 824–839. https://doi.org/10.1016/j.cpr.2005.05.002 [Google Scholar] [Crossref]

14. Paulus, M. P. (2024). Interoceptive processing in substance use disorders (SUDs): From the neuroanatomy to insights from computational models and predictive coding frameworks. In M. Wittmann, I. A. Strigo, & A. N. Simmons (Eds.), The bodily self, emotion, and subjective time: Exploring interoception through the contributions of AD (Bud) Craig. Springer. [Google Scholar] [Crossref]

15. Paulus, M. P., & Feinstein, J. S. (2019). An active inference approach to interoceptive psychopathology. Annual Review of Clinical Psychology, 15, 97–122. https://doi.org/10.1146/annurev-clinpsy-050718-095617 [Google Scholar] [Crossref]

16. Paulus, M. P., & Stewart, J. L. (2014). Interoception and drug addiction. Neuropharmacology, 76(Part B), 342–350. https://doi.org/10.1016/j.neuropharm.2013.07.002 [Google Scholar] [Crossref]

17. Price, C. J., & Crowell, S. E. (2016). Respiratory sinus arrhythmia as a potential measure in substance use treatment–outcome studies. Addiction, 111(4), 615–625. [Google Scholar] [Crossref]

18. Sinha, R. (2024). Stress and substance use disorders: Risk, relapse, and treatment outcomes. The Journal of Clinical Investigation, 134(16), e172883. [Google Scholar] [Crossref]

19. Smith, R., Badcock, P., & Friston, K. J. (2021). Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry and Clinical Neurosciences, 75(1), 3–13. [Google Scholar] [Crossref]

20. Stotts, A. L., & Northrup, T. F. (2015). The promise of third-wave behavioral therapies in the treatment of substance use disorders. Current Opinion in Psychology, 2, 75–80. [Google Scholar] [Crossref]

• A Comparative Study of Adjustment Level, Parental Income and Academic Achievement of Adolescent Girls
• Increase in STIs among Adolescents in Masvingo District
• A Correlation between SDG 4 (Quality Education) and Emotional Disposition of Teacher Educators in Hyderabad District
• Ear Piercing Behaviour and Self-Esteem among Male University Students in Nigeria
• A Study of Religiosity and Psychological Well-Being