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Behavioural and neural evidence for self-reinforcing expectancy effects on pain
Open accessAbstract
Beliefs and expectations often persist despite evidence to the contrary. Here we examine two potential mechanisms underlying such 'self-reinforcing' expectancy effects in the pain domain: modulation of perception and biased learning. In two experiments, cues previously associated with symbolic representations of high or low temperatures preceded painful heat. We examined trial-to-trial dynamics in participants' expected pain, reported pain and brain activity. Subjective and neural pain responses assimilated towards cue-based expectations, and pain responses in turn predicted subsequent expectations, creating a positive dynamic feedback loop. Furthermore, we found evidence for a confirmation bias in learning: higher- and lower-than-expected pain triggered greater expectation updating for high- and low-pain cues, respectively. Individual differences in this bias were reflected in the updating of pain-anticipatory brain activity. Computational modelling provided converging evidence that expectations influence both perception and learning. Together, perceptual assimilation and biased learning promote self-reinforcing expectations, helping to explain why beliefs can be resistant to change.
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“Studies of confirmation bias in direct perceptual experiences like pain are more rare, but early evidence indicates biases in pain perception caused by predictive cues can short-circuit learning, creating positive feedback loops between predictions and experiences that result in resistance to disconfirming evidence (26).”
From Social information creates self-fulfilling prophecies in judgments of pain, vicarious pain, and cognitive effort (2026) · cites this paper as [26]
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“Even cues conditioned to conceptual representations of reinforcers (e.g., images of thermometers indicating high or low pain) can elicit enduring effects in some cases (26).”
From Social information creates self-fulfilling prophecies in judgments of pain, vicarious pain, and cognitive effort (2026) · cites this paper as [26]
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Corticolimbic Circuitry in Chronic Pain Tracks Pain Intensity Relief Following Exposure In Vivo
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Κube, T., Körfer, K., Riecke, J., & Glombiewski, J. A. (2022) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2022.110807 · Cited by 7
Electrophysiological indices of pain expectation abnormalities in fibromyalgia patients
Barjola, P., Peláez, I., Ferrera, D., González, J. L., Furlong, L. V., Peñacoba, C., López‐López, A., Fernandes-Magalhaes, R., & Mercado, F. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.943976 · Cited by 7
“Therefore, deficiencies in associative learning could become the context unstable and unpredictable, making the anticipation of pain for adequate coping very difficult, and could play an important role in the development and maintenance of chronic pain syndromes (Jepma et al., 2018; Zhang et al., 2019).”
BackgroundLeu, C., Glineur, E., & Liberati, G. (2024) · Royal Society Open Science · doi:10.1098/rsos.240626 · Cited by 7
“To further understand the modulatory effects of expectation on pain perception, recent investigations studied its effects on ongoing neural oscillations.”
BackgroundThe placebo effect: To explore or to exploit?
Barnes, K., Rottman, B. M., & Colagiuri, B. (2021) · Cognition · doi:10.1016/j.cognition.2021.104753 · Cited by 6
“This is consistent with the placebo literature (Anchisi & Zanon, 2015; Büchel et al., 2014; Jepma et al., 2018; Wiech, 2016 - but see Au Yeung et al., 2014, and Colagiuri & Quinn, 2018, for exceptions), but previous research has not been designed to explore the association during conditioning, i.e.…”
BackgroundResultZika, O., Appel, J. E., Klinge, C., Shkreli, L., Browning, M. D., Wiech, K., & Reinecke, A. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.01.020 · Cited by 6
“Consistent with similar studies (19,28,32), we employed a modeling framework based on reinforcement learning (51).”
Alaiti, R. K., Zuccolo, P. F., Hunziker, M. H. L., Cañeiro, J. P., Vlaeyen, J. W. S., & Costa, M. F. (2020) · Pain · doi:10.1097/j.pain.0000000000001919 · Cited by 6
Finan, P. H., & Colloca, L. (2025) · Pain · doi:10.1097/j.pain.0000000000003654 · Cited by 5
Nguyen-Phuong-Mai, M. (2020) · Frontiers in Communication · doi:10.3389/fcomm.2020.00014 · Cited by 8
“Researchers reported that the expectation of pain, even when the stimulus isn’t painful, can lead us to believe that we are actually hurt (Jepma et al., 2018).”
BackgroundGallego‐Sendarrubias, G. M., Voogt, L. P., Arias‐Buría, J. L., BRAITHWAITE, F., & Fernández‐de‐las‐Peñas, C. (2022) · Musculoskeletal Science and Practice · doi:10.1016/j.msksp.2022.102515 · Cited by 5
How the magnitude and precision of pain predictions shape pain experiences
Derksen, S. M. J. C., Konttinen, M., Myronenko, A., Seymour, B., & Peerdeman, K. J. (2024) · European Journal of Pain · doi:10.1002/ejp.4769 · Cited by 5
“…can be influenced by many factors, including previous experiences, such as the predictions and stimuli during preceding trials in this study (Jepma et al., 2018; Pavy, Zaman, Von Leupoldt, & Torta, 2024), anxiety about or fear of the painful procedure, and attentional focus (Bushnell et…”
Wolf, M., Wittkamp, C. A., & Rose, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-77693-z · Cited by 4
“Based on previous findings, we expected to find persisting placebo and nocebo effects after one week on the behavioral level 5,8,9,19 .”
InfluentialBayesian surprise intensifies pain in a novel visual-noxious association
Ishikawa, R., Ono, G., & Izawa, J. (2025) · Cognition · doi:10.1016/j.cognition.2025.106064 · Cited by 5
“The RL-based model for updating pain perception posits that it trains the prediction of pain rather than the perception itself (Jepma et al., 2018; Mancini et al., 2022; Taylor et al., 2017).”
Neurophysiological encoding of aversive prediction errors
Gopalakrishnan, R., Sonneborn, C., Baillet, S., Machado, A. G., Wager, T. D., & Roy, M. (2025) · Pain · doi:10.1097/j.pain.0000000000003712 · Cited by 4
“4,25,39 The brain signals that correlated with our definition of PE does not imply causation, which is a general limitation inherent to all studies that infer learning-related processes from neural activity alone.”
Self-citationExposure to false cardiac feedback alters pain perception and anticipatory cardiac frequency
Parrotta, E., Bach, P., Pezzulo, G., Zaccaro, A., Perrucci, M. G., Costantini, M., & Ferri, F. (2026) · eLife · doi:10.7554/elife.90013.3 · Cited by 4
Learning the statistics of pain: computational and neural mechanismsPreprint
Mancini, F., Zhang, S., & Seymour, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.21.465270 · Cited by 4
“The Bayesian frequency model is consistent with many other tasks that involve cognitive model learning or acquisition of explicit contingency knowledge across modalities, including pain (Yoshida et al., 2013; Jepma et al., 2018; Hoskin et al., 2019).”
BackgroundExpectation generation and its effect on subsequent pain and visual perception
Botvinik‐Nezer, R., Geuter, S., Lindquist, M. A., & Wager, T. D. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013053 · Cited by 3
“However, this persistence has been replicated in multiple studies [10,63,65] and may be explained by a ‘self-reinforcing’ feedback loop detailed in Jepma et al. 2018 [65], whereby initial expectations influence both stimulus processing and learning to create persistent effects of initial beliefs.”
InfluentialSelf-citationRethinking coping in chronic pain: a systematic review and dynamic interaction model of coping
Blandhol, M., Lee, S. Y., & Ellingsen, D. (2026) · PAIN Reports · doi:10.1097/pr9.0000000000001421 · Cited by 4
“62 Expectations (ie, sensory or temporal predictability of pain) are therefore an important part of pain perception and coping.”
Brain rhythms of depression: A predictive processing perspective
Strube, A., & Pizzagalli, D. A. (2026) · Trends in Neurosciences · doi:10.1016/j.tins.2026.06.006 · Cited by 3
Hausteiner-Wiehle, C., & Henningsen, P. (2024) · PPmP. Psychotherapie, Psychosomatik, medizinische Psychologie · doi:10.1055/a-2564-6206 · Cited by 3
Novick, A. M., Stoddard, J., Johnson, R. L., Duffy, K. A., Berkowitz, L. N., Costa, V. D., Sammel, M. D., & Epperson, C. N. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279764 · Cited by 4
“Participants were required to rate their expectation and experience of reward for three types of images (erotic, pleasant, and neutral) in a reward task adapted from Jepma et al [30] and Willroth et al [31].”
MethodPredictability of placebo responses and their clinical implications
Branco, P., Vachon‐Presseau, É., Hall, K. T., Wager, T. D., & Apkarian, A. V. (2023) · Oxford University Press eBooks · doi:10.1093/med/9780197645444.003.0005 · Cited by 3
Self-citationNeural mechanisms underlying placebo and nocebo effects in tonic muscle pain
Chen, M., Wu, X. X., Zhang, L., Zhang, F., Li, L., Zhang, Y., Xiong, D., Qiu, Y., Hu, L., & Xiao, W. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120877 · Cited by 3
“By contrast, once established, placebo effects can also persist due to self-reinforcing expectations (Jepma et al., 2018), making it difficult to extinguish.”
Mahajan, P., Dayan, P., & Seymour, B. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013538 · Cited by 4
Ounjai, K., Suppaso, L., Hohwy, J., & Lauwereyns, J. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.569078 · Cited by 3
Li, J., Chen, S., Zhang, L., Weng, L., Lin, X., Tu, Y., & Peng, W. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003675 · Cited by 2
“Second, we examined external validity by applying the same computational approach to two independent datasets from Jepma and colleagues 2018 [3], where participants provided trial-by-trial expectation ratings, allowing direct comparison between model-derived and subjective expectations.”
InfluentialReproducible, Generalizable Brain Models of Affective Processes
Kragel, P. A., & Wager, T. D. (2019) · Nebraska Symposium on Motivation/The Nebraska symposium on motivation · doi:10.1007/978-3-030-27473-3_8 · Cited by 3
Self-citationUnderstanding the Neurocomputational Mechanisms of Antidepressant Placebo Effects
Peciña, M., Dombrovski, A. Y., Price, R. B., & Karim, H. T. (2021) · Journal of Psychiatry and Brain Science · doi:10.20900/jpbs.20210001 · Cited by 2
“While recent evidence suggests that RL theories play a significant role in placebo analgesia [11,12], similar theories have never been tested in the field of antidepressant placebo effects, and promise to transform how we understand, enhance, inhibit and control for antidepressant placebo effects.”
Background
Metadata
- Authors
- Jepma, M., Koban, L., van Doorn, J., Jones, M., & Wager, T. D.
- Affiliations
- Leiden University (NL)
- University of Colorado Boulder (US)
- Dartmouth College (US)
- Corresponding author
- Jepma, Marieke
- Venue
- Nature Human Behaviour
- Published
- 2018-10-29
- Volume
- 2
- Issue
- 11
- Pages
- 838-855
- ISSN
- 2397-3374 (ISSN-L)
- PMC
- PMC6768437
- OpenAlex
- W2898173573
- Cited by
- 155
- References
- 130
- Citations by year
- 2026: 19 · 2025: 21 · 2024: 29 · 2023: 25 · 2022: 19 · 2021: 20 · 2020: 11 · 2019: 10 · 2018: 1
- FWCI
- 6.25
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH076136)
- National Institute on Drug Abuse (R01DA027794)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Pain Management and Placebo Effect
- Health Sciences › Medicine › Physiology (medical) › Pain Mechanisms and Treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- pain anticipation, confirmation bias, biased learning, pain perception, expectation updating, belief updating, thermal pain, computational modelling, individual differences
- MeSH terms
- Adult, Brain, Brain Mapping, Culture, Female, Humans, Learning, Male, Motivation, Neuropsychological Tests, Pain, Psychological Theory, Reinforcement, Psychology, Pain Perception
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