The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Deconstructing the sensation of pain: The influence of cognitive processes on pain perception
Abstract
Phenomena such as placebo analgesia or pain relief through distraction highlight the powerful influence cognitive processes and learning mechanisms have on the way we perceive pain. Although contemporary models of pain acknowledge that pain is not a direct readout of nociceptive input, the neuronal processes underlying cognitive modulation are not yet fully understood. Modern concepts of perception—which include computational modeling to quantify the influence of cognitive processes—suggest that perception is critically determined by expectations and their modification through learning. Research on pain has just begun to embrace this view. Insights into these processes promise to open up new avenues to pain prevention and treatment by harnessing the power of the mind.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
The Cerebral Signature for Pain Perception and Its Modulation
Tracey, I., & Mantyh, P. W. (2007) · Neuron · doi:10.1016/j.neuron.2007.07.012 · Cited by 1,843
Pain demands attention: A cognitive–affective model of the interruptive function of pain.
Eccleston, C., & Crombez, G. (1999) · Psychological Bulletin · doi:10.1037/0033-2909.125.3.356 · Cited by 1,718
An fMRI-Based Neurologic Signature of Physical Pain
Wager, T. D., Atlas, L. Y., Lindquist, M. A., Roy, M., Woo, C.-W., & Kross, E. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1204471 · Cited by 1,597
Corticostriatal functional connectivity predicts transition to chronic back pain
Baliki, M. N., Petre, B., Torbey, S., Herrmann, K. M., Huang, L., Schnitzer, T. J., Fields, H. L., & Apkarian, A. V. (2012) · Nature Neuroscience · doi:10.1038/nn.3153 · Cited by 1,050
Fear-Avoidance Model of Chronic Pain: The Next Generation
Crombez, G., Eccleston, C., Van Damme, S., Vlaeyen, J. W. S., & Karoly, P. (2012) · The Clinical Journal of Pain · doi:10.1097/ajp.0b013e3182385392 · Cited by 960
Activation of the Opioidergic Descending Pain Control System Underlies Placebo Analgesia
Eippert, F., Bingel, U., Schoell, E. D., Yacubian, J., Klinger, R., Lorenz, J., & Büchel, C. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.014 · Cited by 839
Expectation in perceptual decision making: neural and computational mechanisms
Summerfield, C., & de Lange, F. P. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3838 · Cited by 832
A Bayesian account of ‘hysteria’
Edwards, M. J., Adams, R. A., Brown, H., Parees, I., & Friston, K. J. (2012) · Brain · doi:10.1093/brain/aws129 · Cited by 825
Bingel, U., Wanigasekera, V., Wiech, K., Ni Mhuircheartaigh, R., Lee, M. C., Ploner, M., & Tracey, I. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3001244 · Cited by 810
Self-citationThe subjective experience of pain: where expectations become reality
Koyama, T., McHaffie, J. G., Laurienti, P. J., & Coghill, R. C. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408576102 · Cited by 747
Nociception, pain, negative moods, and behavior selection
Baliki, M. N., & Apkarian, A. V. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.005 · Cited by 731
Learning and inference in the brain
Friston, K. (2003) · Neural Networks · doi:10.1016/j.neunet.2003.06.005 · Cited by 724
Kucyi, A., & Davis, K. D. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2014.11.006 · Cited by 587
A multisensory investigation of the functional significance of the “pain matrix”
Mouraux, A., Diukova, A., Lee, M. C., Wise, R. G., & Iannetti, G. D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.084 · Cited by 559
Getting the pain you expect: mechanisms of placebo, nocebo and reappraisal effects in humans
Tracey, I. (2010) · Nature Medicine · doi:10.1038/nm.2229 · Cited by 553
Direct evidence for spinal cord involvement in placebo analgesia
Eippert, F., Finsterbusch, J., Bingel, U., & Büchel, C. (2009) · Science · doi:10.1126/science.1180142 · Cited by 512
Placebo Analgesia: A Predictive Coding Perspective
Büchel, C., Geuter, S., Sprenger, C., & Eippert, F. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.042 · Cited by 505
Atlas, L. Y., & Wager, T. D. (2012) · Neuroscience Letters · doi:10.1016/j.neulet.2012.03.039 · Cited by 463
Opponent appetitive-aversive neural processes underlie predictive learning of pain relief
Seymour, B., O'Doherty, J. P., Koltzenburg, M., Wiech, K., Frackowiak, R., Friston, K., & Dolan, R. (2005) · Nature Neuroscience · doi:10.1038/nn1527 · Cited by 439
Self-citationMind wandering away from pain dynamically engages antinociceptive and default mode brain networks
Kucyi, A., Salomons, T. V., & Davis, K. D. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312902110 · Cited by 406
Keltner, J. R., Furst, A., Fan, C., Redfern, R., Inglis, B., & Fields, H. L. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.4463-05.2006 · Cited by 346
Prefrontal cortex modulates placebo analgesia
Krummenacher, P., Candia, V., Folkers, G., Schedlowski, M., & Schönbächler, G. (2010) · Pain · doi:10.1016/j.pain.2009.09.033 · Cited by 337
Prestimulus functional connectivity determines pain perception in humans
Ploner, M., Lee, M. C., Wiech, K., Bingel, U., & Tracey, I. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906186106 · Cited by 295
Self-citationNonopioid placebo analgesia is mediated by CB1 cannabinoid receptors
Benedetti, F., Amanzio, M., Rosato, R., & Blanchard, C. (2011) · Nature Medicine · doi:10.1038/nm.2435 · Cited by 289
Distinct Brain Systems Mediate the Effects of Nociceptive Input and Self-Regulation on Pain
Woo, C.-W., Roy, M., Buhle, J. T., & Wager, T. D. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002036 · Cited by 263
Frontostriatal Gating of Tinnitus and Chronic Pain
Rauschecker, J. P., May, E. S., Maudoux, A., & Ploner, M. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.08.002 · Cited by 253
Perceived Controllability Modulates the Neural Response to Pain
Salomons, T. V., Johnstone, T., Backonja, M.-M., & Davidson, R. J. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.1315-04.2004 · Cited by 253
Learning about pain: The neural substrate of the prediction error for aversive events
Ploghaus, A., Tracey, I., Clare, S., Gati, J. S., Rawlins, J. N. P., & Matthews, P. M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.160266497 · Cited by 248
Representation of aversive prediction errors in the human periaqueductal gray
Roy, M., Shohamy, D., Daw, N., Jepma, M., Wimmer, G. E., & Wager, T. D. (2014) · Nature Neuroscience · doi:10.1038/nn.3832 · Cited by 246
Perceptual decision neurosciences – A model-based review
Mulder, M. J., van Maanen, L., & Forstmann, B. U. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.07.031 · Cited by 221
Pain, decisions, and actions: a motivational perspective
Wiech, K., & Tracey, I. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00046 · Cited by 188
Self-citationLearning to predict and control harmful events: chronic pain and conditioning
Vlaeyen, J. W. S. (2015) · Pain · doi:10.1097/j.pain.0000000000000107 · Cited by 159
Influence of prior information on pain involves biased perceptual decision-making
Wiech, K., Vandekerckhove, J., Zaman, J., Tuerlinckx, F., Vlaeyen, J. W. S., & Tracey, I. (2014) · Current Biology · doi:10.1016/j.cub.2014.06.022 · Cited by 115
Self-citationMolecular mechanisms of placebo responses in humans
Peciña, M., & Zubieta, J.-K. (2014) · Molecular Psychiatry · doi:10.1038/mp.2014.164 · Cited by 105
Dissociable Learning Processes Underlie Human Pain Conditioning
Zhang, S., Mano, H., Ganesh, G., Robbins, T. W., & Seymour, B. (2015) · Current Biology · doi:10.1016/j.cub.2015.10.066 · Cited by 91
Painful Issues in Pain Prediction
Hu, L., & Iannetti, G. D. (2016) · Trends in Neurosciences · doi:10.1016/j.tins.2016.01.004 · Cited by 81
Kessner, S. S., Forkmann, K., Ritter, C., Wiech, K., Ploner, M., & Bingel, U. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109014 · Cited by 59
Self-citationCrombez, G., & Wiech, K. (2011) · Pain · doi:10.1016/j.pain.2011.02.028 · Cited by 27
Self-citationTowards a taxonomy of pain modulations
Ploner, M., Bingel, U., & Wiech, K. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.02.007 · Cited by 12
Self-citation
Cited by
Symptoms and the body: taking the inferential leap
Van den Bergh, O., Witthöft, M., Petersen, S., & Brown, R. J. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.01.015 · Cited by 543
Endogenous and Exogenous Opioids in Pain
Corder, G., Castro, D. C., Bruchas, M. R., & Scherrer, G. (2018) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061522 · Cited by 502
Vlaeyen, J. W. S., Maher, C. G., Wiech, K., Van Zundert, J., Meloto, C. B., Diatchenko, L., Battié, M. C., Goossens, M., Koes, B., & Linton, S. J. (2018) · Nature Reviews Disease Primers · doi:10.1038/s41572-018-0052-1 · Cited by 497
Self-citationPloner, M., Sorg, C., & Gross, J. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.12.001 · Cited by 434
Management of functional somatic syndromes and bodily distress
Henningsen, P., Zipfel, S., Sattel, H., & Creed, F. (2018) · Psychotherapy and Psychosomatics · doi:10.1159/000484413 · Cited by 335
Metadata
- Authors
- Wiech, K.
- Venue
- Science
- Published
- 2016-11-04
- Volume
- 354
- Issue
- 6312
- Pages
- 584-587
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2548633651
- Cited by
- 482
- References
- 41
- FWCI
- 23.11
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
