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Behavioural and neural evidence for self-reinforcing expectancy effects on pain

Abstract

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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Fig. 1: Experimental design and behavioural results.
Fig. 2: Cue effects on heat-evoked brain activity.
Fig. 3: Bidirectional effects of expectations and pain on one another.
Fig. 4: Confirmation bias in expectation updating.
Fig. 5: Computational models capturing effects of cue-based expectations on pain and confirmation bias on expectation updating.
Fig. 6: Posterior distributions for the group-level means of the models’ parameters.
Fig. 7: Confirmation bias in the updating of pain-anticipatory brain activity.

Data availability

The single-trial behavioural and NPS data, which are needed to reproduce all behavioural and NPS analyses in the paper, are available through the Open Science Framework repository, https://osf.io/bqkz3/. The fMRI data, which are needed to reproduce the analyses on anticipatory brain activity, are available from the corresponding author upon request.

References

  1. 1.

    Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, Cambridge, 1998).

  2. 2.

    Pavlov, I. P. Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex (Dover Publications, New York, 1960).

  3. 3.

    Benedetti, F. Placebo effects: from the neurobiological paradigm to translational implications. Neuron 84, 623–637 (2014).

    CAS  PubMed  Google Scholar 

  4. 4.

    Benedetti, F., Carlino, E. & Pollo, A. How placebos change the patient’s brain. Neuropsychopharmacology 36, 339–354 (2011).

    PubMed  Google Scholar 

  5. 5.

    Colloca, L. & Benedetti, F. Placebos and painkillers: is mind as real as matter? Nat. Rev. Neurosci. 6, 545–552 (2005).

    CAS  PubMed  Google Scholar 

  6. 6.

    Wager, T. D. & Atlas, L. Y. The neuroscience of placebo effects: connecting context, learning and health. Nat. Rev. Neurosci. 16, 403–418 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Oken, B. S. Placebo effects: clinical aspects and neurobiology. Brain 131, 2812–2823 (2008).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Price, D. D., Finniss, D. G. & Benedetti, F. A comprehensive review of the placebo effect: recent advances and current thought. Annu. Rev. Psychol. 59, 565–590 (2008).

    PubMed  Google Scholar 

  9. 9.

    Walsh, B. T., Seidman, S. N., Sysko, R. & Gould, M. Placebo response in studies of major depression: variable, substantial, and growing. J. Am. Med. Assoc. 287, 1840–1847 (2002).

    Google Scholar 

  10. 10.

    Sterzer, P., Frith, C. & Petrovic, P. Believing is seeing: expectations alter visual awareness. Curr. Biol. 18, R697–R698 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Summerfield, C. & Egner, T. Expectation (and attention) in visual cognition. Trends. Cogn. Sci. 13, 403–409 (2009).

    PubMed  Google Scholar 

  12. 12.

    Gilbert, C. D. & Li, W. Top-down influences on visual processing. Nat. Rev. Neurosci. 14, 350–363 (2013).

    CAS  Google Scholar 

  13. 13.

    Nitschke, J. B. et al. Altering expectancy dampens neural response to aversive taste in primary taste cortex. Nat. Neurosci. 9, 435–442 (2006).

    CAS  PubMed  Google Scholar 

  14. 14.

    Rao, R. P. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79–87 (1999).

    CAS  PubMed  Google Scholar 

  15. 15.

    Srinivasan, M. V., Laughlin, S. B. & Dubs, A. Predictive coding: a fresh view of inhibition in the retina. Proc. R. Soc. Lond. B. Biol. Sci. 216, 427–459 (1982).

    CAS  PubMed  Google Scholar 

  16. 16.

    Buchel, C., Geuter, S., Sprenger, C. & Eippert, F. Placebo analgesia: a predictive coding perspective. Neuron 81, 1223–1239 (2014).

    PubMed  Google Scholar 

  17. 17.

    Friston, K. & Kiebel, S. Predictive coding under the free-energy principle. Phil. Trans. R. Soc. Lond. B 364, 1211–1221 (2009).

    Google Scholar 

  18. 18.

    Friston, K. A theory of cortical responses. Phil. Trans. R. Soc. Lond. B 360, 815–836 (2005).

    Google Scholar 

  19. 19.

    Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204 (2013).

    PubMed  Google Scholar 

  20. 20.

    Merton, R. K. The self-fulfilling prophecy. Antioch Rev. 8, 193–210 (1948).

    Google Scholar 

  21. 21.

    Wager, T. D., Scott, D. J. & Zubieta, J. K. Placebo effects on human mu-opioid activity during pain. Proc. Natl Acad. Sci. USA 104, 11056–11061 (2007).

    CAS  PubMed  Google Scholar 

  22. 22.

    Wiech, K. Deconstructing the sensation of pain: the influence of cognitive processes on pain perception. Science 354, 584–587 (2016).

    CAS  PubMed  Google Scholar 

  23. 23.

    Atlas, L. Y. & Wager, T. D. How expectations shape pain. Neurosci. Lett. 520, 140–148 (2012).

    CAS  PubMed  Google Scholar 

  24. 24.

    Montgomery, G. H. & Kirsch, I. Classical conditioning and the placebo effect. Pain 72, 107–113 (1997).

    CAS  PubMed  Google Scholar 

  25. 25.

    Atlas, L. Y., Bolger, N., Lindquist, M. A. & Wager, T. D. Brain mediators of predictive cue effects on perceived pain. J. Neurosci. 30, 12964–12977 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Colloca, L., Petrovic, P., Wager, T. D., Ingvar, M. & Benedetti, F. How the number of learning trials affects placebo and nocebo responses. Pain 151, 430–439 (2010).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Jepma, M. & Wager, T. D. Conceptual conditioning: mechanisms mediating conditioning effects on pain. Psychol. Sci. 26, 1728–1739 (2015).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Koban, L. & Wager, T. D. Beyond conformity: social influences on pain reports and physiology. Emotion 16, 24–32 (2016).

    PubMed  Google Scholar 

  29. 29.

    Vase, L., Norskov, K. N., Petersen, G. L. & Price, D. D. Patients’ direct experiences as central elements of placebo analgesia. Phil. Trans. R. Soc. Lond. B 366, 1913–1921 (2011).

    Google Scholar 

  30. 30.

    Vase, L., Robinson, M. E., Verne, G. N. & Price, D. D. Increased placebo analgesia over time in irritable bowel syndrome (IBS) patients is associated with desire and expectation but not endogenous opioid mechanisms. Pain 115, 338–347 (2005).

    PubMed  Google Scholar 

  31. 31.

    Craggs, J. G., Price, D. D., Perlstein, W. M., Verne, G. N. & Robinson, M. E. The dynamic mechanisms of placebo induced analgesia: evidence of sustained and transient regional involvement. Pain 139, 660–669 (2008).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Rescorla, R. A. & Wagner, A. R. in Classical Conditioning II: Current Research and Theory (eds Black, A. H. & Prokasy, W. F.) 64–99 (Appleton-Century-Crofts, New York, 1972).

  33. 33.

    Eippert, F., Finsterbusch, J., Bingel, U. & Buchel, C. Direct evidence for spinal cord involvement in placebo analgesia. Science 326, 404 (2009).

    CAS  PubMed  Google Scholar 

  34. 34.

    Geuter, S. & Buchel, C. Facilitation of pain in the human spinal cord by nocebo treatment. J. Neurosci. 33, 13784–13790 (2013).

    CAS  PubMed  Google Scholar 

  35. 35.

    Plassmann, H., O’Doherty, J., Shiv, B. & Rangel, A. Marketing actions can modulate neural representations of experienced pleasantness. Proc. Natl Acad. Sci. USA 105, 1050–1054 (2008).

    CAS  PubMed  Google Scholar 

  36. 36.

    Doll, B. B., Hutchison, K. E. & Frank, M. J. Dopaminergic genes predict individual differences in susceptibility to confirmation bias. J. Neurosci. 31, 6188–6198 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Doll, B. B., Jacobs, W. J., Sanfey, A. G. & Frank, M. J. Instructional control of reinforcement learning: a behavioral and neurocomputational investigation. Brain Res. 1299, 74–94 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Biele, G., Rieskamp, J., Krugel, L. K. & Heekeren, H. R. The neural basis of following advice. PLoS Biol. 9, e1001089 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Li, J., Delgado, M. R. & Phelps, E. A. How instructed knowledge modulates the neural systems of reward learning. Proc. Natl Acad. Sci. USA 108, 55–60 (2011).

    CAS  PubMed  Google Scholar 

  40. 40.

    Staudinger, M. R. & Buchel, C. How initial confirmatory experience potentiates the detrimental influence of bad advice. Neuroimage 76, 125–133 (2013).

    PubMed  Google Scholar 

  41. 41.

    Biele, G., Rieskamp, J. & Gonzalez, R. Computational models for the combination of advice and individual learning. Cogn. Sci. 33, 206–242 (2009).

    PubMed  Google Scholar 

  42. 42.

    Apkarian, A. V., Bushnell, M. C., Treede, R. D. & Zubieta, J. K. Human brain mechanisms of pain perception and regulation in health and disease. Eur. J. Pain 9, 463–484 (2005).

    PubMed  Google Scholar 

  43. 43.

    Peyron, R., Laurent, B. & Garcia-Larrea, L. Functional imaging of brain responses to pain. Neurophysiol. Clin. 30, 263–288 (2000).

    CAS  PubMed  Google Scholar 

  44. 44.

    Coghill, R. C. et al. Distributed processing of pain and vibration by the human brain. J. Neurosci. 14, 4095–4108 (1994).

    CAS  PubMed  Google Scholar 

  45. 45.

    Rainville, P., Bushnell, M. C. & Duncan, G. H. Representation of acute and persistent pain in the human CNS: potential implications for chemical intolerance. Ann. N. Y. Acad. Sci. 933, 130–141 (2001).

    CAS  PubMed  Google Scholar 

  46. 46.

    Mazzola, L., Isnard, J., Peyron, R., Guenot, M. & Mauguiere, F. Somatotopic organization of pain responses to direct electrical stimulation of the human insular cortex. Pain 146, 99–104 (2009).

    CAS  PubMed  Google Scholar 

  47. 47.

    Johansen, J. P., Fields, H. L. & Manning, B. H. The affective component of pain in rodents: direct evidence for a contribution of the anterior cingulate cortex. Proc. Natl Acad. Sci. USA 98, 8077–8082 (2001).

    CAS  PubMed  Google Scholar 

  48. 48.

    Johansen, J. P. & Fields, H. L. Glutamatergic activation of anterior cingulate cortex produces an aversive teaching signal. Nat. Neurosci. 7, 398–403 (2004).

    CAS  PubMed  Google Scholar 

  49. 49.

    Woo, C. W., Roy, M., Buhle, J. T. & Wager, T. D. Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain. PLoS Biol. 13, e1002036 (2015).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Lopez-Sola, M. et al. Towards a neurophysiological signature for fibromyalgia. Pain 158, 34–47 (2017).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Lindquist, M. A. et al. Group-regularized individual prediction: theory and application to pain. Neuroimage 145, 274–287 (2017).

    PubMed  Google Scholar 

  52. 52.

    Krishnan, A. et al. Somatic and vicarious pain are represented by dissociable multivariate brain patterns. eLife 5, e15166 (2016).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Wager, T. D. et al. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368, 1388–1397 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Tabor, A., Thacker, M. A., Moseley, G. L. & Kording, K. P. Pain: a statistical account. PLoS Comput. Biol. 13, e1005142 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Anchisi, D. & Zanon, M. A Bayesian perspective on sensory and cognitive integration in pain perception and placebo analgesia. PLoS ONE 10, e0117270 (2015).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Grahl, A., Onat, S. & Buchel, C. The periaqueductal gray and Bayesian integration in placebo analgesia. eLife 7, e32930 (2018).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Dayan, P. & Kakade, S. in Advances in Neural Information Processing Systems Vol. 13 (eds Dietterich, T. G., Leen, T. K. & Tresp, V.) 451–457 (MIT Press, Cambridge, 2000).

  58. 58.

    Kalman, R. E. A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960).

    Google Scholar 

  59. 59.

    Koyama, T., McHaffie, J. G., Laurienti, P. J. & Coghill, R. C. The subjective experience of pain: where expectations become reality. Proc. Natl Acad. Sci. USA 102, 12950–12955 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Wager, T. D., Atlas, L. Y., Leotti, L. A. & Rilling, J. K. Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. J. Neurosci. 31, 439–452 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Porro, C. A. et al. Does anticipation of pain affect cortical nociceptive systems? J. Neurosci. 22, 3206–3214 (2002).

    CAS  PubMed  Google Scholar 

  62. 62.

    Lin, C. S., Hsieh, J. C., Yeh, T. C., Lee, S. Y. & Niddam, D. M. Functional dissociation within insular cortex: the effect of pre-stimulus anxiety on pain. Brain Res. 1493, 40–47 (2013).

    CAS  PubMed  Google Scholar 

  63. 63.

    Rissman, J., Gazzaley, A. & D’Esposito, M. Measuring functional connectivity during distinct stages of a cognitive task. Neuroimage 23, 752–763 (2004).

    PubMed  Google Scholar 

  64. 64.

    Mumford, J. A., Davis, T. & Poldrack, R. A. The impact of study design on pattern estimation for single-trial multivariate pattern analysis. Neuroimage 103, 130–138 (2014).

    PubMed  Google Scholar 

  65. 65.

    Rosenthal, R. & Jacobson, L. Pygmalion in the Classroom; Teacher Expectation and Pupils’ Intellectual Development (Holt, New York, 1968).

  66. 66.

    Bonte, M., Parviainen, T., Hytonen, K. & Salmelin, R. Time course of top-down and bottom-up influences on syllable processing in the auditory cortex. Cereb. Cortex 16, 115–123 (2006).

    PubMed  Google Scholar 

  67. 67.

    Firestone, C. & Scholl, B. J. Cognition does not affect perception: evaluating the evidence for ‘top-down’ effects. Behav. Brain Sci. 39, e229 (2016).

    PubMed  Google Scholar 

  68. 68.

    Ma, Y. et al. Serotonin transporter polymorphism alters citalopram effects on human pain responses to physical pain. Neuroimage 135, 186–196 (2016).

    CAS  PubMed  Google Scholar 

  69. 69.

    Brascher, A. K., Becker, S., Hoeppli, M. E. & Schweinhardt, P. Different brain circuitries mediating controllable and uncontrollable pain. J. Neurosci. 36, 5013–5025 (2016).

    CAS  PubMed  Google Scholar 

  70. 70.

    Woo, C. W. et al. Quantifying cerebral contributions to pain beyond nociception. Nat. Commun. 8, 14211 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Becker, S., Gandhi, W., Pomares, F., Wager, T. D. & Schweinhardt, P. Orbitofrontal cortex mediates pain inhibition by monetary reward. Soc. Cogn. Affect. Neurosci. 12, 651–661 (2017).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Jones, E. E. Attribution: Perceiving the Causes of Behavior (General Learning Press, Morristown, 1972).

  73. 73.

    Weiner, B. An Attributional Theory of Motivation and Emotion (Springer-Verlag, New York, 1986).

    Google Scholar 

  74. 74.

    Huber, P. J. Robust Statistics (Wiley, New York, 1981).

    Google Scholar 

  75. 75.

    Landy, M. S., Maloney, L. T., Johnston, E. B. & Young, M. Measurement and modeling of depth cue combination: in defense of weak fusion. Vision Res. 35, 389–412 (1995).

    CAS  PubMed  Google Scholar 

  76. 76.

    de Gardelle, V. & Summerfield, C. Robust averaging during perceptual judgment. Proc. Natl Acad. Sci. USA 108, 13341–13346 (2011).

    PubMed  Google Scholar 

  77. 77.

    Clark, W. C. & Yang, J. C. Acupunctural analgesia? Evaluation by signal detection theory. Science 184, 1096–1098 (1974).

    CAS  PubMed  Google Scholar 

  78. 78.

    Clark, W. C. Sensory-decision theory analysis of the placebo effect on the criterion for pain and thermal sensitivity. J. Abnorm. Psychol. 74, 363–371 (1969).

    CAS  PubMed  Google Scholar 

  79. 79.

    Wiech, K. et al. Influence of prior information on pain involves biased perceptual decision-making. Curr. Biol. 24, R679–R681 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Lavin, M. J. Establishment of flavor-flavor associations using a sensory preconditioning training procedure. Learn. Motiv. 7, 173–183 (1976).

    Google Scholar 

  81. 81.

    Rizley, R. C. & Rescorla, R. A. Associations in second-order conditioning and sensory preconditioning. J. Comp. Physiol. Psychol. 81, 1–11 (1972).

    CAS  PubMed  Google Scholar 

  82. 82.

    White, K. & Davey, G. C. Sensory preconditioning and UCS inflation in human ‘fear’ conditioning. Behav. Res. Ther. 27, 161–166 (1989).

    CAS  PubMed  Google Scholar 

  83. 83.

    Wimmer, G. E. & Shohamy, D. Preference by association: how memory mechanisms in the hippocampus bias decisions. Science 338, 270–273 (2012).

    CAS  PubMed  Google Scholar 

  84. 84.

    Coppens, E., Spruyt, A., Vandenbulcke, M., Van Paesschen, W. & Vansteenwegen, D. Classically conditioned fear responses are preserved following unilateral temporal lobectomy in humans when concurrent US-expectancy ratings are used. Neuropsychologia 47, 2496–2503 (2009).

    PubMed  Google Scholar 

  85. 85.

    Atlas, L. Y., Doll, B. B., Li, J., Daw, N. D. & Phelps, E. A. Instructed knowledge shapes feedback-driven aversive learning in striatum and orbitofrontal cortex, but not the amygdala. eLife 5, e15192 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Yang, H. et al. Striatal-limbic activation is associated with intensity of anticipatory anxiety. Psychiat. Res. 204, 123–131 (2012).

    Google Scholar 

  87. 87.

    Roy, M. et al. Representation of aversive prediction errors in the human periaqueductal gray. Nat. Neurosci. 17, 1607–1612 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Seymour, B. et al. Temporal difference models describe higher-order learning in humans. Nature 429, 664–667 (2004).

    CAS  PubMed  Google Scholar 

  89. 89.

    O’Doherty, J. P. Contributions of the ventromedial prefrontal cortex to goal-directed action selection. Ann. N. Y. Acad. Sci. 1239, 118–129 (2011).

    PubMed  Google Scholar 

  90. 90.

    Bartra, O., McGuire, J. T. & Kable, J. W. The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage 76, 412–427 (2013).

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Hare, T. A., Camerer, C. F. & Rangel, A. Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648 (2009).

    CAS  PubMed  Google Scholar 

  92. 92.

    Flor, H. New developments in the understanding and management of persistent pain. Curr. Opin. Psychiatry 25, 109–113 (2012).

    PubMed  Google Scholar 

  93. 93.

    Soderlund, A. The role of educational and learning approaches in rehabilitation of whiplash-associated disorders in lessening the transition to chronicity. Spine 36, S280–S285 (2011).

    PubMed  Google Scholar 

  94. 94.

    Mansour, A. R., Farmer, M. A., Baliki, M. N. & Apkarian, A. V. Chronic pain: the role of learning and brain plasticity. Restor. Neurol. Neurosci. 32, 129–139 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Apkarian, A. V. Pain perception in relation to emotional learning. Curr. Opin. Neurobiol. 18, 464–468 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Colloca, L. & Benedetti, F. How prior experience shapes placebo analgesia. Pain 124, 126–133 (2006).

    PubMed  Google Scholar 

  97. 97.

    Andre-Obadia, N., Magnin, M. & Garcia-Larrea, L. On the importance of placebo timing in rTMS studies for pain relief. Pain 152, 1233–1237 (2011).

    PubMed  Google Scholar 

  98. 98.

    Zunhammer, M. et al. The effects of treatment failure generalize across different routes of drug administration. Sci. Transl. Med. 9, eaal2999 (2017).

    PubMed  Google Scholar 

  99. 99.

    Kessner, S., Wiech, K., Forkmann, K., Ploner, M. & Bingel, U. The effect of treatment history on therapeutic outcome: an experimental approach. J. Am. Med. Assoc. Intern. Med. 173, 1468–1469 (2013).

    Google Scholar 

  100. 100.

    Jenewein, J. et al. Fear-learning deficits in subjects with fibromyalgia syndrome? Eur. J. Pain 17, 1374–1384 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Meulders, A. et al. Contingency learning deficits and generalization in chronic unilateral hand pain patients. J. Pain. 15, 1046–1056 (2014).

    PubMed  Google Scholar 

  102. 102.

    Zaman, J., Vlaeyen, J. W., Van Oudenhove, L., Wiech, K. & Van Diest, I. Associative fear learning and perceptual discrimination: a perceptual pathway in the development of chronic pain. Neurosci. Biobehav. Rev. 51, 118–125 (2015).

    PubMed  Google Scholar 

  103. 103.

    Browning, M., Behrens, T. E., Jocham, G., O’Reilly, J. X. & Bishop, S. J. Anxious individuals have difficulty learning the causal statistics of aversive environments. Nat. Neurosci. 18, 590–596 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Koban, L. et al. Social anxiety is characterized by biased learning about performance and the self. Emotion 17, 1144–1155 (2017).

    PubMed  Google Scholar 

  105. 105.

    Rutledge, R. B., Skandali, N., Dayan, P. & Dolan, R. J. A computational and neural model of momentary subjective well-being. Proc. Natl Acad. Sci. USA 111, 12252–12257 (2014).

    CAS  PubMed  Google Scholar 

  106. 106.

    Eldar, E. & Niv, Y. Interaction between emotional state and learning underlies mood instability. Nat. Commun. 6, 6149 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Jepma, M., Jones, M. & Wager, T. D. The dynamics of pain: evidence for simultaneous site-specific habituation and site-nonspecific sensitization in thermal pain. J. Pain. 15, 734–746 (2014).

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Wager, T. D. et al. Brain mediators of cardiovascular responses to social threat. Part II: Prefrontal-subcortical pathways and relationship with anxiety. Neuroimage 47, 836–851 (2009).

    PubMed  PubMed Central  Google Scholar 

  109. 109.

    Wager, T. D. et al. Brain mediators of cardiovascular responses to social threat. Part I: Reciprocal dorsal and ventral sub-regions of the medial prefrontal cortex and heart-rate reactivity. Neuroimage 47, 821–835 (2009).

    PubMed  PubMed Central  Google Scholar 

  110. 110.

    Daw, N. D., O’Doherty, J. P., Dayan, P., Seymour, B. & Dolan, R. J. Cortical substrates for exploratory decisions in humans. Nature 441, 876–879 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Zajkowski, W. K., Kossut, M. & Wilson, R. C.A. A causal role for right frontopolar cortex in directed, but not random, exploration. eLife 6, e27430 (2017).

    PubMed  PubMed Central  Google Scholar 

  112. 112.

    Jones, M., Curran, T., Mozer, M. C. & Wilder, M. H. Sequential effects in response time reveal learning mechanisms and event representations. Psychol. Rev. 120, 628–666 (2013).

    PubMed  Google Scholar 

  113. 113.

    Sutton, R. S. Gain adaptation beats least squares? In Proc. 7th Yale Workshop on Adaptive and Learning Systems 161–166 (1992); https://pdfs.semanticscholar.org/7ec8/876f219b3b3d5c894a3f395c89c382029cc5.pdf

  114. 114.

    Yu, A. & Cohen, J. in Advances in Neural Information Processing Systems Vol. 22 (eds Bengio, Y. et al.) 1873–1880 (NIPS Foundation, La Jolla, 2009).

  115. 115.

    Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116.

    Jacobs, R. A. Optimal integration of texture and motion cues to depth. Vision Res. 39, 3621–3629 (1999).

    CAS  PubMed  Google Scholar 

  117. 117.

    Kakade, S. & Dayan, P. Acquisition and extinction in autoshaping. Psychol. Rev. 109, 533–544 (2002).

    PubMed  Google Scholar 

  118. 118.

    Kording, K. P. & Wolpert, D. M. Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004).

    PubMed  Google Scholar 

  119. 119.

    Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–29 (2017).

    Google Scholar 

  120. 120.

    Gelman, A. Bayesian Data Analysis 3rd edn (CRC Press, Boca Raton, 2014).

  121. 121.

    Gelman, A. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis 1, 515–534 (2006).

    Google Scholar 

  122. 122.

    Bennett, C. H. Efficient estimation of free-energy differences from monte-carlo data. J. Comput. Phys. 22, 245–268 (1976).

    Google Scholar 

  123. 123.

    Meng, X. L. & Wong, W. H. Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Stat. Sin. 6, 831–860 (1996).

    Google Scholar 

  124. 124.

    Gronau, Q. F. et al. A tutorial on bridge sampling. J. Math. Psychol. 81, 80–97 (2017).

    PubMed  PubMed Central  Google Scholar 

  125. 125.

    Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).

    Google Scholar 

  126. 126.

    Wager, T. D., Keller, M. C., Lacey, S. C. & Jonides, J. Increased sensitivity in neuroimaging analyses using robust regression. Neuroimage 26, 99–113 (2005).

    PubMed  Google Scholar 

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Acknowledgements

We thank M. Powell and D. Ryan for assistance with data collection, and M. Roy and M. López-Solà for discussions. This research was made possible with the support of National Institutes of Health grants NIMH 2R01MH076136 and R01DA027794 (to T.D.W.), a VENI grant of the Netherlands Organization for Scientific Research (to M. Jepma), and AFOSR grant FA9550-14-1-0318 (to M. Jones). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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M.Jepma, L.K. and T.D.W. conceived and designed the experiments. M.Jepma conducted the experiments and analysed the data. L.K., J.D., M.Jones and T.D.W. provided expertise and feedback. M.Jepma, L.K., J.D., M.Jones and T.D.W. wrote the manuscript.

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Correspondence to Marieke Jepma.

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Jepma, M., Koban, L., van Doorn, J. et al. Behavioural and neural evidence for self-reinforcing expectancy effects on pain. Nat Hum Behav 2, 838–855 (2018). https://doi.org/10.1038/s41562-018-0455-8

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