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

Author

Listed:
  • Marieke Jepma

    (University of Amsterdam
    University of Colorado Boulder)

  • Leonie Koban

    (University of Colorado Boulder)

  • Johnny Doorn

    (University of Amsterdam)

  • Matt Jones

    (University of Colorado Boulder)

  • Tor D. Wager

    (University of Colorado Boulder)

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.

Suggested Citation

  • Marieke Jepma & Leonie Koban & Johnny Doorn & Matt Jones & Tor D. Wager, 2018. "Behavioural and neural evidence for self-reinforcing expectancy effects on pain," Nature Human Behaviour, Nature, vol. 2(11), pages 838-855, November.
  • Handle: RePEc:nat:nathum:v:2:y:2018:i:11:d:10.1038_s41562-018-0455-8
    DOI: 10.1038/s41562-018-0455-8
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    Cited by:

    1. Flavia Mancini & Suyi Zhang & Ben Seymour, 2022. "Computational and neural mechanisms of statistical pain learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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