Evidence map›Paper›PMID 38985572›Full record

ArticleeLife2024

Statistical learning shapes pain perception and prediction independently of external cues.

Jakub Onysk, Nicholas Gregory, Mia Whitefield, Maeghal Jain, Georgia Turner, Ben Seymour, Flavia Mancini

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Healing vs. hurting words: the power of language in pain perception-a narrative review.Frontiers in pain research (Lausanne, Switzerland) · 2026
    Review
  5. Article
  6. Impaired Adaptive Learning in Chronic Pain Contributes to Apathy.medRxiv : the preprint server for health sciences · 2025
    Article
  7. Article
  8. Article
  9. Short-term variability of chronic musculoskeletal pain.Frontiers in pain research (Lausanne, Switzerland) · 2025
    Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jakub OnyskComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0003-0876-5465
Nicholas GregoryComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Mia WhitefieldComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Maeghal JainComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Georgia TurnerComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Ben SeymourWellcome Centre for Integrative Neuroimaging, John Radcliffe Hospital, Headington, Oxford, United Kingdom.ORCID https://orcid.org/0000-0003-1724-5832
Flavia ManciniComputational and Biological Learning Unit, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0001-8441-9236

Funding

IITP MSIT 2019-0-01371Medical Research Council MR/T010614/1Medical Research Council MR/W027593/1NIHR Oxford Health BRC NIHR203316University of Cambridge Research Computing Service EP/T022159/1Versus Arthritis 21192Versus Arthritis 21537Wellcome TrustWellcome Trust 10.35802/214251
6 · The paper itself

Abstract

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

Indexed as

CuesPain PerceptionAdultFemaleHumansLearningMaleYoung AdultBayesian inferenceendogenous pain regulationhumanlearningneurosciencepainplaceboreinforcement learning

Identifiers

PMID38985572
PMCPMC11236420

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.