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Pain Expectation Effects Are Predicted by Emotion Rather Than Precision

2025· article· en· W4406167368 on OpenAlexaff
C Cheung, Phivos Phylactou

Bibliographic record

VenueJournal of Neuroscience · 2025
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsParkwood InstituteLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsPsychologyStimulus (psychology)PerceptionPlaceboPhysical medicine and rehabilitationCognitive psychologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

Pain is a universal subjective experience that is influenced not only by the objective intensity of the sensory stimulus but also by multiple internal factors, such as expectations and emotions (Melzack and Casey, 1968). Unravelling the specific mechanisms through which these factors influence the pain experience may help us better understand how to predict, treat, and eventually prevent pain. However, the neural underpinnings of pain and its modulation by internal factors remain poorly understood. The ability of expectations to affect the pain experience is illustrated by the placebo and nocebo effects, which refer, respectively, to decreased pain resulting from positive expectations toward a treatment (e.g., pain cream) and increased pain resulting from negative expectations toward a procedure (e.g., an injection). Previous research has shown that the amount of pain one experiences when receiving an electric shock is influenced not only by stimulus characteristics, such as the intensity of the shock, but also by expectations (Hoskin et al., 2019). The effect of these expectations varies depending on how precise the expectations are thought to be. Thus, if one's expectations about the painfulness of an upcoming electric shock were more consistent (i.e., the participant always expected the same level of painfulness) in the past, their expectation about the painfulness had a greater influence on their actual pain experience. Accordingly, subjects who had more inconsistent expectations (i.e., the participant expected varying levels of painfulness) had reduced expectation influence on pain perception (Hoskin et al., 2019). This effect is consistent with a Bayesian model that proposes that the degree to which expectations influence perception is determined by the consistency or precision of the expectations (Hoskin et al., 2019). Other research demonstrated that the modulation of expectation effects by the preciseness of those expectations can also be … Correspondence should be addressed to Chloe L. Cheung at ccheu252{at}uwo.ca.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.268
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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