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Record W4410431958 · doi:10.1016/j.jphyss.2025.100026

Pupil dilation evoked by painful electrical stimulation is abolished during pain inhibition by distraction

2025· article· en· W4410431958 on OpenAlexafffund
Alice Wagenaar-Tison, Zoha Deldar, Benjamin Provencher, Stéphane Northon, Nabi Rustamov, Isabelle Blanchette, Sylvain Sirois, Mathieu Piché

Bibliographic record

VenueThe Journal of Physiological Sciences · 2025
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsUniversité LavalUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDistractionNociceptionStimulationPupillary responseAnticipation (artificial intelligence)AnesthesiaPsychologyStimulus (psychology)MedicinePupilAudiologyNeuroscienceInternal medicineCognitive psychology

Abstract

fetched live from OpenAlex

The aim of the present study was to examine the contribution of spinal and supraspinal processes to pain modulation by attention. It is hypothesized that pain inhibition by distraction is accompanied by reduced pain-evoked pupil dilation and cerebral activity, but no inhibition of the nociceptive flexion reflex (NFR), while pain anticipation is expected to increase pain perception and pain-related responses. Twenty healthy volunteers received 90 painful stimuli in control, distraction (mental arithmetic), and anticipation (visual cue) conditions. Anticipation did not modulate pain (p = .7), while distraction decreased pain significantly (p < .001). Moreover, pupil diameter increased 500-1000 ms post-stimulus in the control condition (p < .05), but this response was abolished by distraction. Distraction also decreased pain-related brain activity (high-gamma oscillations) (p = .004), but not the NFR (p = .3). These results suggest that pain inhibition by distraction is produced, in part, by supraspinal inhibition of nociceptive processes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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