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Record W2032411060 · doi:10.1016/s0304-3959(03)00297-5

Effects of odors on pain perception: deciphering the roles of emotion and attention

2003· article· en· W2032411060 on OpenAlexafffund
Chantal Villemure, Burton M. Slotnick, Catherine M. Bushnell

Bibliographic record

VenuePain · 2003
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchNational Institutes of HealthMcGill University
KeywordsPsychologyValence (chemistry)OdorAnxietyMoodPerceptionAffect (linguistics)ArousalNociceptionPain perceptionCognitive psychologyAudiologyNeuroscienceClinical psychologyMedicineCommunicationPsychiatryAnesthesia

Abstract

fetched live from OpenAlex

Emotions have been shown to alter pain perception, but the underlying mechanism is unclear since emotions also affect attention, which itself changes nociceptive transmission. We manipulated independently direction of attention and emotional state, using tasks involving heat pain and pleasant and unpleasant odors. Shifts in attention between the thermal and olfactory modalities did not alter mood or anxiety. Yet, when subjects focused attention on the pain, they perceived it as clearly more intense and somewhat more unpleasant than when they attended to the odor. In contrast, odor valence altered mood, anxiety level, and pain unpleasantness, but did not change the perception of pain intensity. Pain unpleasantness ratings correlated with mood, but not with odor valence, suggesting that emotional changes underlie the selective modulation of pain affect. These results show that emotion and attention differentially alter pain perception and thus invoke at least partially separable neural modulatory circuits.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.239
Teacher spread0.189 · 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".

Quick stats

Citations360
Published2003
Admission routes2
Has abstractyes

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