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Record W7011479508

The modulation of pain by visual emotional stimulation

2012· dissertation· en· W7011479508 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2012
Typedissertation
Languageen
FieldSocial Sciences
TopicGerman Social Sciences and History
Canadian institutionsnot available
FundersMcGill University
KeywordsSadnessDistractionMoodValence (chemistry)PerceptionAffect (linguistics)CognitionChronic pain
DOInot available

Abstract

fetched live from OpenAlex

We experience pain as a unified perception, which actually results from the integration of sensory, cognitive and emotional processing in the brain. Studies show that both emotional state and attention influence our pain experience, with emotions preferentially altering the unpleasantness of pain and distraction altering the perceived pain intensity. In the laboratory, odors or music have been used both as distracters and to manipulate mood state, and these manipulations alter pain perception as well. Other data show that observing emotional pictures, including pictures of faces expressing emotions, alters emotional state. Yet, little is known about how emotional pictures affect pain perception. Thus, this thesis explores the effect of visual emotional stimulation on pain perception. We used pictures as mood inducers and we manipulated the attention of the subjects by directing their attention back and forth between the experimentally-induced heat pain and the emotional stimuli. In the first pilot study, we used complex scenes of positive, negative and neutral valence, and in the three following studies we used emotional faces expressing happiness, sadness and neutral. We collected ratings of pain intensity, pain unpleasantness and mood on visual analog scales. In the last study, the valence of the emotional faces had a significant effect on pain intensity, pain unpleasantness and mood ratings such that subjects reported less pain intensity, less pain unpleasantness and a better mood when viewing happy faces compared to when viewing sad faces. The direction of attention, however, did not have a significant effect on any of the ratings. Although our final study design failed to manipulate attention, the emotional faces that we used modulated mood and pain perception effectively. Thus, the studies presented in this thesis show that viewing emotional faces alters both the perceived intensity and unpleasantness of pain and provide support for the use of emotional faces as mood inducers and for the use of emotional faces to study the effects of discrete emotions on pain perception.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.286
Teacher spread0.269 · 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.

Study designNot applicable
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

Citations0
Published2012
Admission routes1
Has abstractyes

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