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Record W2076649429 · doi:10.1016/j.pain.2005.01.001

Effects of deliberate control on verbal and facial expressions of pain

2005· article· en· W2076649429 on OpenAlexaff
Kenneth M. Prkachin

Bibliographic record

VenuePain · 2005
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsFacial expressionFacial Action Coding SystemPsychologyParallelsExpression (computer science)AudiologyMedicineCommunication

Abstract

fetched live from OpenAlex

The 'facial feedback hypothesis' suggests that inhibiting or exaggerating pain displays produces parallel effects on subjective experience. Research on the regulation of emotional expressions suggests that the act of self-regulation may be detectable in the properties of facial behavior. Both issues were examined in this study. Healthy young volunteers were videotaped while they were exposed to electric shocks varying in intensity. Participants in the Augment group were instructed to exaggerate their facial reactions to the shocks. Participants in the Attenuate group were instructed to inhibit their reactions. Controls simply responded to the shocks. All groups rated the pain of each shock on numeric, sensory and affective scales. In subsequent phases, judges rated the intensity of pain displays for all participants, and facial reactions were measured with the Facial Action Coding System. Results provided no support for the facial feedback hypothesis. Judges' ratings of participants' pain indicated that the augment instructions produced distinct alterations in pain expression. The control and inhibit groups showed linear increases in pain expression with increasing pain intensity, which did not differ significantly. Fine-grained analysis of participants' facial behavior provided evidence that pain augmentation was accompanied by topographic changes in pain expression. Parallels with existing studies, methodological issues and practical implications of the findings are discussed.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.293
Teacher spread0.281 · 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 designOther design
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

Citations31
Published2005
Admission routes1
Has abstractyes

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