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Record W2900212739 · doi:10.1155/2018/5741941

Problems with Facial Mimicry Might Contribute to Emotion Recognition Impairment in Parkinson’s Disease

2018· review· en· W2900212739 on OpenAlexafffund
Margaret Prenger, Penny A. MacDonald

Bibliographic record

VenueParkinson s Disease · 2018
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMimicryFacial expressionParkinson's diseaseDiseaseEmbodied cognitionCognitionAffect (linguistics)MedicinePsychologyCognitive psychologyPopulationNeuroscienceCommunicationPathologyBiology

Abstract

fetched live from OpenAlex

Difficulty with emotion recognition is increasingly being recognized as a symptom of Parkinson's disease. Most research into this area contends that progressive cognitive decline accompanying the disease is to be blamed. However, facial mimicry (i.e., the involuntary congruent activation of facial expression muscles upon viewing a particular facial expression) might also play a role and has been relatively understudied in this clinical population. In healthy participants, facial mimicry has been shown to improve recognition of observed emotions, a phenomenon described by embodied simulation theory. Due to motor disturbances, Parkinson's disease patients frequently show reduced emotional expressiveness, which translates into reduced mimicry. Therefore, it is likely that facial mimicry problems in Parkinson's disease contribute at least partly to the emotional recognition deficits that these patients experience and might greatly influence their social cognition abilities and quality of life. The present review aims to highlight the need for further inquiry into the motor mechanisms behind emotional recognition in Parkinson's disease by synthesizing behavioural, physiological, and neuroanatomical evidence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.039
GPT teacher head0.299
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2018
Admission routes2
Has abstractyes

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