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Record W2109339578 · doi:10.3109/01942638.2011.653626

Recognition of Facial Expressions of Emotion in Adults with Down Syndrome

2012· article· en· W2109339578 on OpenAlexaff
Naznin Virji‐Babul, Kimberley Watt, Farouk S. Nathoo, Peter Johnson

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2012
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsUniversity of VictoriaDown Syndrome Research FoundationUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsPsychologyFacial expressionCLIPSOddsMotion (physics)PerceptionDevelopmental psychologyIdentification (biology)Facial recognition systemEmotional expressionCognitive psychologyCommunicationLogistic regressionArtificial intelligenceMedicinePattern recognition (psychology)Computer science

Abstract

fetched live from OpenAlex

Research on facial expressions in individuals with Down syndrome (DS) has been conducted using photographs. Our goal was to examine the effect of motion on perception of emotional expressions. Adults with DS, adults with typical development matched for chronological age (CA), and children with typical development matched for developmental age (DA) viewed photographs and video clips of facial expressions of: happy, sad, mad, and scared. The odds of accurate identification of facial expressions were 2.7 times greater for video clips compared with photographs. The odds of accurate identification of expressions of mad and scared were greater for video clips compared with photographs. The odds of accurate identification of expressions of mad and sad were greater for adults but did not differ between adults with DS and children. Adults with DS demonstrated the lowest accuracy for recognition of scared. These results support the importance of motion cues in evaluating the social skills of individuals with DS.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.068
GPT teacher head0.360
Teacher spread0.292 · 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 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

Citations16
Published2012
Admission routes1
Has abstractyes

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