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Record W4213068685 · doi:10.1027/1864-9335/a000470

Face Masks Impair Basic Emotion Recognition

2022· article· en· W4213068685 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyAgreeablenessBig Five personality traitsExtraversion and introversionFacial expressionEmotion recognitionFacial recognition systemFace perceptionCognitive psychologyPersonalityFace (sociological concept)Developmental psychologySocial psychologyPerceptionCommunicationPattern recognition (psychology)

Abstract

fetched live from OpenAlex

Abstract. With the widespread adoption of masks, there is a need for understanding how facial obstruction affects emotion recognition. We asked 120 participants to identify emotions from faces with and without masks. We also examined if recognition performance was related to autistic traits and personality. Masks impacted recognition of expressions with diagnostic lower face features the most and those with diagnostic upper face features the least. Persons with higher autistic traits were worse at identifying unmasked expressions, while persons with lower extraversion and higher agreeableness were better at recognizing masked expressions. These results show that different features play different roles in emotion recognition and suggest that obscuring features affects social communication differently as a function of autistic traits and personality.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.075
GPT teacher head0.378
Teacher spread0.303 · 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