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Record W4401112013 · doi:10.1177/00332941241269547

Incomplete Faces Do but Masked Faces Do Not Affect Mind Perception

2024· article· en· W4401112013 on OpenAlexaff
Farid Pazhoohi, Keina Aoki, Alan Kingstone

Bibliographic record

VenuePsychological Reports · 2024
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyPerceptionAttributionAffect (linguistics)Agency (philosophy)Social perceptionFacial expressionFace perceptionSocial psychologyFace (sociological concept)Sense of agencyDevelopmental psychologyCognitive psychologyCommunication

Abstract

fetched live from OpenAlex

The human face plays a critical role in how we perceive the minds of others. The current research across two studies explored whether face masks also impact mind perception, with the expectation that they lead to lower attributions of agency and experience to individuals, making them seem less mentally capable due to their association with reduced facial expression perception and impaired communication. In the first study, participants' ratings of masked and unmasked faces for agency and experience did not yield significant differences, suggesting that wearing a face mask does not affect the perception of the mind. To explore whether these findings applied when the lower face was cropped instead of masked, results of the second study showed that removing the lower face led to decreased agency ratings, but similar to the first study, there were no changes in experience ratings. Altogether, our results showed that wearing face masks does not reduce the perception of mental capacity. Moreover, female faces received higher ratings for both agency and experience compared to male faces. The complex relationship between face masks, gender, and mind perception warrants further exploration.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0130.003

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 head0.126
GPT teacher head0.390
Teacher spread0.264 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

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