ArticleEurope's journal of psychology2024
Accuracy and Speed of Emotion Recognition With Face Masks.
Article in Europe's journal of psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Sampling characteristics in the mean emotion processing of multiple faces.Psychological research · 2026Article
- 'The mirror of the soul?' Inferring sadness in the eyes.Scientific reports · 2024Article
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Authors and funding
9 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearing face masks is one of the important actions to prevent the spread of COVID-19 among people around the world. Nevertheless, social interaction is limited via masks, and this impacts the accuracy and speed of emotional perception. In the present study, we assess the impact of mask-wearing on the accuracy and speed of emotion recognition. Fifty people (female n = 39, male n = 11) aged 19-28 participated in the study (M = 21.1 years). We used frontal photos of a Kosova woman who belonged to the same participants' age group, with a grey background. Twelve different pictures were used that showed the emotional states of fear, joy, sadness, anger, neutrality, and disgust, in masked and unmasked conditions. The experiment was conducted in a controlled laboratory setting. Participants were faster for identifying emotions like joy (1.507 ms) and neutral (1.971 ms). The participants were more accurate (emotions identification) in unmasked faces (M = 85.7%) than in masked faces (M = 73.8%), F(1,98) = 20.73, MSE = 1027.66, p ≤ .001, partial η² = 0.17. Masks make confusion and reduce the accuracy and speediness of emotional detection. This may have a notable impact on social interactions among peoples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.