Drift-diffusion modeling reveals that masked faces are preconceived as unfriendly

Publication date

2023-10-09

Authors

Mulder, Martijn J.ORCID 0000-0001-9640-4322ISNI 0000000391634262
Prummer, Franziska
Terburg, DavidISNI 0000000393680801
Kenemans, LeonISNI 0000000390041596

Editors

Advisors

Supervisors

Document Type

Article
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License

cc_by

Abstract

During the COVID-19 pandemic, the use of face masks has become a daily routine. Studies have shown that face masks increase the ambiguity of facial expressions which not only affects (the development of) emotion recognition, but also interferes with social interaction and judgement. To disambiguate facial expressions, we rely on perceptual (stimulus-driven) as well as preconceptual (top-down) processes. However, it is unknown which of these two mechanisms accounts for the misinterpretation of masked expressions. To investigate this, we asked participants (N = 136) to decide whether ambiguous (morphed) facial expressions, with or without a mask, were perceived as friendly or unfriendly. To test for the independent effects of perceptual and preconceptual biases we fitted a drift-diffusion model (DDM) to the behavioral data of each participant. Results show that face masks induce a clear loss of information leading to a slight perceptual bias towards friendly choices, but also a clear preconceptual bias towards unfriendly choices for masked faces. These results suggest that, although face masks can increase the perceptual friendliness of faces, people have the prior preconception to interpret masked faces as unfriendly.

Keywords

Humans, COVID-19, Pandemics, Diffusion, Emotions, Judgment

Citation

Mulder, M J, Prummer, F, Terburg, D & Kenemans, J L 2023, 'Drift-diffusion modeling reveals that masked faces are preconceived as unfriendly', Scientific Reports, vol. 13, no. 1, 16982. https://doi.org/10.1038/s41598-023-44162-y