Fig 1.
Trial structure of the emotion-recognition and social judgment task.
Each square represents a single page on participants’ screen. Please note that the stimuli and the item wording are not accurate but serve illustrative purposes. For copyright reasons, we cannot include the stimuli used in the study. Percentages below the included stimuli indicate the number of correctly recognized emotional expressions across participants and trials in each condition. The depicted individual has given written informed consent (as outlined in the PLOS consent form) to be included in the figure.
Fig 2.
Factor-based comparisons of emotion-recognition accuracy.
Comparisons are based on simple multilevel logistic regression models with emotion-recognition accuracy as the outcome and the respective factor as the sole predictor. For factors with three levels, we repeated the analysis using the level initially coded as 010 as the reference group. The dashed line indicates the average emotion-recognition accuracy across trials, participants, and factors ( = .12).
Table 1.
Associations between all continuous variables included in the study.
Fig 3.
The effect of condition, age, gender, and age of target on emotion-recognition accuracy.
The predicted effects are shown on the vertical axis, with the expected probability of accurate emotion recognition shown on the horizontal axis. The intercept represents the average expected probability of accurate emotion recognition for young females seeing young and unmasked faces. The ICC equaled .12. The striped pattern indicates a significant effect (p < .05). AoT = age of target.
Table 2.
Multilevel logistic regression models capturing the effect of condition, age, gender, and age of target on emotion-recognition accuracy.
Table 3.
Multilevel regression models capturing the effect of condition, valence, and mask-related associations on social judgments.