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Pupillary responses to masked and gaze-averted faces
Cansu Malak1,2, Funda Yildirim3
1Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Face masks and gaze direction impact emotion recognition. Pupil dilation, measured by pupillometry, indicates cognitive effort during emotion identification, especially with masked fearful faces.
Area of Science:
- Cognitive Psychology
- Human-Computer Interaction (HCI)
- Neuroscience
Background:
- Face masks, prevalent during COVID-19, influence emotion recognition by obscuring facial features.
- Gaze direction is crucial for emotion identification, particularly when faces are partially covered.
- The physiological underpinnings of how masks and gaze affect emotion processing remain underexplored.
Purpose of the Study:
- To investigate the combined effects of face masks and gaze direction on emotion recognition.
- To explore the role of pupillometry in understanding cognitive processes during masked emotion perception.
- To assess how pupil size changes relate to accuracy and errors in emotion identification.
Main Methods:
- Utilized pupillometry to measure pupil size changes in 40 participants.
- Presented participants with images of faces displaying anger, fear, or neutral emotions.
- Manipulated gaze direction (direct, averted) and face mask conditions (mask, no mask).
Main Results:
- Behavioral accuracy confirmed the importance of the eye region for anger/neutral recognition and direct gaze benefits.
- Averted gaze improved fear recognition, aligning with prior research.
- Pupil dilation significantly increased with recognition errors, particularly for masked fearful faces, suggesting heightened cognitive load and ambiguity.
Conclusions:
- Pupil dilation serves as a physiological indicator of cognitive effort in emotion identification, especially under challenging conditions like masked faces.
- Participants employed compensatory strategies, relying more on gaze and visible cues when masks obscured facial information.
- Findings offer insights for developing adaptive support systems in HCI and VR to enhance user experience by accounting for cognitive load.
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