A Pipeline for Automated Facial Expression Coding in Mother-Daughter Dyads
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Date
2022-03
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Abstract
Throughout the lifespan, expression and perception of facial displays of emotion influence how we interpret and interact with the world around us. How we interpret others' facial expressions (among other nonverbal and verbal cues) can impact our own emotional experiences during social interactions in positive and negative ways. Prior shows that mothers "drive" affect in daughters during interactions, greater for depressed than non-depressed daughters. However, the role of facial expressions relative to other forms of communication in driving such effects is unclear, as measuring facial expressions is time- and labor-intensive. Automated facial expression coding (AFEC) technology circumvented difficulties with manual coding. Mothers and adolescent daughters (aged 13-18) participated in a conflict discussion task while being videotaped. Daughters were identified as self-injuring (n=20), depressed (n=14), or control (n=13) through clinical interviews. In the present study, control mothers demonstrated greater facial correspondence of both negative emotion and positive emotion in their daughters compared to mothers with daughters with psychopathology. Additionally, mothers of daughters with a history of self-injuring behavior expressed emotion more intensely than other mothers. Overall, AFEC software bypassed many hardships of manual behavioral coding and the costs of commercial programs.
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Poster Division: Social and Behavioral Sciences: 3rd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)
Keywords
emotion, dyadic, facial expression