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Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research

Nathaniel Haines,Ziv E. Bell,Sheila E. Crowell,Hunter Hahn,Dana Kamara,Heather McDonough-Caplan,Tiffany M Shader,Theodore P. Beauchaine

Published 2019 in Development and Psychopathology

ABSTRACT

Abstract As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation.

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