Manual and Automatic Measures Confirm-Intranasal Oxytocin Increases Facial Expressivity (bibtex)
by Catherine Neubauer, Sharon Mozgai, Stefan Scherer, Joshua Woolley, Brandon Chuang
Abstract:
The effects of oxytocin on facial emotional expressivity were investigated in individuals with schizophrenia and age-matched healthy controls during the completion of a Social Judgment Task (SJT) with a double-blind, placebo-controlled, cross-over design. Although pharmacological interventions exist to help alleviate some symptoms of schizophrenia, currently available agents are not effective at improving the severity of blunted facial affect. Participant facial expressivity was previously quantified from video recordings of the SJT using a wellvalidated manual approach (Facial Expression Coding System; FACES). We confirm these findings using an automated computer-based approach. Using both methods we found that the administration of oxytocin significantly increased total facial expressivity in individuals with schizophrenia and increased facial expressivity at trend level in healthy controls. Secondary analysis showed that oxytocin also significantly increased the frequency of negative valence facial expressions in individuals with schizophrenia but not in healthy controls and that oxytocin did not significantly increase positive valence facial expressions in either group. Both manual coding and automatic facial analysis revealed the same pattern of findings. Considering manual annotation can be expensive and timeconsuming, these results suggest that automatic facial analysis may be an efficient and cost-effective alternative to currently utilized manual approaches and may be ready for use in clinical settings.
Reference:
Manual and Automatic Measures Confirm-Intranasal Oxytocin Increases Facial Expressivity (Catherine Neubauer, Sharon Mozgai, Stefan Scherer, Joshua Woolley, Brandon Chuang), In Affective Computing and Intelligent Interaction, 2017.
Bibtex Entry:
@article{neubauer_manual_2017,
	title = {Manual and {Automatic} {Measures} {Confirm}-{Intranasal} {Oxytocin} {Increases} {Facial} {Expressivity}},
	url = {https://www.researchgate.net/publication/321644417_Manual_and_Automatic_Measures_Confirm-Intranasal_Oxytocin_Increases_Facial_Expressivity?enrichId=rgreq-22efb1e32ef30cdd22e6bee2b3b63d56-XXX&enrichSource=Y292ZXJQYWdlOzMyMTY0NDQxNztBUzo1NjkwNTI4NzM4NTQ5NzZAMTUxMjY4NDE4NTcyOQ%3D%3D&el=1_x_2&_esc=publicationCoverPdf},
	abstract = {The effects of oxytocin on facial emotional expressivity were investigated in individuals with schizophrenia and age-matched healthy controls during the completion of a Social Judgment Task (SJT) with a double-blind, placebo-controlled, cross-over design. Although pharmacological interventions exist to help alleviate some symptoms of schizophrenia, currently available agents are not effective at improving the severity of blunted facial affect. Participant facial expressivity was previously quantified from video recordings of the SJT using a wellvalidated manual approach (Facial Expression Coding System; FACES). We confirm these findings using an automated computer-based approach. Using both methods we found that the administration of oxytocin significantly increased total facial expressivity in individuals with schizophrenia and increased facial expressivity at trend level in healthy controls. Secondary analysis showed that oxytocin also significantly increased the frequency of negative valence facial expressions in individuals with schizophrenia but not in healthy controls and that oxytocin did not significantly increase positive valence facial expressions in either group. Both manual coding and automatic facial analysis revealed the same pattern of findings. Considering manual annotation can be expensive and timeconsuming, these results suggest that automatic facial analysis may be an efficient and cost-effective alternative to currently utilized manual approaches and may be ready for use in clinical settings.},
	journal = {Affective Computing and Intelligent Interaction},
	author = {Neubauer, Catherine and Mozgai, Sharon and Scherer, Stefan and Woolley, Joshua and Chuang, Brandon},
	month = dec,
	year = {2017},
	keywords = {ARL, DoD, UARC, Virtual Humans}
}
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