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{{Orphan|date=May 2011}}
{{Userspace draft|source=ArticleWizard|date=January 2011}}
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* Kathryn Segovia, Ph. D Candidate in Communication at Stanford University
* Leo Yeykelis, Ph. D Candidate in Communication at Stanford University
==Current Research==
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'''Automatic Facial Feature Detection and Analyses'''
While most prior research on facial expressions involve some form of manual coding by human coders based on established facial coding systems (e.g., FACS), this methodology uses just a small webcam and computer software to predict an individual’s errors and performance quality based only on facial features that are tracked and logged automatically. Using just the first five to seven minutes of facial feature data, we were able to predict a participant’s performance on a 30 minute experimental task with up to 90% accuracy. There are countless applications for this methodology that would facilitate research of other media effects. For instance, this methodology can predict purchasing decisions based on facial expressions (e.g., “buying” face vs. “not-buying” face) while participants engage in an online shopping task. Researchers can also monitor emotional fluctuations in real time as people make their selection of media content and verify whether or not the choices are contributing toward maintaining a good mood (i.e., mood management theory; Zillmann) based on their facial expressions. In addition, advertisers could benefit by receiving real-time data on the participant’s responses to advertisements. Automatic facial feature analysis is not yet a perfect ‘looking glass’ to a person’s mind, but its advantages are obvious and promising.
==Past Research==
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Researchers discovered that by allowing a subject to use an avatar of varying attractiveness or height, this affected how they acted in a virtual environment. They adapted to the role they felt their avatar played.
'''[[Transformed social interaction|Transformed Social Interaction]]'''
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