Persistent Link:
http://hdl.handle.net/10150/268514
Title:
Emotional Sophistication: Studies of Facial Expressions in Games
Author:
Rossi, Filippo
Issue Date:
2012
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Decision-making is a complex process. Monetary incentives constitute one of the forces driving it, however the motivational space of decision-makers is much broader. We care about other people, we experience emotional reactions, and sometimes we make mistakes. Such social motivations (Sanfey, 2007) drive our own decisions, as well as affect our beliefs about what motivates others' decisions. Behavioral and brain sciences have started addressing the role of social motivations in economic games (Camerer, 2004; Glimcher et al., 2009), however several aspects of social decisions, such as the process of thinking about others' emotional states - emotional sophistication - have been rarely investigated. The goal of this project is to use automatic measurements of dynamic facial expressions to investigate non-monetary motivations and emotional sophistication. The core of our approach is to use state-of-the-art computer vision techniques to extract facial actions from videos in real-time (based on the Facial Action Coding System of Ekman and Friesen (1978)), while participants are playing economic games. We will use powerful statistical machine learning techniques to make inferences about participants internal emotional states during these interactions. These inferences will be used (a) to predict behavior; (b) to explain why a decision is made in terms of the hidden forces driving it; and (c) to investigate the ways in which people construct their beliefs about other people's future actions. The contributions of this targeted interdisciplinary project are threefold. First, it develops new methodologies to study decision processes. Second, it uses these methods to test hypotheses about the role of first order beliefs about social motivations. Finally, our statistical approach sets the ground for "affectively aware" systems, that can use facial expressions to assess the internal states of their users, thus improving human-machine interactions.
Type:
text; Electronic Dissertation
Keywords:
game theory; machine learning; neuroeconomics; Psychology; emotions; facial expressions
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Psychology
Degree Grantor:
University of Arizona
Advisor:
Nadel, Lynn

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleEmotional Sophistication: Studies of Facial Expressions in Gamesen_US
dc.creatorRossi, Filippoen_US
dc.contributor.authorRossi, Filippoen_US
dc.date.issued2012-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractDecision-making is a complex process. Monetary incentives constitute one of the forces driving it, however the motivational space of decision-makers is much broader. We care about other people, we experience emotional reactions, and sometimes we make mistakes. Such social motivations (Sanfey, 2007) drive our own decisions, as well as affect our beliefs about what motivates others' decisions. Behavioral and brain sciences have started addressing the role of social motivations in economic games (Camerer, 2004; Glimcher et al., 2009), however several aspects of social decisions, such as the process of thinking about others' emotional states - emotional sophistication - have been rarely investigated. The goal of this project is to use automatic measurements of dynamic facial expressions to investigate non-monetary motivations and emotional sophistication. The core of our approach is to use state-of-the-art computer vision techniques to extract facial actions from videos in real-time (based on the Facial Action Coding System of Ekman and Friesen (1978)), while participants are playing economic games. We will use powerful statistical machine learning techniques to make inferences about participants internal emotional states during these interactions. These inferences will be used (a) to predict behavior; (b) to explain why a decision is made in terms of the hidden forces driving it; and (c) to investigate the ways in which people construct their beliefs about other people's future actions. The contributions of this targeted interdisciplinary project are threefold. First, it develops new methodologies to study decision processes. Second, it uses these methods to test hypotheses about the role of first order beliefs about social motivations. Finally, our statistical approach sets the ground for "affectively aware" systems, that can use facial expressions to assess the internal states of their users, thus improving human-machine interactions.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectgame theoryen_US
dc.subjectmachine learningen_US
dc.subjectneuroeconomicsen_US
dc.subjectPsychologyen_US
dc.subjectemotionsen_US
dc.subjectfacial expressionsen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplinePsychologyen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorNadel, Lynnen_US
dc.contributor.committeememberPiatteli Palmarini, Massimoen_US
dc.contributor.committeememberMorrison, Claytonen_US
dc.contributor.committeememberSanfey, Alanen_US
dc.contributor.committeememberFasel, Ianen_US
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