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Social Signal Processing: Survey of an Emerging Domain
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 153 (32 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.
Social Signal Processing: State-of-the-art and future perspectives of an emerging domain
- IN PROCEEDINGS OF THE ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA
, 2008
"... The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next- ..."
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Cited by 27 (7 self)
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The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes aset of recommendations for enabling the development of the next generation of socially-aware computing.
Social signals, their function, and automatic analysis: a survey
- In Proceedings of the International Conference on Multimodal interfaces
, 2008
"... ABSTRACT Social Signal Processing (SSP) aims at the analysis of social behaviour in both Human-Human and Human-Computer interactions. SSP revolves around automatic sensing and interpretation of social signals, complex aggregates of nonverbal behaviours through which individuals express their attitu ..."
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Cited by 14 (2 self)
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ABSTRACT Social Signal Processing (SSP) aims at the analysis of social behaviour in both Human-Human and Human-Computer interactions. SSP revolves around automatic sensing and interpretation of social signals, complex aggregates of nonverbal behaviours through which individuals express their attitudes towards other human (and virtual) participants in the current social context. As such, SSP integrates both engineering (speech analysis, computer vision, etc.) and human sciences (social psychology, anthropology, etc.) as it requires multimodal and multidisciplinary approaches. As of today, SSP is still in its early infancy, but the domain is quickly developing, and a growing number of works is appearing in the literature. This paper provides an introduction to nonverbal behaviour involved in social signals and a survey of the main results obtained so far in SSP. It also outlines possibilities and challenges that SSP is expected to face in the next years if it is to reach its full maturity.
Social Signal Processing: Understanding Social Interactions through Nonverbal Behavior Analysis
"... This paper introduces Social Signal Processing (SSP), the domain aimed at automatic understanding of social interactions through analysis of nonverbal behavior. The core idea of SSP is that nonverbal behavior is machine detectable evidence of social signals, the relational attitudes exchanged betwee ..."
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Cited by 13 (1 self)
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This paper introduces Social Signal Processing (SSP), the domain aimed at automatic understanding of social interactions through analysis of nonverbal behavior. The core idea of SSP is that nonverbal behavior is machine detectable evidence of social signals, the relational attitudes exchanged between interacting individuals. Social signals include (dis-)agreement, empathy, hostility, and any other attitude towards others that is expressed not only by words but by nonverbal behaviors such as facial expression and body posture as well. Thus, nonverbal behavior analysis is used as a key to automatic understanding of social interactions. This paper presents not only a survey of the related literature and the main concepts underlying SSP, but also an illustrative example of how such concepts are applied to the analysis of conflicts in competitive discussions. 1.
R.: Sensors Model Student Self Concept in the Classroom
, 2009
"... Abstract. In this paper we explore findings from three experiments that use minimally invasive sensors with a web based geometry tutor to create a user model. Minimally invasive sensor technology is mature enough to equip classrooms of up to 25 students with four sensors at the same time while using ..."
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Cited by 9 (4 self)
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Abstract. In this paper we explore findings from three experiments that use minimally invasive sensors with a web based geometry tutor to create a user model. Minimally invasive sensor technology is mature enough to equip classrooms of up to 25 students with four sensors at the same time while using a computer based intelligent tutoring system. The sensors, which are on each student’s chair, mouse, monitor, and wrist, provide data about posture, movement, grip tension, arousal, and facially expressed mental states. This data may provide adaptive feedback to an intelligent tutoring system based on an individual student’s affective states. The experiments show that when sensor data supplements a user model based on tutor logs, the model reflects a larger percentage of the students ’ self-concept than a user model based on the tutor logs alone. The models are further expanded to classify four ranges of emotional self-concept including frustration, interest, confidence, and excitement with over 78 % accuracy. The emotional predictions are a first step for intelligent tutor systems to create sensor based personalized feedback for each student in a classroom environment. Bringing sensors to our children’s schools addresses real problems of students ’ relationship to mathematics as they are learning the subject. 1
Designing Persuasive Robots: How Robots Might Persuade People Using Vocal and Nonverbal Cues
"... Social robots have to potential to serve as personal, organizational, and public assistants as, for instance, diet coaches, teacher’s aides, and emergency respondents. The success of these robots—whether in motivating users to adhere to a diet regimen or in encouraging them to follow evacuation proc ..."
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Cited by 3 (1 self)
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Social robots have to potential to serve as personal, organizational, and public assistants as, for instance, diet coaches, teacher’s aides, and emergency respondents. The success of these robots—whether in motivating users to adhere to a diet regimen or in encouraging them to follow evacuation procedures in the case of a fire—will rely largely on their ability to persuade people. Research in a range of areas from political communication to education suggest that the nonverbal behaviors of a human speaker play a key role in the persuasiveness of the speaker’s message and the listeners’ compliance with it. In this paper, we explore how a robot might effectively use these behaviors, particularly vocal and bodily cues, to persuade users. In an experiment with 32 participants, we evaluate how manipulations in a robot’s use of nonverbal cues affected participants ’ perceptions of the robot’s persuasiveness and their compliance with the robot’s suggestions across four conditions: (1) no vocal or bodily cues, (2) vocal cues only, (3) bodily cues only, and (4) vocal and bodily cues. The results showed that participants complied with the robot’s suggestions significantly more when it used nonverbal cues than they did when it did not use these cues and that bodily cues were more effective in persuading participants than vocal cues were. Our model of persuasive nonverbal cues and experimental results have direct implications for the design of persuasive behaviors for humanlike robots.
Encouraging contribution to online communities
"... Many hands make light work, goes the proverb. But only if all those hands actually do some work. To be successful, online communities need the people who participate in them to contribute the resources on which the group’s existence is built. The types of resource contributions needed differ widely ..."
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Cited by 2 (0 self)
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Many hands make light work, goes the proverb. But only if all those hands actually do some work. To be successful, online communities need the people who participate in them to contribute the resources on which the group’s existence is built. The types of resource contributions needed differ widely across different types of groups. Volunteers in NASA’s clickworker community
Challenges for Virtual Humans in Human Computing
- In: AI for Human Computing. Lecture Notes in Artificial Intelligence 4451
, 2007
"... The vision of Ambient Intelligence (AmI) presumes a plethora of embedded services and devices that all endeavor to support humans in their daily activities as unobtrusively as possible. Hardware gets distributed throughout the environment, occupying even the fabric of our clothing. The environment i ..."
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Cited by 1 (0 self)
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The vision of Ambient Intelligence (AmI) presumes a plethora of embedded services and devices that all endeavor to support humans in their daily activities as unobtrusively as possible. Hardware gets distributed throughout the environment, occupying even the fabric of our clothing. The environment is equipped
Designing Gaze Behavior for Humanlike Robots
, 2009
"... material are those of the author and do not necessarily reflect those of these funding agencies. ii ..."
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Cited by 1 (0 self)
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material are those of the author and do not necessarily reflect those of these funding agencies. ii
Is It What You Do, or When You Do It? The Roles of Contingency and Similarity in Pro-Social Effects of Imitation
, 2013
"... Abstract Being imitated has a wide range of pro-social effects, but it is not clear how these effects are mediated. Naturalistic studies of the effects of being imitated have not established whether pro-social outcomes are due to the similarity and/or the contingency between the movements performed ..."
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Abstract Being imitated has a wide range of pro-social effects, but it is not clear how these effects are mediated. Naturalistic studies of the effects of being imitated have not established whether pro-social outcomes are due to the similarity and/or the contingency between the movements performed by the actor and those of the imitator. Similarity is often assumed to be the active ingredient, but we hypothesized that contingency might also be important, as it produces positive affect in infants and can be detected by phylogenetically ancient mechanisms of associative learning. We manipulated similarity and contingency between performed and observed actions in a computerized task. Similarity had no positive effects; however, contingency resulted in greater enjoyment of the task, reported closeness to others, and helping behavior. These results suggest that the pro-social effects of being imitated may rely on associative mechanisms.
