We describe a computational model of social norms based on identifying values that a certain culture finds desirable such as dignity, generosity and politeness. The model quantifies these values in the form of Culture-Sanctioned Social Metrics (CSSMs) and treats social norms as the requirement to maximize these metrics from the perspective of the self, peers and public. This model can be used to create realistic social simulations, to explain or predict human behavior in specific scenarios, or as a component of robots or agents that need to interact with humans in specific social-cultural settings. We validate the model by using it to represent a complex deception scenario and showing that it can yield non-trivial insights such as the explanation of apparently irrational human behavior.
The difference between public and private behavior, of interest for our model, had been the subject of several research studies. For instance, @cite_37 studied the behavior of children in private and public settings in specially created games. The study found that children over age 9, who are typically assumed to exhibit full theory of mind skills, had been better able to resist selfish impulses in public than in private settings, while this difference did not exist for children under 9.
@cite_37
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Are selfish impulses less likely to be pursued when decisions are publicly observable? Is the presence of peers a potential solution to social dilemmas? In this paper we report data on the self-control decisions of children aged 6 to 11 who participated in games that require one to resist a selfish impulse for several minutes in order to benefit others. In Public Condition children make decisions in public view of the group of other participants, while in Private Condition they have the possibility to decide privately. We find that children aged 9 and higher are better able to resist selfish impulses in public environments. Younger children, however, display no such effect. Further, we find self-control substantially impacted by group size. When decisions are public, self-control is better in larger groups, while in private condition the opposite holds. Abstract In this article we propose an account of the early development of children's knowledge about the mind and report two studies designed to test a part of it. According to this “connections-representations” account, young children begin their discovery of the mental world by learning that they and other people have internal experiences or mental states that connect them cognitively to external objects and events-experiences such as seeing them, hearing them, and wanting them. Later, they realize that the same object can be seriously (other than in pretense) mentally represented in different, seemingly contradictory ways: for example, as A in appearance, B in reality, C according to their perceptual or conceptual perspective, and D according to another person's. The results of both studies confirmed the prediction that 3-year-olds would perform well on appearance-reality and perceptual perspective-taking tasks requiring only an understanding of cognitive connections, but perform poorly on tasks requiring an understanding of seemingly-contrary-to-fact mental representations. To illustrate, children of this age had little difficulty determining that they could hear but could not see a noise-making object located on the experimenter's side of a barrier, and that the experimenter could see it (connections-level tasks). In contrast, they were largely unable to say, for instance, that a toy bear held behind a large elephant mask and emitting a cat sound looked like an elephant, sounded like a cat, and really was a toy bear (representations-level tasks)—even though the experimenter had actually told them previously what it looked like, sounded like, and really was. The article concludes with speculations about the possible origins of connections and representations knowledge and observations about the significance of these acquisitions for the child's development. In search for better technological solutions for education, we adapted a principle from economic game theory, namely that giving a help will promote collaboration and eventually long-term relations between a robot and a child. This principle has been shown to be effective in games between humans and between humans and computer agents. We compared the social and cognitive engagement of children when playing checkers game combined with a social strategy against a robot or against a computer. We found that by combining the social and game strategy the children (average age of 8.3 years) had more empathy and social engagement with the robot since the children did not want to necessarily win against it. This finding is promising for using social strategies for the creation of long-term relations between robots and children and making educational tasks more engaging. An additional outcome of the study was the significant difference in the perception of the children about the difficulty of the game – the game with the robot was seen as more challenging and the robot – as a smarter opponent. This finding might be due to the higher perceived or expected intelligence from the robot, or because of the higher complexity of seeing patterns in three-dimensional world. We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition.