Developmental Neural Correlates of Social Interaction

Children develop in a sea of reciprocal social interaction, but their brain development is predominately studied in non-interactive contexts (e.g., viewing photographs of faces). This dissertation investigated how the developing brain supports social interaction. Specifically, novel paradigms were used to target two facets of social experience—social communication and social motivation—across three studies in children and adults. In Study 1, adults listened to short vignettes—which contained no social information—that they believed to be either prerecorded or presented over an audio-feed by a live social partner. Simply believing that speech was from a live social partner increased activation in the brain’s mentalizing network—a network involved in thinking about others’ thoughts. Study 2 extended this paradigm to middle childhood, a time of increasing social competence and social network complexity, as well as structural and functional social brain development. Results showed that, as in adults, regions of the mentalizing network were engaged by live speech. Taken together, these findings indicate that the mentalizing network may support the processing of interactive communicative cues across development. Given this established importance of social-interactive context, Study 3 examined children’s social motivation when they believed they were engaged in a computer-based chat with a peer. Children initiated interaction via sharing information about their likes and hobbies and received responses from the peer. Compared to a non-social control, in which children chatted with a computer, peer interaction increased activation in mentalizing regions and reward circuitry. Further, within mentalizing regions, responsivity to the peer increased with age. Thus, across all three studies, social cognitive regions associated with mentalizing supported real-time social interaction. In contrast, the specific social context appeared to influence both reward circuitry involvement and age-related changes in neural activity. Future studies should continue to examine how the brain supports interaction across varied real-world social contexts. In addition to illuminating typical development, understanding the neural bases of interaction will offer insight into social disabilities such as autism, where social difficulties are often most acute in interactive situations. Ultimately, to best capture human experience, social neuroscience ought to be embedded in the social world.

Joint Optimization for Social Content Delivery in Wireless Networks

Over the last decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well-received. However, as users become dominantly mobile, little is done to consider the impacts of the wireless environment, especially the capacity constraints and changing channel. In this dissertation, we investigate a centralized wireless content delivery system, aiming to optimize overall user experience given the capacity constraints of the wireless networks, by deciding what contents to deliver, when and how. We propose a scheduling framework that incorporates content-based reward and deliverability. Our approach utilizes the broadcast nature of wireless communication and social nature of content, by multicasting and precaching. Results indicate this novel joint optimization approach outperforms existing layered systems that separate recommendation and delivery, especially when the wireless network is operating at maximum capacity. Utilizing limited number of transmission modes, we significantly reduce the complexity of the optimization. We also introduce the design of a hybrid system to handle transmissions for both system recommended contents ('push') and active user requests ('pull'). Further, we extend the joint optimization framework to the wireless infrastructure with multiple base stations. The problem becomes much harder in that there are many more system configurations, including but not limited to power allocation and how resources are shared among the base stations ('out-of-band' in which base stations transmit with dedicated spectrum resources, thus no interference; and 'in-band' in which they share the spectrum and need to mitigate interference). We propose a scalable two-phase scheduling framework: 1) each base station obtains delivery decisions and resource allocation individually; 2) the system consolidates the decisions and allocations, reducing redundant transmissions. Additionally, if the social network applications could provide the predictions of how the social contents disseminate, the wireless networks could schedule the transmissions accordingly and significantly improve the dissemination performance by reducing the delivery delay. We propose a novel method utilizing: 1) hybrid systems to handle active disseminating requests; and 2) predictions of dissemination dynamics from the social network applications. This method could mitigate the performance degradation for content dissemination due to wireless delivery delay. Results indicate that our proposed system design is both efficient and easy to implement.