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The Social Systems Informatics Program is working on the following projects:

Newborn Smiles: More than Face Value

Few things are as heart-warming as an infant’s smile. To a mother, a newborn’s cheery smile is often the first of many meaningful interactions that will take place. Early childhood development has long been an elusive and mysterious topic for psychologists. To an ordinary person, an infant’s smile may appear to be a simple expression of happiness or contentment—but some researchers have discovered deeper meaning associated with their smiles.

Dr. Daniel Messinger, a Professor of Psychology at the University of Miami, has participated in extensive research that suggests intentional, goal-oriented behavior rooted in infants’ smiles. Dr. Messinger and his colleagues found that when infants reach 4 months of age, the timing and duration of an infant’s smile reveals purposeful, goal-oriented behavior. Furthermore, they found that mothers and infants have different goals when smiling in their interactions.

Mothers consistently sought to maximize the time that both they and the infant spent smiling, while infants sought to receive a smile from the mother even if she was not smiling. In a word, infants want to be “smiled at” by mothers. Because mother-infant smiling is a critical point in social development, researchers took great interest in infants’ behavior. Understanding infants’ early social behavior has the potential to strengthen our understanding of social development, and of disorders that occur early in development such as autism.

In order to substantiate the idea that infants’ smiles were goal-oriented, Messinger and his team used principles of Control Theory—a discipline that deals with influencing dynamic system behavior, including robots and forms of artificial intelligence. After conducting a study with infants and mothers, Messinger and his colleagues utilized an interactive robot that closely mimics the appearance of an infant, with dynamic, lifelike facial expressions.

Their infant-like robot, Diego-San, employs machine learning to adapt its behavior. That is, it adjusts its behavior based on given input. In this case, 32 UC San Diego undergraduate students participated in Messinger’s study. The students acted as the “mother” in this situation; the frequency and duration of their smiles were used as input by Diego-San. Students were also asked to rate the interaction with the robot on a scale from 1 to 5, where 1 is apathetic and 5 is positive. In order for Diego-San to be able to analyze students’ facial expressions, the Computer Expression Recognition Toolbox, or CERT, was used.

Data indicates goals clearly. Mothers want mutual smiling, while infants to be smiled at.

From the resulting data, students were found to have similar preferences to the mothers; they wanted to maximize the amount of time spent mutually smiling with the robot. Diego-San used different strategies of interactions with the students to vary the frequency and duration of its smiles. One of the strategies employed by the robot was a controller based on infant goals. This strategy was found to have significant effect on the duration of student-only smiling. This finding suggests that human behavior can be, to some degree, modeled with computational resources.

This study is fascinating in the way that it draws its findings. It is remarkable that a robot could substantiate the findings of hypothesized human behavior. This manner of research has exciting implications for the future. As machine learning continues to evolve, studies such as this one will prove invaluable in better understanding human behavior from a quantitative and empirical perspective. In the same way that humans adapt their behavior based on their experiences, sophisticated programming allows computers to adapt their functionality with information and data provided by users. This study suggests that computers will continue to take a more active role in conducting meaningful research.

Read at: PLOS (Public Library of Science) and The Wall Street Journal.

Community Development Teams to Scale-Up Multidimensional Treatment Foster Care in California

Through support from the National Institutes of Mental Health, we have designed a randomized trial to test two alternative implementation strategies for the Multidimensional Treatment Foster Care in 40 counties in California. One implementation strategy, the Community Development Team, uses a peer-to-peer model of multiple counties working together to facilitate the implementation of this evidence-based program. Changing social networks are used to examine how adoption, partnership formation, and fidelity of implementation stages are affected by both existing and new communication streams.

Effectiveness Trial of Youth Suicide Prevention Delivered by Teen Peer Leaders

We are funded by the National Institutes of Mental Health for 5 years to evaluate a school-based suicide prevention program using a randomized trial design (R01MH091452, P Wyman PI, CH Brown co-Investigator). The intervention, Sources of Strength, trains peer leaders to change the norms and behavior of their peers regarding suicide and help-seeking. We use multiple assessments of youth and adult social networks to examine the impact of this intervention, with special attention to those youth who are most socially isolated or friends of youth who themselves have suicide thoughts and behaviors.

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