The Organization of Social Knowledge
Sociality defines human life. On an individual level, our happiness and prosperity depend greatly on our ability to interact successfully with others. We need other people to help us learn and grow, to protect and guide us, to work and build with us, to find love and make meaning. On a societal level, many of our most notable accomplishments - both good and bad - result from our ability to cooperate with each other.
The complexity of the social world makes it challenging for any one of us to understand. However, we must understand it to reap the benefits of our species' unique sociality. SCRAP Lab's research focuses on the mental and neural systems that help us make sense of the social world. We suggest that they do this by distilling the complexity of the social world down to a relatively simple representation: a set of mental maps. Just like ordinary maps help us organize our understanding of the physical world, these mental maps can help us organize the social world. They can reduce the complexity of another person's mental states, traits, or actions to coordinates on just a few key dimensions: the longitude and latitude lines of the social world.
Drawing on this perspective, we have already charted several domains of social knowledge using a combination of methods including fMRI, text analysis, computational modeling, and behavioral experiments. You can read more about this research here:
Predictive Social Cognition
Imagine you had access to a real, honest-to-goodness crystal ball: what would you choose to foresee? What moments in time - what events in your life - would you choose to preview? Many answers to this question share a common theme: people. We are intensely interested in the futures of people we care about. This interest reflects the importance of predicting the social future. If you can accurately predict what others will do, or how they will feel, you can plan your own actions accordingly to better achieve your goals.
A major branch of SCRAP Lab's research focuses on social prediction: are people accurate, and if so, how do they do achieve this accuracy? We measure the accuracy of people's predictions by comparing those predictions against measure of "ground truth" - the social reality they are trying to predict. For example, in some of the research you can read about in the links below, we used experience-sampling to measure how often certain emotions really precede or follow one another, and then compared this ground truth to perceiver's judgements of the same transitional probabilities.
We also investigate the mental and neural mechanisms that make accurate social prediction possible. In this vein, we have developed a theoretical model of predictive social cognition (see image at right, or below on mobile). This model connects social prediction to our research on the organization of social knowledge. Specifically, it claims that the maps people make of the social world have a special property: the closer two stimuli (e.g., a pair of mental states) are two one another within the map, the higher the transitional probability between them. In other words, proximity on these social maps predicts the social future. To read more about our empirical findings and theoretical model, see here:
Computational Analysis of Naturalistic Interactions
One of the lab's major new directions is the computational analysis of naturalistic social interactions. Calls for greater naturalism in social psychology go back as far as foundational figures like Kurt Lewin. However, such studies remain the exception, not the rule. The reasons are largely methodological. In order to turn recordings of natural social behaviors such as gestures, facial expressions, or speech into numbers that can be analyzed statistically, researchers must typically watch or listen to these recordings, and manually annotating what is happening in them. This is a slow, expensive, and tedious process. As such, it cannot scale up to the large datasets necessary to disentangle complex social behaviors. Fortunately, recent advances in machine learning now make it possible to automatically annotate these features. We are using these tools to understand people's verbal and nonverbal behavior - and the factors which influence this behavior - in naturalistic interactions. Watch the video below to see a demonstration of some of the lab's capabilities in this regard.
