No abstract

length: 150 words Abstract Surprise signals a discrepancy between predicted and observed outcomes. It is theorized to segment the flow of experience into discrete perceived events, drive affective experiences, and create particularly resilient memories. However, the ability to precisely measure naturalistic surprise has remained elusive. We used advanced basketball analytics to derive a quantitative measure of surprise and characterized its behavioral, physiological, and neural effects on human subjects observing basketball games. We found that surprise served to segment ongoing experiences, as reflected in subjectively perceived event boundaries and shifts in neocortical neural patterns underlying belief states. Interestingly, these effects differed by whether surprising moments contradicted or bolstered current predominant beliefs.Surprise also positively correlated with pupil dilation, processing in subcortical regions associated with dopamine, game enjoyment, and, along with these physiological and neural measures, long-term memory. These investigations support key predictions from event segmentation theory and extend theoretical conceptualizations of surprise to real-world contexts.

Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., releasing comprehensive information about variables in a format amenable to both automated and manual processing) can make the task of data preparation less arduous and less error prone for all types of data analysis. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.