Using network science to provide insights into the structure of event knowledge

Abstract

The structure of event knowledge plays a critical role in prediction, reconstruction of memory for personal events, construction of possible future events, action, language usage, and social interactions. Despite numerous theoretical proposals such as scripts, schemas, and stories, the highly variable and rich nature of events and event knowledge have been formidable barriers to characterizing the structure of event knowledge in memory. We used network science to provide insights into the temporal structure of common events. Based on participants' production and ordering of the activities that make up events, we established empirical profiles for 80 common events to characterize the temporal structure of activities. We used the event networks to investigate multiple issues regarding the variability in the richness and complexity of people's knowledge of common events, including: the temporal structure of events; event prototypes that might emerge from learning across many experiential instances and be expressed by people; the degree to which scenes (communities) are present in various events; the degree to which people believe certain activities are central to an event; how centrality might be distributed across an event's activities; and similarities among events in terms of their content and their temporal structure. Thus, we provide novel insights into human event knowledge, and describe 18 predictions for future human studies.