Proceedings of the Annual Meeting of the Cognitive Science Society

There are several competing theories regarding how event knowledge is represented in the mind, ranging from a strictlytemporally ordered list of activities to sets of connected scenes which may themselves consist of ordered activities. Weemployed a network science approach to provide data-driven insight into event structure. We converted sets of humangenerated activity sequences, in which roughly 25 participants list up to 12 activities for 81 different events (making asandwich, cleaning the house, taking money out of an ATM, etc.), into directed, weighted networks. Analyses of the eventnetworks revealed a complex and varied temporal structure to events. In addition, we were able to identify scenes withinevents, and use graph theory to understand activity centrality, popularity, and influence, as well as the coupling betweenthese activity characteristics. In the aggregate, we find that network science makes multiple data-driven, empiricallytestable predictions about event structure.