Colloquium Recording Here

Title: Testing a network science model of event knowledge

Abstract:

People's knowledge of common events such as "going grocery shopping" is rich and complex. 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 temporal structure of event knowledge in memory. In this talk, I will describe how we are using network science to provide insights into the temporal structure of common events, and to generate and test predictions for human studies. Based on participants' production and ordering of the activities that make up events, we established empirical profiles for 80 common events to characterize their temporal structure. I will present how we have used the resulting event networks to investigate multiple issues regarding the variability in the richness and complexity of people’s knowledge of the temporal structure of common events, including: how network centrality measures predict human rankings of the centrality of an event’s activities; the degree to which scenes (network communities) are present in various events, and how we might test this; and similarities among events in terms of both their content and their temporal structure, and how we plan to test whether or not this aspect of event knowledge is encoded in memory. Overall, I will argue that implemented event networks provide novel insights into human event knowledge.

Bio:

I received my undergraduate degree in Cognitive Science at Queen's University in Kingston, Ontario, and my M.Sc. and Ph.D. in Psychology at McGill University in Montreal, Quebec with Mark Seidenberg. I did my postdoc with Mike Tanenhaus at the University of Rochester. I currently am a Professor at the University of Western Ontario in London, Ontario, Canada, where I have been a faculty member for 32 years. I have been extremely fortunate to have worked with a number of wonderful colleagues and excellent trainees for a long time. Over the years, with my colleagues and trainees, we have studied word recognition, object concepts, sentence comprehension, and the role of event knowledge in understanding language. In each of these areas, we have used computational models to implement theoretical principles, and to generate and test predictions for human empirical studies with neurotypical and multiple other populations. For the past few years, we have focused on people's knowledge of common events.

Readings:

Brown, K. S., Hannah, K. E., Christidis, N., Hall-Bruce, M., Stevenson, R. A., Elman, J. L., & McRae, K. (2024). Using network science to provide insights into the structure of event knowledge in memory. Cognition, 251. [32 pages] https://doi.org/10.1016/j.cognition.2024.105845

McRae, K., Brown, K. S., & Elman, J. L. (2021). Prediction-based learning and processing of event knowledge. Topics in Cognitive Science, 13, 206-223. https://doi.org/10.1111/tops.12482