Submission 579
Modeling Human Event Comprehension at Human Scale
SymposiumTalk-05
Presented by: Jeffrey M. Zacks
A grand challenge for cognitive science and neuroscience is to explain how the human brain forms and updates stable representations of events in the stream of behavior. One family of theories propose that event updating is triggered by transient failures in prediction quality. Prediction quality has two aspects, prediction error, or surprisal; and uncertainty, or entropy. One major barrier to understanding their roles has been constructing experimental paradigms and computational frameworks that can be directly compared to human behavior and brain activity. A second barrier is posed by the fact that in naturalistic activity, these two potential signals are correlated. This means it takes a relatively large sample of activities to tease them apart. In this talk, I will describe a large corpus of highly instrumented action recordings and accompanying behavioral and functional MRI measurements. Using these together with a computational model, we have tested the roles of prediction error and prediction uncertainty in event updating. The data support important roles for both signals in human event comprehension.