Proceedings of the Annual Meeting of the Cognitive Science Society

Data continuously stream into our minds, guiding our learn- ing and inference with no trial delimiters to parse our experi- ence. These data can take on a variety of forms, but research on causal learning has emphasized discrete contingency data over continuous sequences of events. We present a formal framework for modeling causal inferences about sequences of point events, based on Bayesian inference over nonhomo- geneous Poisson processes (NHPPs). We show how to apply this framework to successfully model data from an experiment by Lagnado and Speekenbrink (2010) which examined human learning from sequences of point events.