Upsetting the contingency table: Causal induction over sequences of point events

Abstract

Data continuously stream into our minds, guiding our learning and inference with no trial delimiters to parse our experience. 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 inference about sequences of point events, based on Bayesian inference over nonhomogeneous Poisson processes (ɴʜᴘᴘs). We show how to apply this framework to successfully model the data from an experiment by Lagnado and Speekenbrink (2010) which examined human learning from sequences of point events.


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