The timing and order in which a set of events occur strongly influences whether people judge them to be causally related. But what do people think particular temporal patterns of events tell them about causal structure? And how do they integrate multiple pieces of temporal evidence? We present a behavioral experiment that explores human causal structure induction from multiple temporal patterns of observations. We compare two simple Bayesian models that make no assumptions about delay lengths, assume that causes must precede their effects but differ in whether they assume simultaneous events can also be causally connected. We find that participants’ judgments are in line with the model that rules out simultaneous causation. Variants of this model that assume people update their beliefs conservatively provide a close fit to participants’ judgments. We discuss possible psychological bases for this conservative belief updating and how we plan to further explore how people learn about causal structure from time.
<< Back to list of publications