Active causal structure learning in continuous time


Research on causal learning has largely focused on learning and reasoning about contingency data aggregated across discrete observations or experiments. However, this setting represents only the tip of the causal cognition iceberg. A more general problem lurking beneath is that of learning the latent causal structure that connects events and actions as they unfold in continuous time. In this paper, we examine how people actively learn about causal structure in a continuous time setting, focusing on when and where people intervene on the system and how this shapes their learning. Across two experiments, we find that participants’ accuracy depends on both the informativeness and evidential complexity of the data they generate. Moreover, intervention choices strike a balance between maximizing expected information and minimizing expected inferential complexity. That is, we find people time and target their interventions to create simple yet informative causal dynamics. We discuss how the continuous-time setting challenges existing computational accounts of active causal learning, and argue that metacognitive awareness of one’s inferential limitations plays a critical role for successful learning in the wild.

Gong, T., Gerstenberg, T., Mayrhofer, R., Bramley, N. R. (2022). Active causal structure learning in continuous time. In PsyArXiv.

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