Continuous time causal structure induction with prevention and generation

Most research into causal learning has focused on atemporal contingency data settings while fewer studies have examined learning and reasoning about systems exhibiting events that unfold in continuous time. Of these, none have yet explored learning about preventative causal influences. How do people use temporal information to infer which components of a causal system are generating or preventing activity of other components? In what ways do generative and preventative causes interact in shaping the behavior of causal mechanisms and their learnability? We explore human causal structure learning within a space of hypotheses that combine generative and preventative causal relationships. Participants observe the behavior of causal devices as they are perturbed by fixed interventions and subject to either regular or irregular spontaneous activations. We find that participants are capable learners in this setting, successfully identifying the large majority of generative, preventative and non-causal relationships but making certain attribution errors. We lay out a computational-level framework for normative inference in this setting and propose a family of more cognitively plausible algorithmic approximations. We find that participants' judgment patterns can be both qualitatively and quantitatively captured by a model that approximates normative inference via a simulation and summary statistics scheme based on structurally local computation using temporally local evidence.