The Paradox of Time in Dynamic Causal Systems

Recent work has shown that people use temporal information including order, delay, and variability to infer causality between events. In this study we build on this work by investigating the role of time in dynamic systems, where causes take continuous values and also continually influence their effects. Recent studies of learning in these systems explored short interactions in a setting with comparatively rapidly evolving dynamics and modeled people as relying on simpler, resource-limited strategies to grapple with the stream of information (Davis et al., 2020). A natural question that arises from such an account is whether interacting with systems that unfold more slowly might reduce the systematic errors that result from these strategies. Paradoxically, we find that slowing the task indeed reduced the frequency of one type of error, but increased the error rate overall. To capture the differences between conditions, we introduce a novel Causal Event Segmentation model based on the notion that people compress the continuous scenes into events and use these to drive structure inference.