Proceedings of the Annual Meeting of the Cognitive Science Society

Recent work has shown that people use temporal informationincluding order, delay, and variability to infer causality be-tween events. In this study we build on this work by investi-gating the role of time in dynamic systems, where causes takecontinuous values and also continually influence their effects.Recent studies of learning in these systems explored short in-teractions in a setting with comparatively rapidly evolving dy-namics 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 anaccount is whether interacting with systems that unfold moreslowly might reduce the systematic errors that result from thesestrategies. Paradoxically, we find that slowing the task indeedreduced the frequency of one type of error, but increased the er-ror rate overall. To capture the differences between conditions,we introduce a novel Causal Event Segmentation model basedon the notion that people compress the continuous scenes intoevents and use these to drive structure inference.