The Emergence of Causal Curiosity from Prior Causal Belief Networks

Abstract

Causal curiosity is foundational to human cognition. It is the desire to understand why events happen, what mechanisms underlie them, and how outcomes can be explained or anticipated. It motivates exploration, sustains attention, and fuels the search for new knowledge. However, despite consensus on the importance of causal curiosity, little is known about how causal curiosity arises from existing belief systems. In our work, we examine how causal curiosity emerges from prior knowledge structures. With Reddit data from 2020 to 2023, and leveraging language models to extract cause-and-effect relationship pairs and identify causal curiosity-driven questions, our findings reveal that causal curiosity is not random or independent, but largely rooted in prior knowledge structure. Particularly, \textit{positive} nouns are more likely to be the object of causal curiosity. Moreover, our findings indicate that concepts that serve more as \textit{causes} are more likely to appear in causal curiosity than those that serve more as effects. Lastly, our results also suggest that causal curiosity emerges from the \textit{central} of the prior belief network. These novel insights reveal that causal curiosity is not random but systematically grounded in prior knowledge structures, and suggest their implication to facilitate the human learning process by motivating people to actively explore and construct deeper understandings rather than passively receiving information.