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The Emergence of Causal Curiosity from Prior Causal Belief Networks

Zhuoyu Shi, Xintong Jiang, Bohan Jiang, Fred Morstatter

arXiv:2609.27946v1cs.SI

TL;DR

The paper examines how causal curiosity relates to prior causal belief structures, a relationship the authors say has received little systematic real-world investigation. Using Reddit data and language models, it finds that curiosity is structured around prior concepts, positive nouns, cause-like concepts, and central network nodes.

  • Problem

    It remains unclear whether causal curiosity arises independently or is anchored in existing belief networks, despite their role in explaining and predicting the world.

    Source: Introduction · p. 1

  • Method

    The authors build causal networks from Reddit posts and comments from 2020–2022, then use a language-model classifier to identify causal curiosity in 2023 “why” questions.

    Source: Introduction · p. 1

  • Results

    Nearly 92% of nouns in curiosity-driven questions were already in prior subreddit networks, and questions also favored positive, cause-like, and structurally central concepts.

    Source: Discussion · p. 8

  • Takeaways & Limitations

    The authors conclude that causal curiosity is systematically grounded in prior knowledge structures rather than randomly distributed.

    Source: Conclusion · p. 9

    Reddit posts may contain personally identifiable information, and platform-specific user demographics may introduce bias.

    Source: Limitations and Ethical Considerations · p. 9

Abstract

from arXiv · show

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.

Introduction

The paper asks whether causal curiosity arises independently or is anchored in existing causal belief networks, and examines how prior knowledge structures relate to the concepts people question. It places that question within work on causal curiosity and mental models.

Related Work

  • Causal curiosity concerns understanding why events happen, how mechanisms work, and how outcomes can be explained or anticipated.The introduction distinguishes causal explanations from mere descriptions and associations.Source: Introduction · p. 1

Introduction

  • The study addresses the gap in understanding whether causal curiosity arises independently or from existing belief networks, and how it relates to those structures.The authors identify this relationship as relevant to question generation and the evolution of collective knowledge.Source: Introduction · p. 1
  • Using Reddit discourse, the authors construct subreddit-specific causal networks from 2020–2022 and analyze 2023 “why” questions across four research questions.The questions concern grounding in prior structures, sentiment, cause-versus-effect direction, and network location.Source: Introduction · p. 1
  • The reported pattern is that curiosity draws on pre-existing concepts, favors positively valenced nouns and causes, and clusters around structurally central ideas.The authors characterize this pattern as systematic rather than random.Source: Introduction · p. 2

Related Work

  • Causal belief networks represent structured cause-and-effect knowledge that supports explaining outcomes, predicting events, and designing interventions.The related-work discussion frames such models as mental representations of links among events, agents, and variables.Source: Causal Belief and Mental Models · p. 2

Data Description

The study uses Reddit posts and comments to build community-specific causal networks and identify curiosity-driven questions, with network-building data preceding the questions in time. It validates question classification and limits the final analysis to selected communities.

Data Description

  • The dataset covers the 200 most active and influential subreddits and focuses on January 2020 through December 2023.The analysis builds causal networks from 2020–2022 material and examines expressed curiosity in 2023.Source: Data Description · p. 2

Computational Methods

  • A RoBERTa-based pipeline extracts explicit cause-and-effect spans, whose head nouns are used to build directed, weighted subreddit networks.The resulting networks serve as the study’s mental models.Source: Extracting Cause-and-Effect Pairs · pp. 2–3
  • The authors selected 732,752 Reddit post titles from 2023 containing the token why as candidates, then used LLaMA-3.3-70B to distinguish curiosity-seeking questions from other uses.The model was prompted for title-only and title-and-selftext variants. It identified 535,506 of 582,309 title-only posts (91.96%) and 133,448 of 150,443 title-and-selftext posts (88.70%) as true curiosity-seeking questions; for each identified question, the authors extracted the noun in the title with Spacy.Source: Detecting Causal Curiosity · p. 3
  • Human validation reports LLaMA precision of 91% for title-only posts and 88% for title-and-selftext posts, with Fleiss’ Kappa agreement of 78.0% and 79.3%, respectively.The validation study sampled 200 posts, split evenly between the two formats.Source: Validation of Causal Curiosity Detection · p. 3
  • The final analysis includes 15 subreddits after applying minimum curiosity-post, noun-token, and causal-pair thresholds and excluding overly broad or diffuse communities.The thresholds include at least 500 curiosity posts in 2023, more than 1,000 distinct nouns, and over 100,000 causal pairs in 2022.Source: Data Finalization · p. 3, Table 1 · p. 3
  • The final subreddit networks combine causal pairs from 2020, 2021, and 2022, totaling 15,019,623 extracted pairs.The three-year directed, weighted networks are constructed after the 2022 extraction step.Source: Data Finalization · p. 4

RQ1 – Causal Curiosity Emerge from Existing

Curiosity-driven questions largely concern concepts already represented in prior subreddit causal networks. The reported patterns also show a positive-valence skew, with proposed motivational explanations for that selectivity.

Curiosity

  • Across subreddits, positive nouns are disproportionately more likely than negative or neutral nouns to be objects of causal curiosity.In r/dating, over 18% of positive nouns appeared in curiosity questions, compared with 10% of negative nouns; the margin was narrower in some analytical communities.Source: Curiosity · p. 4, Figure 1 · p. 5

Knowledge Structures

  • 91.7% mean noun overlap (SD = 3.1%) across 15 subreddits shows that 2023 curiosity-title nouns commonly appeared in 2020–2022 causal networks.Overlap ranged from 84.8% in r/dating to 95.8% in r/soccer.Source: Table 2 · p. 4, Knowledge Structures · p. 4

Curiosity

  • The authors suggest that questions about positive outcomes may be motivated by their perceived replicability, desirability, and usefulness for guiding future behavior.They contrast these potentially actionable explanations with negative events that may offer less incentive or actionability for exploration.Source: Curiosity · pp. 4–5
  • The discussion proposes affective forecasting as one possible explanation for why positive concepts anchor causal inquiries.It links positive entities with anticipated reward, such as success, satisfaction, or improvement.Source: Curiosity · p. 5
  • The authors conclude that curiosity is affectively selective and may reflect motivational value as well as knowledge gaps.They describe positive concepts as more strongly attracting causal curiosity than other valences.Source: Curiosity · p. 5

RQ3 - Directionality in Causal Curiosity

Causal curiosity in Reddit communities is biased toward concepts that function more as causes than effects. Across subreddits, cause-dominant concepts appear more often in curiosity-seeking questions, a pattern linked to their explanatory and practical leverage.

RQ3 - Directionality in Causal Curiosity

  • The analysis classifies a noun as more cause-like when its out-degree exceeds its in-degree, and more effect-like when its in-degree is at least its out-degree.In-degree counts appearances as an effect; out-degree counts appearances as a cause.Source: RQ3 - Directionality in Causal Curiosity · p. 5
  • Across all 15 subreddits, cause-dominant nouns were significantly more likely to appear in causal curiosity questions; in formula1, 66.9% were causal antecedents versus 33.1% effect-like concepts.Similar cause-dominant shares were reported in r/soccer (67.0%), r/DnD (63.8%), and r/movies (62.7%).Source: RQ3 - Directionality in Causal Curiosity · p. 5, Figure 2 · p. 6
  • Curiosity questions more often target antecedent factors, agents, or mechanisms that produce outcomes than the outcomes themselves.The pattern is described as curiosity directed upstream in the community’s causal mental model.Source: RQ3 - Directionality in Causal Curiosity · pp. 5–6
  • The authors suggest that causes may attract curiosity because understanding them offers greater opportunity to intervene, replicate, or control future events.Effects are characterized as downstream observations that are less informative for intervention.Source: RQ3 - Directionality in Causal Curiosity · p. 6

RQ4 - Structural Centrality and Causal

Causal curiosity is concentrated on concepts with high degree and betweenness centrality in subreddit networks. The authors interpret these targets as structurally pivotal hubs and bridges in community causal discourse.

RQ4 - Structural Centrality and Causal

  • The analysis ranks nouns by degree and betweenness centrality, bins each measure into percentiles, and computes the proportion of curiosity-driven mentions in each bin.Degree measures the number of direct causal connections, while betweenness captures how much a concept bridges network parts.Source: Curiosity · p. 6
  • The top 10% of nodes accounted on average for 37.7% of curiosity-driven mentions by degree centrality and 35.4% by betweenness centrality.For degree centrality, subreddit values ranged from 13.7% in r/soccer to 55.7% in r/Eldenring; for betweenness, from 13.2% to 52.9%, respectively.Source: Figure 3 · p. 7, Figure 4 · p. 7, Curiosity · p. 8
  • The authors interpret central targets as hubs or bridges that organize causal discourse and connect otherwise disjoint parts of the belief network.They suggest that understanding these concepts may offer broader inferential payoff.Source: Curiosity · p. 8
  • Central nodes may also be more frequently discussed, visible, and cognitively accessible, making them shared focal points for public inquiry.The authors frame this as a social dimension of explanation-seeking.Source: Curiosity · p. 8

Discussion

The discussion presents causal curiosity as systematically grounded in prior community knowledge and patterned by concept valence, causal role, and network centrality. These patterns are interpreted as reflecting both explanatory leverage and social visibility.

Discussion

  • Nearly 92% of nouns in curiosity-driven questions were already embedded in subreddit causal networks from the preceding three years.The authors describe these shared mental models as epistemic infrastructure that both limits and supports new causal inquiry.Source: Discussion · p. 8
  • Positive nouns were disproportionately likely to appear in causal curiosity-driven questions across domains.The authors connect this pattern to curiosity about positive outcomes as potentially actionable and motivating.Source: Discussion · p. 8
  • Curiosity-driven questions disproportionately target cause-like concepts rather than effect-like concepts, which the authors associate with the potential to intervene, replicate, or control outcomes.The discussion frames this directional focus as a preference for explanatory control.Source: Discussion · p. 8
  • The most central 10% of nodes accounted for more than one-third of curiosity-driven mentions, while peripheral concepts were almost entirely absent.The authors describe central nodes as hubs or bridges with high inferential leverage.Source: Discussion · pp. 8–9
  • The discussion also links centrality to social exposure: central concepts are more frequently discussed, more visible, and more cognitively accessible.The authors characterize curiosity as both cognitively strategic and socially scaffolded.Source: Discussion · p. 9

Limitations and Ethical Considerations

The study notes ethical and methodological constraints associated with Reddit data and automated causal-network construction.

Limitations and Ethical Considerations

  • Reddit posts may contain personally identifiable information or offensive language, and platform-specific demographics could bias the observed patterns.The authors report using aggregate patterns and avoiding identifiable information, while emphasizing privacy and consent concerns.Source: Limitations and Ethical Considerations · p. 9
  • Automated extraction of causal networks can introduce noise through parsing errors, semantic ambiguity, and incomplete representations of belief structures.Source: Limitations and Ethical Considerations · p. 9
  • The authors identify possible misuse of the work in manipulation and political campaigns, alongside risks from recontextualizing community discourse.The study received IRB approval for its annotation study with Prolific annotators.Source: Limitations and Ethical Considerations · p. 9

Future Work

The authors propose broader curiosity detection and multimodal analysis, as well as longitudinal study of how events may reshape causal curiosity.

Future Work

  • Refining linguistic models to capture more question forms and pragmatic cues could provide a more comprehensive picture of epistemic inquiry.Source: Future Work · p. 9
  • Multimodal extensions could examine how curiosity manifests through images, memes, or links.Source: Future Work · p. 9
  • Longitudinal designs could trace how events reshape causal curiosity and potentially alter which concepts become epistemically central.Examples include global crises, technological innovations, and major sporting outcomes.Source: Future Work · p. 9

Conclusion

The conclusion presents causal curiosity as systematically grounded in prior belief structures and directed toward familiar, positive, causally generative, and central concepts.

Conclusion

  • Causal curiosity emerges systematically within prior belief structures and is directed toward familiar concepts.Source: Conclusion · p. 9
  • Curiosity is biased toward positive and rewarding concepts.Source: Conclusion · p. 9
  • Causal curiosity is directed upstream toward causes rather than downstream toward effects.Source: Conclusion · p. 9
  • Curiosity clusters around central nodes that structure discourse.Source: Conclusion · p. 9

Checklist

The checklist records affirmative answers about the paper’s stated scope, methods, limitations, ethics, and data practices.

Checklist

  • The checklist says the paper’s main claims accurately reflect its contributions and scope, and that its methodological approach is appropriate.Source: Checklist · p. 10
  • The authors report discussing limitations, potential negative societal impacts, possible misuse, and steps to mitigate negative outcomes.Source: Checklist · p. 10
  • The checklist reports citation of existing asset creators, a license discussion, no new assets, and discussion of consent and data handling.Source: Checklist · p. 11
  • The authors report obtaining university IRB approval for the annotation experiment and providing participant instructions and screenshots in the Validation and Appendix sections.Source: Checklist · p. 11

Appendix

The appendix provides classification prompts for two post formats, annotation examples, and annotation-task cost information.

Appendix

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