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Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations

Authors:

Shahreen Salim,

Md Naimul Hoque,

Klaus MuellerAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 8, Issue CSCW1

Article No.: 21, Pages 1 - 37

https://doi.org/10.1145/3637298

Published: 26 April 2024 Publication History

Abstract

Causal belief is a cognitive practice that humans apply everyday to reason about cause and effect relations between factors, phenomena, or events. Like optical illusions, humans are prone to drawing causal relations between events that are only coincidental (i.e., causal illusions). Researchers in domains such as cognitive psychology and healthcare often use logistically expensive experiments to understand causal beliefs and illusions. In this paper, we propose Belief Miner, a crowdsourcing method for evaluating people's causal beliefs and illusions. Our method uses the (dis)similarities between the causal relations collected from the crowds and experts to surface the causal beliefs and illusions. Through an iterative design process, we developed a web-based interface for collecting causal relations from a target population. We then conducted a crowdsourced experiment with 101 workers on Amazon Mechanical Turk and Prolific using this interface and analyzed the collected data with Belief Miner. We discovered a variety of causal beliefs and potential illusions, and we report the design implications for future research.

Supplemental Material

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This zip file contains a folder containing the survey questions of the formative and final study, and the screenshots of modules of the initial and redesigned interface.

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Cited By

Wang ABorland DPeck TWang WGotz D(2024)Causal Priors and Their Influence on Judgements of Causality in Visualized DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345638131:1(765-775)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456381

Index Terms

Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 8, Issue CSCW1

CSCW

April 2024

6294 pages

EISSN:2573-0142

DOI:10.1145/3661497

Editor:
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Apple Inc., United States

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Published: 26 April 2024

Published in PACMHCI Volume 8, Issue CSCW1

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Wang ABorland DPeck TWang WGotz D(2024)Causal Priors and Their Influence on Judgements of Causality in Visualized DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345638131:1(765-775)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456381

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