Journal article
Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations
Proceedings of the ACM on human-computer interaction, Vol.8(CSCW1), pp.1-37
04/23/2024
DOI: 10.1145/3637298
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.
Details
- Title: Subtitle
- Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations
- Creators
- Shahreen Salim - Stony Brook UniversityMd Naimul Hoque - University of Maryland, College ParkKlaus Mueller - Stony Brook University
- Resource Type
- Journal article
- Publication Details
- Proceedings of the ACM on human-computer interaction, Vol.8(CSCW1), pp.1-37
- Publisher
- ACM
- DOI
- 10.1145/3637298
- ISSN
- 2573-0142
- eISSN
- 2573-0142
- Number of pages
- 37
- Grant note
- IIS 1941613, IIS 1527200 / NSF (National Science Foundation) (http://dx.doi.org/10.13039/https://doi.org/10.13039/100000001)
- Language
- English
- Date published
- 04/23/2024
- Academic Unit
- Computer Science
- Record Identifier
- 9984787260102771
Metrics
1 Record Views