A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations (original) (raw)

Visual Analytics and Imaging Laboratory (VAI Lab) Computer Science Department, Stony Brook University, NY

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.

Teaser: The discrepancy networks generated from the combined crowd network and ground truth network in our first user study, designed to gauge the crowd's beliefs in the causes of some of the effects of climate change:

Each link color represents the discrepancy between the crowd and ground truth for that specific causal relation. The link colors denote the degree of discrepancy or illusion and the type (being misinformed or being oblivious). (A) shows the cases of potentially misinformed links, (B) shows the cases of potentially oblivious links, and (C) shows the cases where the crowd correctly predicted the credibility scores. The legend table (row represents crowd score, column represents credibility score) on the right shows the discrepancy/illusion score (in each cell) and the corresponding color. the credibility score increases from left to right, whereas the crowd score increases from bottom to top. Only the significant links (total crowd vote = 4) and attributes are visible.

Video: Watch it to get a quick overview how a crowd participant would build a small causal network with our interface:

Paper: S. Salim, N. Hoque, K. Mueller, �Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations.� Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1), 1-37. 2024. PDF

Funding: NSF grants IIS 1941613 and IIS 1527200