Research Overview
Research Overview
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Every day, people are bombarded with information from a wide variety of sources, and yet they do not have nearly enough time to process it. How do people sift through tremendous amounts of information and decide what to use when making inferences? What cues do people rely on to distinguish between false and factual information? How do people integrate information from various social media sites, which present content in multiple formats and evoke different sensory pathways? What do people do when they face conflicting evidence? My research investigates these core questions as well as their implications in education and business.
Every day, people are bombarded with information from a wide variety of sources, and yet they do not have nearly enough time to process it. How do people sift through tremendous amounts of information and decide what to use when making inferences? What cues do people rely on to distinguish between false and factual information? How do people integrate information from various social media sites, which present content in multiple formats and evoke different sensory pathways? What do people do when they face conflicting evidence? My research investigates these core questions as well as their implications in education and business.
Social vs. Statistical Information
Social vs. Statistical Information
How do people use social evidence to guide their decisions? Are the mechanisms sufficiently sophisticated to take advantage of social cues in current environments? The rise of Internet has led to the proliferation of social information, such as user-generated content (UGC). According to BrightLocal’s 2019 Local Consumer Review Survey, over 80% of consumers read online reviews for businesses. When choosing among a choice set containing similar options, prospective consumers are apt to rely on insights provided by those with direct experience using a good or a service. Given the ubiquity of online word-of-mouth and the popularity of online shopping, this stream of my research uses a cognitive science perspective to explore how consumers combine information from various sites and presented in different formats to make everyday inferences. Specifically, I ask how consumers infer product quality and product experience by integrating different attributes of reviews, such as average product ratings (valence), number of reviews (volume), distributions of ratings (variance), and text of individual reviews (content).
Representative publications:
Powell, D., Yu, J., DeWolf, M., & Holyoak, K. J. (2017). The love of large numbers: A popularity bias in consumer choice. Psychological Science, 28(10), 1432-1442. [link][pdf]
Statistical Inference through Visualization
Statistical Inference through Visualization
Despite being the driving force behind statistics and despite being useful in virtually every aspect of life, statistical inference is one of the most challenging applications of statistics. Can we enhance inferential reasoning, both formal and informal, in an intuitive way by leveraging the visual system? We have created and upgraded our own game-based interventions to investigate how well statistical rules apply to intuitive judgments of information conveyed through visual displays.
Representative publications:
Yu, J., Goldstone, R. L., & Landy, D. (2018). Experientially Grounded Learning About the Roles of Variability, Sample Size, and Difference Between Means in Statistical Reasoning. In T. T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 2747 – 2752). Austin, TX: Cognitive Science Society. [pdf]
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