Sean Guo

I am an RGC postdoc at the Hong Kong University of Science and Technology (HKUST), currently researching AI-generated content, misinformation and large language models from a psychological perspective.

Research Interests

AI-generated content (AIGC)
Large Language Models
Mechanisms of belief
Visual misinformation
Memory

Education

🎓 Hong Kong University (HKU)

PhD in Psychology, 2021 - 2025

🎓 University of California, Los Angeles (UCLA)

B.S. in Psychobiology, 2016-2020

Minor in Cognitive Science
Specialization in Computing

Research

People are more susceptible to misinformation with realistic AI-synthesized images that provide strong evidence to headlines

Guo, S., Zhong, Y., & Hu, X. (2025), Harvard Kennedy School Misinformation Review

Here, we examine how properties of AI-synthesized images influence belief in misinformation and memory for corrections. Realistic and probative (i.e., providing strong evidence) images predicted greater belief in false headlines. Additionally, we found preliminary evidence that paying attention to properties of images could selectively lower belief in false headlines. Our findings suggest that advances in photorealistic image generation will likely lead to greater susceptibility to misinformation, and that future interventions should consider shifting attention to images.

https://doi.org/10.37016/mr-2020-189

Specific media literacy tips improve AI-generated visual misinformation discernment

Guo, S., Swire-Thompson, B. & Hu, X. (2025), Cognitive Research: Principles and Implications

As AI-generated images become increasingly realistic, distinguishing fact from fiction online grows harder. In this study, targeted tips on spotting AI images helped participants better identify false AI-generated images more so than general media literacy tips, though both approaches slightly reduced belief in true headlines. The results highlight that specific guidance can curb AI misinformation, but preserving trust in real information remains a challenge.

https://doi.org/10.1186/s41235-025-00648-z

Faster initial retrieval of misinformation corrections predicts better long-term memory: An ERP study

Guo, S., Chen, D., Hu, W., & Hu, X. (2026), Neuropsychologia

Even after misinformation is corrected, false beliefs can linger. Using EEG, we explored how people encode 'true' and 'false' events, their causes, and corrections/affirmations. Quick correct memory for whether an event was true or false predicted better memory the next day. EEG signals revealed that corrections evoke weaker P300 responses but stronger frontal slow-wave activity when successfully remembered, suggesting that processing corrections is more cognitively demanding than processing affirmations.

https://doi.org/10.1016/j.neuropsychologia.2026.109428

Providing an alternative explanation improves misinformation rejection and alters event-related potentials during veracity judgements

Guo, S., Chen, D., & Hu, X. (2025), Brain and Cognition

In this EEG study, we examine how the brain encodes and later evaluates corrected information. Providing an alternative explanation alongside a retraction significantly reduced the continued influence effect and enhanced neural markers of memory encoding and recollection during later truth judgments. Therefore, replacing misinformation with a plausible alternative could strengthen corrective processing in memory, offering new insight into the neurocognitive mechanisms that make misinformation persistent, and how to counter it more effectively.

https://doi.org/10.1016/j.bandc.2025.106290

Skills

Software

Python, R, Qualtrics, MATLAB (EEGLAB), PsychoPy

AI Tools

Large language model APIs, Qualtrics chatbot integration, Midjourney

Languages

English, Chinese (Mandarin), Chinese (Cantonese), Japanese, French

Publications & Works in Progress

Get in touch at seanguo@ust.hk

Last Update: 6 Oct. 2026

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