Hey, I'm Haijiang
Currently a third-year PhD student at the University of Warwick (Psychology), fully funded by a Chancellor's International Scholarship.
Biography
I received my BSc degrees in Medical Technology and Psychology from Peking University in 2019, where I started to be interested in information processing in human brain (the most complex and fascinating machine created by nature), particularly on algorithmic level from a systematic perspective. After that, I began to study how human behavior and neural system are interacted with gaming addiction at Chinese Academy of Sciences, and received MSc there in 2022. Currently, I'm pursuing a PhD in Psychology at the University of Warwick, focusing on human behavior, cognitive science and artificial intelligence. My research explores the intersection of human cognition and AI, aiming to better understand and model how people process information, generate interpretations to the world and make decisions. On the other hand, how current AI can benefit from insights into human cognition. Outside of my main projects, I'm passionate about developing web applications that make a positive impact to scientific community. I value creativity, continuous learning, and using technology to solve real-world problems. My motto is 'Passion is all you need'.
Research Interests
Representation elicitation
Combining MCMC and generative agents to quickly recover how agents mentally represent categorical concepts through inter-chained decision-making trials.
Human-AI alignment
The central question is: How to properly force AI to learn from human behavior and mental representations?
LLM as cognitive model
Using LLM to model human behavior falls into two pathways: learn from 1) massive human responses; 2) reasoning of computational models.
Research Outputs
Articles
Yan, H., Tsvetkov, C., Chater, N., Sanborn, AN. (2024). Quickly recovering comprehensive individual mental representations of facial affect. Submitted. Preprint at https://doi.org/10.31234/osf.io/5hm49
Zhu, JQ., Yan, H., Griffith, TL. (2024). Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice. Accepted (ICLR 2025). Preprint at https://doi.org/10.48550/arXiv.2405.19313
Zhu, JQ., Yan, H., Griffith, TL. (2024). Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo. In Proceedings of the 46th Annual Meeting of the Cognitive Science Society.
Sanborn, AN., Yan, H., Tsvetkov, C. (2024). Combining meta-learned models with process models of cognition. Behavioral and Brain Sciences, 47, 43–44.
Yan, H., Li, Q., Yu, K., and Zhao, G. (2021). Large-scale network dysfunction in youths with Internet gaming disorder: a meta-analysis of resting-state functional connectivity studies. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 109, 110242.
Yan, H., Zhao, G., Yu, K., and Sun, X. (2021). What lead gaming problematically addictive for certain people instead of others? Correlates of personality, protective factors and biaxial structure of Internet Gaming Disorder. Preprint at https://doi.org/10.31234/osf.io/zdy9j.
Conferences
July, 2023. Exploring human representations of facial affect by integrating a deep generative model into Markov Chain Monte Carlo With People. Poster at the 56th Annual Meeting of the Society for Mathematical Psychology, Amsterdam, NL.
July, 2024. Recovering individual mental representations of facial affect using Markov Chain Monte Carlo with People and Gatekeepers. Talk at the 57th Annual Meeting of the Society for Mathematical Psychology, Tilburg, NL.
July, 2024. Recovering individual mental representations of facial affect using Markov Chain Monte Carlo with People and Gatekeepers. Poster at the 46th Annual Meeting of the Cognitive Science Society, Rotterdam, NL.
Projects
All-in-One MCMCP (AIOM)
An integrated application mainly built by Nodejs for users to customize and deploy a Markov Chain Monte Carlo with People (MCMCP) study or other variants.
Get in touch
Feel free to reach out directly by email at haijiang.r.yan@gmail.com