Yunnie Liu (she/her)
Research Interests
My research focuses on complex emotion processing, multi-modal information integration, and self-concept development & reconstruction in human–computer interaction. Recently, I am also exploring how large language models can be used to extract complex affective concepts and how algorithmic features (e.g., repetitiveness and explainability) shape users’ agency, mental models, and decision-making.
Education
- 2025.09—Present | Peking University, School of Psychological and Cognitive Sciences:
Ph.D. candidate in Applied Psychology, Self Exploration and Meaningful Existence Lab. My work includes validating LLMs’ ability to extract complex affective concepts with computational grounded theory, and investigating how algorithmic repetitiveness and explainability influence users’ agency, self-concept, mental-model construction, trust, and product preference.
- 2019.09—2022.07 | Institute of Psychology, Chinese Academy of Sciences:
I specialized in Cognitive Psychology under Prof. Xingshan Li at the Reading and Visual Cognition Lab, IPCAS. With an impeccable GPA of 3.62/4, I harnessed rigorous training in experimental tools for text and speech processing and techniques like eye-tracking methodologies., making me proficient in designing experiments involving audio-visual modalities and analyzing data.
My dissertation, titled "Cross-Modal Transfer of Word Knowledge from Auditory Modality to Visual Modality", is a testament to my capability in designing, executing, and interpreting complex experiments.
- 2014.09—2018.07 | Guilin University of Electronic Technology:
Besides the foundational courses, my bachelor's phase emphasized hands-on projects, enhancing my technical agility. I took a keen interest in Information System Analysis and algorithmic strategies, which paved the way for my future experimental endeavors.
Research Experiences
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2025—Present | Self Exploration and Meaningful Existence Lab, Peking University
I conduct research on (1) LLM-based complex affect representation and human-alignment validation, (2) interventions on how algorithmic repetitiveness and feedback mechanisms shape users’ agency and possible selves, and (3) XAI and decision modeling: testing nonlinear trade-offs between explanation cost, trust, and preference.
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2024 | Child Language & Reading Research Lab, Peking University
Understanding dyslexia with multi-modal machine learning: training models on resting-state EEG and multimodal features (neural + behavioral + family environment) to diagnose and rank key predictors of reading ability and dyslexia.
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2023—2024 | MRI Center, Academy of Advanced Interdisciplinary Studies, Peking University
Worked on multi-modal studies (MRI/EEG/behavior) in the CCBD cohort and special cohorts with language/reading difficulties. As a lead experimenter, I executed protocols involving fMRI and EEG and supported cognitive behavioral tasks in children, while analyzing behavioral and resting-state neuroimaging data related to human agent perception and cognitive mechanisms.
- 2020—2022 | Institute of Psychology, Chinese Academy of Sciences:
I led a series of cross-modal studies, where my hands-on approach saw me using tools like E-prime, ExperimentBuilder, and PsychoPy. My analytic mindset complemented these skills, as I derived intricate insights using linear mixed models in R.
- 2017-2018 | Guilin University of Electronic Technology:
My tech-savvy nature came to the forefront as I developed algorithm-driven projects like the "Book Lending Management System" and played a critical role in the "Intelligent Robot Companion Box", integrating advanced algorithms with human-robot interactive modules.
Publications
My experimental skills reflect in my contributions to various research papers:
- Liu, J., Gu, J., Feng, C., Shi, W., Biemann, C., & Li, X. (2023). Cross-Modal Impact of Recent Word Encountering Experience. Scientific Studies of Reading.
- Liu, J., Yulu, S., & Meng, X. (in preparation). Understanding Dyslexia: Leveraging Multi-Modal Machine Learning for Diagnosis and Feature Ranking.
- Gu, J., Zhou, J., Bao, Y., Liu, J., Perea, M., & Li, X. (2022). The effect of transposed-character distance in Chinese reading. Journal of experimental psychology. Learning, memory, and cognition, 10.1037/xlm0001180.
- Zhang, G., Yao, P., Ma, G., Wang, J., Zhou, J., Huang, L., Xu, P., Chen, L., Chen, S., Gu, J., Wei, W., Cheng, X., Hua, H., Liu, P., Lou, Y., Shen, W., Bao, Y., Liu, J., Lin, N., & Li, X. (2022). The database of eye-movement measures on words in Chinese reading. Scientific Data, 9(1), 411. doi:10.1038/s41597-022-01464-6.
Skills & Advanced Proficiencies
- Neuroimaging: Proficient in fMRI and EEG methodologies tailored for diverse research scenarios.
- Experimental Software: Mastery over tools such as E-prime, ExperimentBuilder, and PsychoPy.
- Programming: Equipped with a national level 2 certification in C language, with hands-on experience in algorithmic strategies.