Dr Di Fu
About
Biography
Dr. Di Fu (Chinese name: 傅迪 or 付迪, Di pronounces as "Dee", which means "enlightenment and inspiration" in Chinese; she guesses this is why she is so enthusiastic about human minds and intelligence) is an assistant professor (UK Lecturer) at the Department of Psychology, University of Surrey. She is the director of the Digital Intelligence for Future Users (DIFU) Lab. Her research group focuses on crossmodal learning and human-robot social interaction.
Before that, she worked as a postdoctoral research associate at the Department of Informatics, University of Hamburg. She got her fast-tracking Ph.D degree in cognitive neuroscience at the Institute of Psychology, Chinese Academy of Sciences, in 2020.
She had been honored as an outstanding graduate of CAS and an outstanding doctoral graduate of Beijing. She has been awarded the Kavli Summer Institute in Cognitive Neuroscience fellowship, the International Postdoctoral Exchange fellowship, the CAS-DAAD joint doctoral student fellowship, and the Chinese National Academic Scholarship.
Her work has been published in the International Journal of Social Robotics, Public Administration Review, NeuroImage, iScience, IEEE IROS, ACM/IEEE HRI, IEEE RO-MAN, IEEE IJCNN, etc.
She also serves on the committees of the Chinese Association for Psychological & Brain Sciences and the Chinese German Association for Biology and Medicine.
Areas of specialism
University roles and responsibilities
- Director of Digital Intelligence for Future Users Lab
- Prgramme Co-leader for MSc Game Design and Digital Innovation
- Social Media Lead for the School of Psychology
Human Impression of Humanoid Robots Mirroring Social Cues
Mirroring non-verbal social cues such as affect or movement can enhance human-human and human-robot interactions in the real world. The robotic platforms and control methods also impact people's perception of human-robot interaction. However, limited studies have compared robot imitation across different platforms and control methods. Our research addresses this gap by conducting two experiments comparing people's perception of affective mirroring between the iCub and Pepper robots and movement mirroring between vision-based iCub control and Inertial Measurement Unit (IMU)-based iCub control. We discovered that the iCub robot was perceived as more humanlike than the Pepper robot when mirroring affect. A vision-based controlled iCub outperformed the IMU-based controlled one in the movement mirroring task. Our findings suggest that different robotic platforms impact people's perception of robots' mirroring during HRI. The control method also contributes to the robot's mirroring performance. Our work sheds light on the design and application of different humanoid robots in the real world.
Supervision
Completed postgraduate research projects I have supervised
- Ziwei Chen (Phd), Social Attention Mechanisms under Face Pareidolia Process, Institute of Psychology, Chinese Academy of Sciences
- Eric Bergter (MSc), Human Navigation Driven: Modeling Visual Localization with Cognitive Graphs and Local Scenes, Department of Informatics, University of Hamburg
- Maximilian Keiff (MSc), Social Attention Prediction in A Free-viewing Eye Tracking Task, Department of Informatics, University of Hamburg
- Navneet Singh Arora (MSc), Multimodal Representational Learning for Dimensional Emotion Recognition, Department of Informatics, University of Hamburg
Teaching
- PSYM034 MSc Dissertation (Module Lead and Dissertation Supervisor)
- PSY3065 UG Dissertation (Dissertation Supervisor)
- PSYM155 Psychology and Game Design II (Module Lead)
Publications
The COVID-19 pandemic has plunged the world into a crisis. To contain this crisis, it is essential to build full cooperation between the government and the public. However, it is unclear which governmental and individual factors are determinants and how they interact with protective behaviors against COVID-19. To resolve this issue, this study builds a multiple mediation model. Findings show that government emergency public information such as detailed pandemic information and positive risk communication had greater impact on protective behaviors than rumor refutation and supplies. Moreover, governmental factors may indirectly affect protective behaviors through individual factors such as perceived efficacy, positive emotions, and risk perception. These findings suggest that systematic intervention programs for governmental factors need to be integrated with individual factors to achieve effective prevention and control of COVID-19 among the public.
Additional publications
My full publication record: https://scholar.google.com/citations?user=AUqLvaIAAAAJ&hl=en