Project Description

Dr. Janet Hui-wen HSIAO

Head and Associate Professor

Dr. Janet Hui-wen HSIAO

Office: 6.23

Phone: (852) 3917-4874


Personal Homepage:

Research Lab:

HKU Researcher Page:


  • B.Sc. (Computer Science), 1999, National Taiwan University
  • M.Sc. (Computing Science), 2002, Simon Fraser University
  • Ph.D. (Cognitive Science), 2006, University of Edinburgh
  • Postdoctoral researcher, 2005-2008, University of California San Diego


  • Cognitive science
  • Computational modeling
  • Hidden Markov modeling of eye movements
  • Perceptual expertise acquisition (e.g., face recognition, reading, etc.)
  • Hemispheric asymmetry in cognitive processes
  • Psycholinguistics


  • Zhang, J., Chan, A. B., Lau, E. Y. Y., & Hsiao, J. H. (in press). Individuals with insomnia misrecognize angry faces as fearful faces while missing the eyes: An eye-tracking study. Sleep.
  • Chung, H. K. S., Leung, J. C. Y., Wong, V. M. Y., & Hsiao, J. H. (2018). When is the right hemisphere holistic and when is it not? The case of Chinese character recognition. Cognition, 178, 50-56.
  • Li, T. K., & Hsiao, J. H. (2018). Music reading expertise modulates hemispheric lateralization in English word processing but not in Chinese character processing. Cognition, 176, 159-173.
  • Chan, C. Y. H., Chan, A. B., Lee, T. M. C., & Hsiao, J. H. (in press). Eye movement patterns in face recognition are associated with cognitive decline in older adults. Psychonomic Bulletin & Review.
  • Chuk, T., Crookes, K., Hayward, W. G., Chan, A. B., & Hsiao, J. H. (2017). Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures. Cognition, 169, 120-117.
  • Coutrot, A., Hsiao, J. H., & Chan, A. B. (2018). Scanpath modeling and classification with Hidden Markov Models. Behavior Research Methods, 50(1), 362-379
  • Chuk, T., Chan, A. B., & Hsiao, J. H. (2017). Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling. Vision Research, 141, 204-216.
  • Hsiao, J. H., & Galmar, B. (2016). Holistic processing as measured in the composite task does not always go with right hemisphere processing in face perception. Neurocomputing182, 165-177.
  • Chuk, T., Chan, A. B., & Hsiao, J. H. (2014). Understanding eye movements in face recognition using hidden Markov models. Journal of Vision, 14(11):8, 1-14. Online Link
  • Tso, R. V. Y., Au, T. K., & Hsiao, J. H. (2014). Perceptual expertise: Can sensorimotor experience change holistic processing and left side bias? Psychological Science, 25(9), 1757-1767. Online Link
  • Hsiao, J. H., Cipollini, B., & Cottrell, G. (2013). Hemispheric asymmetry in perception: A differential encoding account. Journal of Cognitive Neuroscience, 25(7), 998-1007. PDF Copy
  • Hsiao, J. H., & Liu, T. T. (2012). The optimal viewing position in face recognition. Journal of Vision, 12(2):22, 1-9. Online Link 
  • Hsiao, J. H. & Cottrell, G. W. (2009). Not all visual expertise is holistic, but it may be leftist: The case of Chinese character recognition. Psychological Science, 20(4), 455-463. PDF Copy
  • Hsiao, J. H. & Cottrell, G. W. (2008). Two fixations suffice in face recognition. Psychological Science, 19(10), 998-1006. PDF Copy


Attention Brain& Cognition Lab (Room 691)

In my lab we study the relationship between brain structure and cognitive processes, using a variety of approaches including computational modeling and cognitive neuroscience (with behavioral, eye movement, and EEG/ERP measures). Current focuses of the lab research include understanding eye movement behavior and underlying cognitive processes through hidden Markov modeling, how different learning experiences influence perceptual expertise acquisition, and how different expertise domains influence each other.


– Project Title: “Understanding eye movements in cognitive tasks using EMHMM (Eye Movement analysis with Hidden Markov Models)

In this project, we will conduct eye tracking studies and analyse eye movement data using the EMHMM methodology, a machine learning based approach for eye movement data analysis. We will apply this methodology to understand eye movement patterns in research on face recognition, facial expression recognition, visual word recognition, and reading.

Intern Selection:

Good communication skills in English or Cantonese; experience in conducting psychological experiments; good grades in cognitive science, cognitive psychology, and research method related courses. Preference will be given to those with eye tracking experiences, or computational background/programming experience in Matlab.

HKU Psychology