The basic goal of my research is to investigate how humans learn and reason, and how intelligent machines might emulate them. In tasks that arise both in childhood (e.g., perceptual learning and language acquisition) and in adulthood (e.g., action understanding and causal inference), humans often paradoxically succeed in making inferences from inadequate data. The data available are often sparse (very few examples), ambiguous (multiple possible interpretations), and noisy (low signal-to-noise ratio). How can an intelligent system cope?
I approach this basic question as it arises in both perception and higher cognition. My research is highly interdisciplinary, integrating theories and methods from psychology, statistics, computer vision, machine learning, and computational neuroscience. Predictions derived from models are used to guide the design of experimental tests of perceptual and cognitive theories. The unified picture emerging from my work is that the power of human inference depends on two basic principles. First, people exploit generic priors — tacit general assumptions about the way the world works, which guide learning and inference from observed data. Second, people have a capacity to generate and manipulate structured representations— representations organized around distinct roles, such as multiple joints in motion with respect to one another in action perception, or the more abstract roles of cause and effect. My current areas of active study include motion perception, action recognition, object recognition, causal learning, and analogical reasoning.
- Lu, H., Wu, Y, & Holyoak, K.H. (2019). Emergence of analogy from relation learning. Proceedings of the National Academy of Sciences, 116, 4176-4181.
- Baker, N., Lu, H, Erlikhman, G. & Kellman, P. J. (2018). Deep convolutional networks do not classify based on global object shape. PLoS Computational Biology, 14(12).
- Kubricht, J. R., Lu, H., & Holyoak, K. J. (2017). Intuitive physics: current research and controversies. Trends in cognitive sciences. 21(10), 749-759.
- Peng, Y., Thurman, S., & Lu, H. (2017). Causal action: a fundamental constraint on perception and inference with body movements. Psychological Science, 28(6), 789-807.
- van Boxtel, J., Dapretto, M., & Lu, H. (2016). Intact recognition, but attentuated adaptation, for biological motion in youth with autism spectrum disorder. Autism Research, 9(10), 1103-1113.
- Thurman, S.M., van Boxtel, J. J. A, Monti, M. M., Chiang, J. N., & Lu, H. (2016). Neural adaptation in pSTS correlates with perceptual aftereffects to biological motion and with autistic traits. NeuroImage, 136: 146-61.
- Lu, H., Rojas, R. R., Beckers, T., & Yuille, A. L. (2016). A Bayesian theory of sequential causal learning and abstract transfer. Cognitive Science, 40(2), 404-39.
- Lee, A. L. F., & Lu, H. (2014). Global-motion aftereffect does not depend on awareness of the adapting motion direction. Attention, Perception, & Psychophysics, 76(3),766-779.
- Thurman , S. M., & Lu, H. (2013). Physical and biological constraints govern perceived animacy of scrambled human forms. Psychological Science, 24, 1133-1141.
- Lu, H., Chen, D., & Holyoak, H. J. (2012). Bayesian analogy with relational transformations. Psychological Review, 119(3), 617-648.
- van Boxtel, J., & Lu, H. (2012). Signature movements lead to efficient search for threatening actions. PLoS ONE, 7(5): e37085, 1-6.
- Lu, H. (2010). Structural processing in biological motion perception. Journal of Vision, 10(12), 1-13.
- Holyoak, K. J., Lee, H. S., & Lu, H. (2010). Analogical and category-based inference: A theoretical integration with Bayesian causal models. Journal of Experimental Psychology: General, 139(4), 702-727.
- Lu, H., Yuille, A., Liljeholm, M., Cheng, P. W., Holyoak, K. J. (2008). Bayesian generic priors for causal learning. Psychological Review, 115(4), 955-984.
- Lu, H., & Liu, Z. (2006). Computing dynamic classification images from correlation maps. Journal of Vision, 6(4), 475-83.