Date published: 12/3/2018

The basic goal of Dr. Lu’s research is to investigate how humans learn and reason, and how intelligent machines might emulate them. Dr. Lu and her lab members approach this basic question as it arises in both perception and higher cognition.

Her research is highly interdisciplinary, integrating theories and methods from psychology, statistics, computer vision, machine learning, and computational neuroscience. Dr. Lu’s lab conducts behavioral and fMRI experiments to test fundamental assumptions on which computational theories are based, and to identify the key computational mechanisms underlying complex cognitive processes. Her group also develops models to guide the design of experimental tests of perceptual and cognitive theories.

The unified picture emerging from Dr. Lu’s 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 are able 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 in reasoning. Dr. Lu’s areas of active study include action recognition and motion perception in both typical populations and people with autism, as well as causal reasoning and analogy.

Dr. Lu has been the recipient of an NSF CAREER award and a Google Faculty Research Award. Her lab has also been funded by ONR and AFOSR, as well as UCLA. Research from Dr. Lu’s lab won the 2014 “best paper” award from the Psychonomic Society for the journal Attention, Perception & Psychophysics, and the 2017 computational modeling prize in perception/action from the Cognitive Science Society.

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