Zili Liu

Ph.D., Brown University
Associate Professor in Cognitive Psychology, UCLA
Office: Franz Hall 7619 
Phone: (310) 267-4683  / Email: zili@psych.ucla.edu

Research and Teaching Interests

The question that interests me most is: how does our visually perceived world differ from the physical world? Obviously our perceptual representation of the world is not a replica, but reflects our unique evolutionary and ecological needs. We selectively amplify certain details in the world and ignore others and, via practice, increase our sensitivity to those details that are deemed important (perceptual learning). We organize these important perceptual details into categories (e.g., objects) and encode them into memory in specific ways so that we can recognize objects effortlessly (object recognition, including face recognition). These organized categories, in turn, impose on our senses so that we perceive the world in a regular, coherent, and stable manner (perceptual organization).  Indeed, the nature of our perceptual representations is one of the most important questions in psychology, and it is this question that has been my main research interest.

I am interested in nearly all aspects of visual perception.  These include three-dimensional (3D) motion perception, 3D shape perception, perceptual learning, and computational modeling.  Recently, our lab has expanded to study action perception and visual motor behavior.  The overarching question we ask in the lab is how the perceptual brain integrates sensory information to arrive at a coherent, stable description of the world.  Most of the time, this description is consistent with reality.  However, there are also times when it is not, and that is when illusions occur.  These illusions provide a unique window into the inner workings of the brain, as follows.  An illusion occurs when objective sensory information is highly ambiguous.  The brain, rather than being indecisive, uses Bayesian priors to disambiguate the sensory information to arrive at a unique and vivid percept.  In this context, modeling the brain’s Bayesian inference becomes both natural and necessary.

Experimentally, our techniques include psychophysics, fMRI, TMS (transcranial magnetic stimulation), eye tracking, virtual reality, and motion tracking (using a state of the art multi-camera system).