Psychophysical and cognitive Aspects of Vision

Lecturer: Hongjing Lu

5 topics：

Lecture 1: An introduction to vision science
      Most of us take completely for granted our ability to understand the world through seeing. However, we do not fully understand how humans are able quickly and effortlessly to perceive meaningful, coherent, three-dimensional scenes. I will demonstrate that perception is not a clear window onto reality, but rather an actively constructed, meaningful model of the world. Examples include phenomena related to adaptation, illusions, ambiguous figures, visual completion, and visual categorization. I will then describe four stages of visual perception—the image-based, surface-based, object-based, and category-based stages of perception—and two metaphorical information processing “directions”—bottom-up and top-down.

Lecture 2: Connecting the statistics in images with vision theories
      I will first present computational evidence on the basis of the work by Olshausen and Field (1998, 2004) to demonstrate the connection between the statistics in natural images and neural encoding of image-based information. Second, I will present the texton theory developed by Julesz (1981) and related experimental evidence.

Lecture 3: From natural statistics to generic priors
      This lecture will illustrate how prior knowledge affects human perception. This prior knowledge, which in its most general form is termed generic priors, is consistent with the statistics of natural scenes. Three studies will be presented, illustrating generic priors related to light-from-above (Kersten, Adelson, etc.), non-accidental viewpoints (Freeman), and slow-and-smooth motion (Weiss).

Lecture 4: Bayesian inference in cognitive science
      This lecture will illustrate the use of a Bayesian framework in the context of cognitive research investigating how humans draw strong inferences from noisy and sparse data. I will present computational models that have been developed for two different domains in which inference is critical—motion perception and causal reasoning. The picture emerging from this work is that a key basis for human inference is the use of generic priors—tacit general assumptions people make about the way the world works, which then guide their learning and inference from observed data. I will sketch broader implications, including future directions for applying Bayesian modeling.

Lecture 5: Review of object recognition
      I will review the literature concerning object recognition from three perspectives—psychophysics, physiology, and computation. I will further discuss the strengths and weaknesses of two main theories, template matching and structural description.

Curriculum Vitae

Hongjing Lu 6451 Franz Hall, UCLA 310-267-4683 ext. #2 hongjing@ucla.edu http://hongjing.bol.ucla.edu/
Education 2005-present Statistics Department, University of California, Los Angeles (UCLA) Postdoc Fellow. Mentor: Alan Yuille 2001-2005 Psychology Department, University of California, Los Angeles (UCLA) Major: Cognitive Psychology;  Minor: Measurement Mentor: Zili Liu Ph.D. Psychology (Completed 12/2005) M.A. Psychology (Completed 6/2002) 2000-2001 Psychology Department, Rutgers, The State University of New Jersey, Newark 1993-2000 School of Mechatronic Engineering, Beijing Institute of Technology, M.S. Engineering (Completed 4/2000) B.S. Engineering (Completed 6/1997)
Research Interests Cognitive psychology and cognitive neuroscience studies of human vision Computational vision Object recognition biological motion Perceptual learning Statistical modeling of cognition Causal learning