Research
ONGOING RESEARCH
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We can only explicitly encode a fraction of what is visible in each glance, and even then, the resultant internal representations are far from faithful to the true state of the external environment. Yet somehow, we get the gist, and perceive the world as stable and complete. How does the brain fool us into this illusion of perceived order amidst continuous sensory chaos?
Our understanding of how the visual brain represents objects has grown exponentially since the monumental discovery of visual neurons tuned to physical stimulus properties. Recent landmark advances in computational power have allowed for the design of systems that can recreate the activity of networks of billions of neurons. Such powerful algorithms can take all the information available in an image in parallel. However, the brain is not a computer. When perceiving our surrounding environment, unlike a computer which can quickly render each pixel of detail, we do not usually see the exact physical properties of single objects in isolation, but instead evaluate their size, color, contrast, orientation, and even semantic meaning with respect to the contest in which they are organized.
EXAMPLES
a) The two central green circles are the same physical size, but the right one looks larger due to the sizes of the surrounding circles, b) the two patches A and B are the same shade of gray, but B appears lighter because it falls in the context of the green cylinder’s shadow, c) the middle green patch is vertically oriented, but we perceive it as tilted slightly clockwise due to the counterclockwise tilt of the surrounding context, and d) the middle green item is perceived as a letter when read as part of the row of letters and as a number when read as part of the column of numbers.
Instead of ignoring, suppressing, or discarding the mass of information that escapes our focused attention, a number of recent findings converge to suggest that the visual system circumvents capacity limitations in part by relying on a default set of heuristics based on regularities in the external environment to guide the formation of initial perceptual chunks that pragmatically constrain further processing. For example, even though we are poor at encoding the specific details of individual items, we are remarkably good at representing the global properties of sets of objects. The efficiency of summarizing average properties of sets of similar objects without encoding redundant details bears many similarities to previous Gestalt proposals that perception is parsimoniously organized as a function of recurrent order in the physical world.
PERCEPTUAL AVERAGING
a) Perceptual averaging: When shown a briefly presented Set of heterogeneously-sized circles followed by a Test circle, observers cannot determine whether the Test circle was a member of the previous Set, but can judge whether the Test circle represents the mean size of the Set. b) Gestalt grouping: Circles grouped by spatial 'Proximity' in rows, by color 'Similarity' in columns, by 'Connectedness' in rows, and by 'Common region' in columns.
Towards furthering our understanding of how the limited capacity visual system allows for our amazing perceptual capabilities, our lab investigates how several such constraints determine the manner in which we can perceive, encode, remember and pay attention to information in the surrounding environment. Check out our Publications page for more information about our current research, and our Participation page for details on how to participate in our current psychophysical, eye tracking and EEG experiments for money or course credit.