Our long-term goal is to understand how humans perform natural tasks given realistic visual input. Object perception is critical for the everyday tasks of recognition, planning, and motor actions. Through vision, we infer intrinsic properties of objects, including their shapes, sizes, materials, as well as their identities. We also infer their depths and movement relationships to each other and ourselves, as well as determine how to use this information. The remarkable fact is that the human visual system provides a high level of functionality despite complex and objectively ambiguous retinal input. Current machine vision systems do not come close to normal human visual competence. In contrast, our daily visual judgments are unambiguous, and our actions are reliable. How is this accomplished? Our conceptual approach to this question is motivated by our previous work on object perception as Bayesian statistical inference, and its implications for how human perception gathers and integrates information about scenes and objects to reduce uncertainty, resolve ambiguity and achieve action goals. Our experimental approach to this question grows out of our team's past accomplishments in using behavioral techniques such as interocular suppression, high-field functional magnetic resonance imaging and analysis, and Bayesian observer analysis of human behavioral performance. We combine our conceptual and experimental approaches to address a new set of questions. In three series of experiments, we aim to better understand: 1) the relationship between cortical activity and the perceptual organization of image features into unambiguous object properties and structures (Within-object interactions);2) how visual information about other objects and surfaces reduces uncertainty about the representation of an object's properties and depth relations (Between-object interactions);and 3) whether and how information and uncertainty may be processed differently depending on the viewer-object interactions demanded by task, as predicted by theory (Viewer-object interactions).

Public Health Relevance

Our research uses behavioral, brain imaging, and computational methods to investigate how the human visual system organizes perceptual information into objects and object relationships, and how that information is used for actions. We expect our results to provide knowledge that will help us to understand circuitry patterns within and among visual areas and pathways in the brain and their relationships to visual functions. Deficits in such circuitry are believed to underly a number of clinical problems including amblyopia, object agnosia, and schizophrenia.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Central Visual Processing Study Section (CVP)
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Steinmetz, Michael A
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University of Minnesota Twin Cities
Schools of Arts and Sciences
United States
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Fulvio, Jacqueline M; Maloney, Laurence T; Schrater, Paul R (2015) Revealing individual differences in strategy selection through visual motion extrapolation. Cogn Neurosci 6:169-79
Lin, Zhicheng (2013) Object-centered representations support flexible exogenous visual attention across translation and reflection. Cognition 129:221-31
Qiu, Cheng; Kersten, Daniel; Olman, Cheryl A (2013) Segmentation decreases the magnitude of the tilt illusion. J Vis 13:19
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Lin, Zhicheng; He, Sheng (2012) Emergent filling in induced by motion integration reveals a high-level mechanism in filling in. Psychol Sci 23:1534-41
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Zhang, Peng; Jamison, Keith; Engel, Stephen et al. (2011) Binocular rivalry requires visual attention. Neuron 71:362-9

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