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Alan A Stocker, MSc, PhD
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Graduate Group Affiliations
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- Neuroscience e
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Contact information
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3401 Walnut 313C
Philadelphia, PA 19104
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Philadelphia, PA 19104
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Email:
astocker@sas.upenn.edu
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astocker@sas.upenn.edu
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Education:
21 a M.Sc. 35 (Biomedical Engineering/Material Sciences) c
30 ETH Zurich, Switzerland, 1995.
21 a Ph.D. 14 (Physics) c
30 ETH Zurich, Switzerland, 2002.
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Permanent link21 a M.Sc. 35 (Biomedical Engineering/Material Sciences) c
30 ETH Zurich, Switzerland, 1995.
21 a Ph.D. 14 (Physics) c
30 ETH Zurich, Switzerland, 2002.
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74 Visual perception; perceptual adaptation; decision making; probabilistic models; computational neuroscience.
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1b RESEARCH INTERESTS
a9 The computational strategies the brain applies in solving perceptual estimation and decision tasks, and the means by which it does so using its neural substrate.
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1b RESEARCH TECHNIQUES
4b Psychophysical experiments; behavioral and neural modeling; theory.
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18 RESEARCH SUMMARY
2d0 "Believing is seeing." - My research interest is in understanding how our visual percept of the world is shaped by our beliefs and expectations about what there is to be perceived. More specifically, research in my laboratory is currently exploring (1) how the statistical properties of our visual environment shape our expectations (i.e. objective expectations), and (2) the degree by which our expectations reflect our own previous perceptual decisions (i.e. subjective expectations). How are these expectations formed? What are the computations by which they are combined with sensory information in order to generate our percepts? And what are the underlying neural processes that perform these computations?
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22f We approach these questions with the combined effort of theory and experiment. Theory provides the hypotheses necessary to derive models that then can be validated with carefully targeted psychophysical and (through collaboration) physiological experiments. The theory of evolution motivates us to consider vision as an optimal inference problem. Using the framework of probability theory, our goal is to derive meaningful computational models that can quantitatively account for perceptual behavior of human subjects over a wide range of visual tasks.
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Description of Research Expertise
1b KEY WORDS74 Visual perception; perceptual adaptation; decision making; probabilistic models; computational neuroscience.
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1b RESEARCH INTERESTS
a9 The computational strategies the brain applies in solving perceptual estimation and decision tasks, and the means by which it does so using its neural substrate.
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1b RESEARCH TECHNIQUES
4b Psychophysical experiments; behavioral and neural modeling; theory.
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18 RESEARCH SUMMARY
2d0 "Believing is seeing." - My research interest is in understanding how our visual percept of the world is shaped by our beliefs and expectations about what there is to be perceived. More specifically, research in my laboratory is currently exploring (1) how the statistical properties of our visual environment shape our expectations (i.e. objective expectations), and (2) the degree by which our expectations reflect our own previous perceptual decisions (i.e. subjective expectations). How are these expectations formed? What are the computations by which they are combined with sensory information in order to generate our percepts? And what are the underlying neural processes that perform these computations?
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22f We approach these questions with the combined effort of theory and experiment. Theory provides the hypotheses necessary to derive models that then can be validated with carefully targeted psychophysical and (through collaboration) physiological experiments. The theory of evolution motivates us to consider vision as an optimal inference problem. Using the framework of probability theory, our goal is to derive meaningful computational models that can quantitatively account for perceptual behavior of human subjects over a wide range of visual tasks.
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