Ph.D., Physics, ETH, Zurich, Switzerland
Models of visual perception and decision making; linking theory to psychophysical and physiological data
"Believing is seeing." - My research interest is to understand 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?
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.
Professor Alan Stocker will be considering new graduate students for admission for Fall 2017.
Xue-Xin Wei and Alan A Stocker (2015). A Bayesian observer model constrained by Efficient coding can explain "anti-Bayesian” percepts, Nature Neuroscience, vol. 18(10), p. 1509-1517.
Matjaz Jogan and Alan A Stocker (2015). Signal integration in human visual speed perception Journal of Neuroscience, 35(25), p. 9381-9390.
Pedro Ortega and Dan D Lee and Alan A Stocker (2015). Causal reasoning in a prediction task with hidden causes. Proceedings of the 37th Annual Cognitive Science Society Meeting, p. 1787-1792.
Adam M Gifford and Yale E Cohen and Alan A Stocker (2014) Characterizing the impact of category uncertainty on human auditory categorization behavior, PLoS Computational Biology, 10(7), p. 1-15.
Dan D Lee and Pedro Ortega and Alan A Stocker (2014) Dynamic Belief State Representations, Current Opinion in Neurobiology, 25, p. 221-227.
Matjaz Jogan and Alan A Stocker (2014) A new two-alternative forced choice method for the unbiased characterization of perceptual bias and discriminability, Journal of Vision, 14(3):22, p. 1–18.
Zhuo Wang and Alan A Stocker and Dan D Lee (2013) Fisher-optimal neural population codes for high-dimensional diffeomorphic stimulus representations, NIPS Advances in Neural Information Processing Systems 26, Lake Tahoe CA, MIT Press, p. 297-305.
Xue-Xin Wei and Alan A Stocker (2012) Bayesian inference with efficient neural population codes, ICANN Int. Conference on Artifical Neural Networks, Springer Lecture Notes in Computer Science, Vol. 7552, p. 523-530
Nicole C Rust and Alan A Stocker, Ambiguity and invariance: two fundamental challenges for visual processing, Current Opinion in Neurobiology, 20:3, June 2010, p. 382-388
James H Hedges, Alan A Stocker and Eero P Simoncelli, Optimal inference explains the perceptual coherence of visual motion stimuli, Journal of Vision, 11(6):14, May 2011, p. 1-16
Alan A Stocker and Eero P Simoncelli (2009), Visual motion aftereffects arise from a cascade of two isomorphic adaptation mechanisms, Journal of Vision, vol. 9, no. 9, p. 1-14.