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.
Long Luu and A.A. Stocker. Post-decision biases reveal a self-consistency principle in perceptual inference. eLife, 7:e33334, May 2018.
Xue-Xin Wei and Alan A Stocker (2017). Lawful relation between perceptual bias and discriminability. Proc. National Academy of Sciences, Vol. 114(38).
Josh I Gold and Alan A Stocker (2017). Visual decision-making in an uncertain and dynamic world Annual Review of Vision Science, Vol. 3(1).
Pedro Ortega and Alan A Stocker (2016). Human decision-making under limited time. NIPS Advances in Neural Information Processing Systems 29, p. 100-108.
Zhuo Wang, Xue-Xin Wei, Alan A Stocker, and Daniel D Lee (2016). Efficient neural codes under metabolic constraints. NIPS Advances in Neural Information Processing Systems 29, p. 4619–4627.
Zhuo Wang and Alan A Stocker and Daniel D Lee (2016). Efficient neural codes that minimize Lp reconstruction error. Neural Computation, vol. 28, p. 2656-2686.
Xue-Xin Wei and Alan A Stocker (2016). Mutual information, Fisher information, and Efficient coding. Neural Computation, vol. 28, p. 305-326.
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.