A2: Modelling Vision H Neumann1, L Schwabe2 |
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1University of Ulm, Germany |
| This tutorial is structured into two parts that will be covered by a morning and an afternoon session. In the morning session we first motivate the role of models in vision science. We show that models can provide links between experimental data from different modalities such as, e.g., psychophysics, neurophysiology, and brain imaging. Models can be used to formulate hypotheses and knowledge about the visual system that can be subsequently tested in experiments and, in turn, also lead to model improvements. To some extent, however, modeling vision is indeed an art as the visual system can be described at various levels of abstraction (e. g. purely descriptive vs. functional models) and different spatial and temporal granularity (e. g. visually responsive neurons vs. brain-wide dynamics, or perceptual tasks vs. learning to see during development). Therefore, throughout the tutorial we address questions such as “How to chose a model for a given question?”, and “How to compare different models?”. Based on this general introduction we will review phenomenological models of early and mid-level vision, addressing vision topics such as perceptual grouping, surface perceptions, motion integration, and optical flow. We discuss a few specific models and show how they can be linked to data from visual psychophysics, and how they may generalize to other visual features. In line with this year’s ECVP focus on “Computational Neuroscience”, we also discuss how such models can be used to constrain hypotheses about the neural code in the visual system, or to make implicit assumptions about these codes explicit. In the afternoon session we first consider neurodynamical models of visual processing and show how cortical network models can affect the interpretation of psychophysical and brain imaging data. We then show how physiological and anatomical findings, as summarized by neurodynamical models, can be used to design experiments and stimuli for visual psychophysics. We then also consider the modeling of vision via modeling learning in the visual system. The rational behind such modeling approaches is that a proper learning algorithm based on first principles will produce models of visual systems when stimulated with natural stimuli. The advantages and pitfalls of such normative modeling will be discussed. Finally, we consider models of higher-level form and motion processing, e.g. biological motion or articulated motion, and compare the performance of such models with human performance and recent advances in visual computing such as markerless motion capture. |
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