Managing Uncertainty in Cue Combination

We develop a hierarchical generative model to study cue combination. The model maps a global shape parameter to local cue-specific parameters, which in turn generate an intensity image. Inferring shape from images is achieved by inverting this model. Inference produces a probability distribution at each level; using distributions rather than a single value of underlying variables at each stage preserves information about the validity of each local cue for the given image. This allows the model, unlike standard combination models, to adaptively weight each cue based on general cue reliability and specific image context. We describe the results of a cue combination psychophysics experiment we conducted that allows a direct comparison with the model. The model provides a good fit to our data and a natural account for some interesting aspects of cue combination.