What to measure next to improve decision making? On top-down task driven feature saliency

Top-down attention is modeled as decision making based on incomplete information. We consider decisions made in a sequential measurement situation where initially only an incomplete input feature vector is available, however, where we are given the possibility to acquire additional input values among the missing features. The procecure thus poses the question what to do next? We take an information theoretical approach implemented for generality in a generative mixture model. The framework allows us reduce the decision about what to measure next in a classification problem to the estimation of a few one-dimensional integrals per missing feature. We demonstrate the viability of the framework on four well-known classification problems.