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A New Framework for Cognitive/Perceptual Problems

There are a variety of real-world domains where “analysis” of incrementally arriving perceptual input requires the simultaneous solution of a set of hard problems. We claim that basically the same set of interacting problems appears in each of these domains. While people can solve the problems in each of these domains, no automated system is close to solving the full set of interacting problems in any of these domains. The purpose of this paper is to state the set of interacting problems that needs to be solved, and present a cognitively motivated framework in which a solution is possible. Our interest is in a solution to the full set of interacting problems. We are not interested in modeling a particular statistical correlation in a data set that appears under certain conditions. An automated solution would be best, but a man-in-the-loop solution that greatly increases a person’s efficiency an/or accuracy would also be acceptable. The primary domain discussed here is understanding the situation in an image or a sequence of images. The problems and their interactions are shown in Table 1. Solving the situation understanding problem involves much of the brain, so a simple neural network is not a viable approach. What is required is a framework that specifies the process by which a person combines a priori knowledge with the observed evidence. Most of the separate functional capabilities called for in our proposed framework exist already, which give us some confidence that an implementation of the framework can be constructed.