Proceedings of the Annual Meeting of the Cognitive Science Society

A connectionist architecture is proposed and provides representations for probabilities and utilities, the basic elements of formal decision making theories. The outputs of standard feed forward feature-extraction networks then become inputs to this decision making network. A formalism shows how the gradient of expected utility can be hack propagated through the decision making network "down" to the feature extraction network. The formalism can be adapted to algorithms which optimize total or minimum expected utility. Utilities can be either given or estimated during learning. When utility estimation and decision making behavior adapt simultaneously, learning dynamics show properties contrasted to "puzzhng" observations made in experimental situations with human subjects. The results illustrate the interest in computational properties emerging out of the integration of elements of decision making formalisms and connectionist learning modeb.