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This article describes a unifying framework for five highly influential but disparate theories of natural learning and behavioral action selection. These theories are normally considered independently, with their own experimental procedures and results. The framework presented builds on a structure of connection types, propagation rules and learning rules, which are used in combination to integrate results from each theory into a whole. These connection types and rules form the action-selection calculus. The calculus will be used to discuss the areas of genuine difference between the factor theories and to identify areas where there is overlap and where apparently disparate findings have a common source. The discussion is illustrated with exemplar experimental procedures. The article focuses on predictive or anticipatory properties inherent in these action-selection and learning theories, and uses the dynamic expectancy model and its computer implementation SRS/E as a mechanism to conduct this discussion.