Combining Action Selection Models with a Five Factor Theory

This paper describes a unifying framework for five highly influential but disparate theories (the five factors) of natural learning and behavioral action selection. These theories are normally considered independently, with their own experimental procedures and results. The framework 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. Exemplar experimental procedures will be used to discuss the areas of genuine difference, and to identify areas where there is overlap and where apparently disparate findings have a common source. The paper 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.