Fuzzy mediation is an innovative approach to the creation of a framework geared towards supervised and collaborative learning in systems with two controllers, one being an expert controller and the second being a novice one. The nature of fuzzy sets allows for the comparison of inputs to reach a consensus on the overall difference between the controls. In previous works we have highlighted the importance of this concept and the aptitude and ease with which this framework adapts itself to agents based on fuzzy learning mechanisms to navigate through a preset course. In this paper we explore the relationship that different mediation equations have when related to different learning environments. We explore five different functions in relation to the tasks being accomplished by an artificial agent navigating through a preset course. We also look in detail at the reaction times between algorithms when different tightness of control is used within the fuzzy mediation core engine. This paper highlights the appropriateness of each of the five mediation equations in relation to the setting in which the fuzzy mediation framework is deployed. Moreover, we guide potential users through the analysis of tightness of control also as it is appropriate to the same environments of deployment of this framework.