5. As soon as T has been included in SM, SM generates the relationships between T and other elements, and makes predictions that include T. These predictions can then be compared with external reality. 6. If the predictions of SM related to T prove to be acceptable, then SM is considered useful in understanding T. If the predictions are unacceptable, then SM is inadequate in understanding T. In neither case, SM can be considered correct or incorrect. 7. Any prediction connected to T has to be associated with the SM which produced it. Example: Gravity is a supposition of Newton's theory. In his theory gravity is a property of the mass of a physical body. In Einstein's theory (another symbolic model), gravity is a property of space and mass. Both theories give good predictions in known specific situations. 8. As the predictions of SM related to T are proved acceptable, SM is considered suitable in understanding T and thus, the predictions of SM including T can be associated with the term knowledge. Knowledge based on an acceptable SM is the purpose of any positive science. We'll see now an extremely complex example. We have intentionally chosen a term which practically has no definition in GCL (the definitions is unclear) and has no associated direct data and facts from the external reality. The term chosen is 'alien' (ET). To study within a positive science a term like ET seems impossible; we will see that this is not so. According to the logical schematic presented, we need a symbolic model (a positive science), which in our example is MDT itself. Generation of a definition of the term ET in MDT means that we accept that ETs have a brain and more, their brain works based on the same principle as the human brain. This can be difficult to accept, but independent of the used SM (MDT or another), the situation is the same: SM generates the definition of ET, whatever SM is, and whatever the definition of ET in GCL might be. We'll try to explain ET in MDT. Let's activate MDT with ET included. MDT considers that the basic functions of the brain are the construction of image models [I] and symbolic models [S]. Let's define a human brain [H] with the parameters I=1, S=1. It is very likely that ET will not have the same parameters. Let's suppose a model of ET with the parameters ET(1,10)(the same capacity to build image models as humans, but ten times capacity to build symbolic models). This is just a possible example. In a complete analysis we need to use a collection of values (I,S). After having choosen a pair (I,S), we start operating MDT with ET included. We can ask a first question, e.g. how can the interaction between a human H(1,1) and an ET(1,10) look like? Which are the tendencies of the ET? Do they want to communicate, do they want to be friends or enemies, etc. MDT can't answer these questions yet. We need to calibrate the model. Calibration is done asking questions with known answers.