The fundamental principle on which the discrete process model (DPM) is based is the assumption that all quantum mechanical processes occur not only in discrete space but also in discrete time. Thus, a process is seen as a transition from one state to another at every step in the (discontinuous) time. These transitions are controlled by the probabilities of moving from one state to another during a step in time. Those probabilities constitute a Markov-matrix, called transition probability matrix.
The process advances in one step in time in the following way: the product of the state vector and the transition probability matrix is calculated, the result being a new state vector (a Markov-vector) for the following point in time. According to this stochastic state vector, "a lot is drawn by nature" to determine which one of the possible states will be actualized to be the next transitional state. The process continues in this way until the system remains in a stable state - and the realization of that state occurs. This stability enables the actualized state to be perceived, in principle. Thus, being observable is defined in DPM as being a stable state of the transitional system.
The model is applied to physical - quantum-mechanical - processes as well as to those of consciousness (mind). In the earlier case, the transition probability matrix remains unchanged during the process, while in the latter it may change in many way.
Two systems interact if some probabilities in their transition matrices are conditional to each other. In this case, both vectors - or their parts - change according to vector-interference.
The vector-interference between the physical and mind systems explains perception as well as psychomotorics. It is shown that, using DPM, there exists no way to reduce conscious phenomena to physical - and vice versa.
Keywords: cognitive, cognition, consciousness, covering, detector, discrete, Eccles, entangle, exocytosis, interference, learning, Markov-matrix, mind, quantum mechanics, perception, psychophysical, synapsis, transition probability.