thinking process is considered to be decoupled from immediate motor action since the random walks happen between virtual (fictitious) percept and action clips in the memory (networks of clips) itself which can be modified (updated) both in the number of its clips and/or in the transition probabilities between the clips via reflections or some compositional properties before the real action $a$ takes place. Due to the ability of the creation of new clips in the network of clips,[^1]one may think of every percept clip or every action clip just as a network clip in the system. Therefore one can formulate PS model just on the network’ clips irrespective of the kind of them. Therefore the transitional probabilities between every two clips $c_i$ and $c_j$ in the time step $t$ can be written as a normalized conditional probability function build of the wight transitions ${\omega}^{(t)} (c_i ,c_j )$. $$P^{(t)} ( c_j |c_i ) = \frac{{\omega}^{(t)} (c_i, c_j )}{\sum_k {\omega}^{(t)} (c_i, c_k)},
\label{eqprob}$$ where ${\omega}^{(t)} (c_i ,c_j ) = f (h^{(t)} (c_i ,c_j ) )$ is modified as an adaptation rule of $$h^{(t+1)} (c_i, c_j ) = h^{(t)} (c_i, c_j ) - \gamma ( h^{(t)} (c_i, c_j ) -1 ) + \sum_l 