  In this paper, we study neural cognition in social network. A stochastic
model is introduced and shown to incorporate two well-known models in Pavlovian
conditioning and social networks as special case, namely Rescorla-Wagner model
and Friedkin-Johnsen model. The interpretation and comparison of these model
are discussed. We consider two cases when the disturbance is independent
identical distributed for all time and when the distribution of the random
variable evolves according to a markov chain. We show that the systems for both
cases are mean square stable and the expectation of the states converges to
consensus.
