  We propose and study a model for the interplay between two different
dynamical processes --one for opinion formation and the other for decision
making-- on two interconnected networks $A$ and $B$. The opinion dynamics on
network $A$ corresponds to that of the M-model, where the state of each agent
can take one of four possible values ($S=-2,-1,1,2$), describing its level of
agreement on a given issue. The likelihood to become an extremist ($S=\pm 2$)
or a moderate ($S=\pm 1$) is controlled by a reinforcement parameter $r \ge 0$.
The decision making dynamics on network $B$ is akin to that of the
Abrams-Strogatz model, where agents can be either in favor ($S=+1$) or against
($S=-1$) the issue. The probability that an agent changes its state is
proportional to the fraction of neighbors that hold the opposite state raised
to a power $\beta$. Starting from a polarized case scenario in which all agents
of network $A$ hold positive orientations while all agents of network $B$ have
a negative orientation, we explore the conditions under which one of the
dynamics prevails over the other, imposing its initial orientation. We find
that, for a given value of $\beta$, the two-network system reaches a consensus
in the positive state (initial state of network $A$) when the reinforcement
overcomes a crossover value $r^*(\beta)$, while a negative consensus happens
for $r<r^*(\beta)$. In the $r-\beta$ phase space, the system displays a
transition at a critical threshold $\beta_c$, from a coexistence of both
orientations for $\beta<\beta_c$ to a dominance of one orientation for
$\beta>\beta_c$. We develop an analytical mean-field approach that gives an
insight into these regimes and shows that both dynamics are equivalent along
the crossover line $(r^*,\beta^*)$.
