In an RNN, you could connect each hidden state at time step t, h(t) to h(t-N), instead of, or in addition to, h(t-1), making it analogous to dilated convolutions, but with hidden-to-hidden connections at the same layer.
So I don't think RNNs are fundamentally more myopic than CNNs (just that there may be practical advantages to using the latter)
Hierarchical RNNs, Clockwork RNNs and Hierarchical Multiscale RNNs and probably others are doing things of this nature.
You could, but it's not equivalent, and no one seems to have been able to use clockwork RNNs or related archs to achieve similar performance, so the differences would seem to make a difference.
So I don't think RNNs are fundamentally more myopic than CNNs (just that there may be practical advantages to using the latter)
Hierarchical RNNs, Clockwork RNNs and Hierarchical Multiscale RNNs and probably others are doing things of this nature.