I would classify pavlovian learning as a type of hebbian learning. Where events that occur together positively reinforce each other (different from reinforcement learning).
This idea has been modified into hopfield networks, and then their descendants boltzmann and restricted boltzmann machines. They use an algorithm called contrastive divergence which is effectively hebbian learning. It tries to make events that occur together produce more stable states, and events that do not occur together are made less stable, thus given a partial state, the network will be attracted to the stable state.
So if a bell rings, and food is received the net will learn that this is a (more) stable state and when/if only the bell rings then the net will naturally be attracted to the state, bell + food, as all other states are less stable.
(Unfortunately I do not understand contrastive divergence particularly well, it may be better to look it up yourself).
This work leads into unsupervised learning, deep learning and auto-encoders.
Although, on second thoughts, I should clarify that this doesn't currently work with temporal difference. So I don't know if I really answered the question.