My uninformed impression was that it had essentially been superceded by deep learning, SGD, etc., so most of the parts relevant to modern AI are already contained in the standard deep learning curriculum. What’s the most important PDP idea that you think is neglected by this curriculum?

Haven't read it, but my default guess based on approximately-zero exposure would be that it's one of those "theories" which basically says "hey let's stick lots of simple units together and run simulations on them", while answering basically-zero questions which I actually care about. (For instance: does it tell me how internal structures or activation-patterns in a mental system will map to specific structures in the external environment? Or how to detect the formation of general-purpose search?) Or, a lower bar: does it make any universal quantitative predictions about neural systems at all? (No, "bigger system do better" does not count as quantitative. If it successfully predicted e.g. the parameters of the scaling curves of model nets 20+ years before those nets were built, then I'd definitely pay attention.) I'd be surprised if there's any real model of cognition there at all, as opposed to somebody just vibing that neural-network-style stuff is vaguely good somehow.

So to answer your question: because I have those expectations, I haven't looked into the topic. If my expectations are wrong, then maybe that's a mistake on my part.

For those who haven’t heard of PDP, what in your opinion was its most impressive advance prediction that was not predicted by other theories?