Depends on how the task is defined. Surely you wouldn't expect Naïve Bayes classifier to behave the same way as humans do, because it's simple and you have no illusions regarding what it can do. So you wouldn't be surprised by the fact that you can forge input of of NB classifier so it would give you false-negative, while staying essentially the same for human. Because humans are not Naïve Bayes classifiers, and you know it. Yet given the right task, right set of input parameters and right approach, NB classifier could yield very good results on practical datasets, so, give performance, you would declare it is "doing job well".

So the same thing shouldn't surprise (nor even upset) you about NNs, if you remember that humans are not Neural Nets. Just don't be fooled by the misleading similarity in names for NN concepts and human brain components, because they are not the same and thus don't act the same. It's just a name. So that shouldn't be anything new.

One more tricky moment is where you speak of "human-provided label". Right, but what is meaning of the label? I mean, if label for all cats would be "chair" and after that NN would label cat as "chair" it would do it's job correctly, and labeling cat as "cat" would be a mistake. Why I'm saying such an obvious nonsense? Because labels are somewhat arbitrary, but there's important difference between labeling objects by principle "recognized by [some specific] human as X" and "has origin of X". For example, you can have some photo of a dog (bad lightning, bad focus, bushes, fog, whatever) that wouldn't be recognized as "dog" by 9/10 humans you showed it, but nevertheless it is an image of a dog. Now is it a good result or a bad result, if your NN classified it as "dog"? Depends on how task is defined.

Anyway, what I was pointing out isn't that NNs' false negatives are not really false negatives or something. Yes they are. But every fuzzy classifier (including human) would give false negatives sometimes if labels are given "by origin", it's pretty obvious. What I was pointing out is that similarity between correctly matched object and incorrectly matched object is relative to the observer, and is directly linked to the technology observers works on. And we know that technology of NNs is completely different from technology of human brain. Once again, "cat classifier" inside of human brain doesn't have exact pixel values on input, and your NN does. So no surprise what looks exactly the same to you, looks (and in fact is!) like two different things for your NN.

So, you are right, currently existing ANNs are not as similar to humans as we'd like them to be (for some purposes, that is), but it is no news. Essentially by my previous comment I was trying to say this: if you are not surprised by the fact that NNs often correctly (by the factor of origin) recognize something that is totally unrecognizable to you (and that is common knowledge — take every modern hand-writtend number recognizer) you should not be surprised by the fact that the don't recognize some things that are clear as day to you, because these two effects are essentialy the one. So I'm not denying NNs aren't perfect (in some sense), I'm saying it's no news, for sure not "backpropagation invention sensation".