Silas, see Naive Bayes classifier for how an “observable characteristics graph” similar to Network 2 should work in theory. It’s not clear whether Hopfield or Hebbian learning can implement this, though.
To put it simply, Network 2 makes the strong assumption that the only influence on features such as color or shape is whether the object is a a rube or a blegg. This is an extremely strong assumption which is often inaccurate; despite this, naive Bayes classifiers work extremely well in practice.
There was once a scorpion who begged a frog to carry him across the river because he could not swim.
The frog hesitated for fearing being stung by the scorpion. The scorpion said: “Don’t worry, you know I won’t sting you since we will both drown if I do that”. So the frog carried the scorpion across the river. But in the middle of the river, the scorpion stung the frog. The frog asked the scorpion in disbelief: “Why did you do this? Now we will both drown!”—“Because you are a game theorist and I am not!”, replied the scorpion.