A visual reconstruction of the episode’s core thesis: plain local correlation rules can detect co-activity, but real intelligence needs a mechanism for assigning blame and credit across hidden units, layers, and time.
The episode’s argument is a branching systems diagram: simple co-activity is useful, but the hard part is routing information about error or reward back to the parameters that caused it.
XOR is the clean toy problem for multivariate credit assignment. Any single pairwise correlation view misses the rule, because the answer lives in combinations.
Two different hard problems often get blurred together: supervised hidden-unit credit assignment and delayed-reward temporal credit assignment. They overlap in spirit, not in mechanism.
The technical insight ages well: local pairwise updates are too weak for many multivariate tasks. The larger claim, that optimization alone explains consciousness, remains far less settled.
This panel is deliberately asymmetrical: the optimization side gets sharper empirical structure, while the consciousness branch is shown as speculative extension rather than established result.