Follow-up question to Podcast 1 with Jeff (location, orientation, and attention)

I’ve given some thought to how the concept of something lower “rising to the top” of a hierarchy might be implemented. This is what I’ve come up with so far (please feel free to tear this apart if I am way off the mark).

The first thing that becomes clear, is that there isn’t any obvious mechanism for literally pushing something lower in a hierarchy up through each level to the top (at least I couldn’t imagine one that seemed plausible).
Instead, the top of the hierarchy must have direct connections to each of the lower levels. This is of course
a deviation from the normal view of hierarchy, so open to criticism here.

Borrowing some ideas from the Global Workspace paper that @Bitking referenced in his Grids to Maps thread, you start with feed forward input traversing a hierarchy level by level in the traditional sense:

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Next, you add direct connections from each level to the top of the hierarchy. The top node will be receiving anomalies from each level, and sending stimulation:

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The signals will compete at the top node, and the most interesting/anomalous signal will be selected. The originating node will be stimulated. This stimulation will combine with the feed forward signal, and excite the node:

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Each node will have lateral connections to other hierarchical branches across other modalities. When a node is excited, it will send a stronger signal from its lateral connections. In this example, let’s imagine this node in the hierarchy is related to sensory input from your hand, and the anomaly was an unexpected bump on your favorite coffee cup:

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The lateral signal will recruit nodes from other hierarchies and modalities. In this case, lets assume there is a connection with a hierarchy related to sensory input from your eyes. Whatever the eyes were attending to before subconsciously will be overruled, and they will now be recruited to help resolve the anomaly with the coffee cup:

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At this point, the global (conscious) attention has shifted to the coffee cup, and now coordinate spaces across the various sensors involved are all in relation to the cup.

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