Ultimately, my view of the world is more nodular than modular—the nodes each tend to have clear centers & unique attributes, but at the margins, they intersect/overlap heavily with their nearest neighbors. As much as I can understand my Si as a distinct part of my experience (which is probably not much), it feels like it’s more concerned with recognizing these nodes, or centerpoints, than in the more futile exercise of trying to draw edges & boundaries onto the gradients between them.

Rather than viewing the world as a bunch of neatly-bounded patches on a quilt, a better analogy might be a pile of down stuffing that you pulled out from inside the quilt. It’s not all uniform—there are different clumps & clusters, suggesting different objects, but at the end there’s no single, definitive way to bound & define these objects.

Right!

This conjunction between fuzzy, multi-meanings (“superposition”) at the level of object detection (Ne), while having that reside within a landscape of “clear, nodular information” (Si), really reflects what I recently wrote about in this thread — curious to hear your thoughts on it?