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Spreading activation (the core mechanism of 1970s–80s semantic network models like Collins & Quillian, Collins & Loftus, ACT-R) matches introspective experience and reaction-time data. When you retrieve “canary → yellow”, activation spreads from the canary node through the “has-color” link to yellow faster than to “canary → ostrich”. This predicts priming, tip-of-the-tongue states, and many semantic illusions. Path-dependent reasoning Human reasoning is notoriously context- and path-sensitive. Ask someone “a bat and a ball cost $1.10…” after they have just thought about baseball → they are far more likely to say $0.10. That is literally a different traversal path in the same network (sports-equipment bat vs. animal bat) priming the wrong micro-concept. Free association and mind-wandering Thought often looks like a random walk (or slightly biased walk) on the semantic graph. Baars’ global workspace + small-world structure of semantic memory explains why you can get from “butter” to “quantum mechanics” in ~6 steps via surprisingly coherent chains. Large language models are empirical proof Modern transformers are giant implicit semantic graphs whose edges are learned during pre-training. Sampling tokens is literally stochastic traversal: at each step you move to neighboring concepts weighted by contextual attention. The fact that this produces subjectively coherent “thought” is strong evidence that traversal is sufficient (or nearly sufficient) for many aspects of human-like cognition.

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