Although that's structurally approximately correct it understates the influence that SA had, and continues to have (IMHO, see below). Mathematically, Spreading Activation is essentially contextually driven probability integration, sort an unruly version of Bayesian Networks -- iterative matrix multiplies of a weighted connectivity matrix given a set of initial "context" weights. My hypothesis is what embeddings are doing for the GPTs can be understood as essentially spreading activation over distributed representations connected to each token. In classical (GOFAI) SA, per Collins and Loftus, you spread to/from the tokens, whereas in modern SA (by my hypothesis) you are spreading through a high dimensionality network represented by the embedding vectors, instead of via the tokens.