  Recent work has attempted to characterize the structure of semantic memory
and the search algorithms which, together, best approximate human patterns of
search revealed in a semantic fluency task. There are a number of models that
seek to capture semantic search processes over networks, but they vary in the
cognitive plausibility of their implementation. Existing work has also
neglected to consider the constraints that the incremental process of language
acquisition must place on the structure of semantic memory. Here we present a
model that incrementally updates a semantic network, with limited computational
steps, and replicates many patterns found in human semantic fluency using a
simple random walk. We also perform thorough analyses showing that a
combination of both structural and semantic features are correlated with human
performance patterns.
