Recent empirical and modeling research has focused on the semantic fluency
task because it is informative about semantic memory. An interesting interplay
arises between the richness of representations in semantic memory and the
complexity of algorithms required to process it. It has remained an open
question whether representations of words and their relations learned from
language use can enable a simple search algorithm to mimic the observed
behavior in the fluency task. Here we show that it is plausible to learn rich
representations from naturalistic data for which a very simple search algorithm
(a random walk) can replicate the human patterns. We suggest that explicitly
structuring knowledge about words into a semantic network plays a crucial role
in modeling human behavior in memory search and retrieval; moreover, this is
the case across a range of semantic information sources
