Simple Search Algorithms on Semantic Networks Learned from Language Use
This addresses the challenge of understanding and simulating human memory retrieval processes in cognitive science, representing an incremental advance by applying existing methods to new data.
The study tackled the problem of modeling human semantic memory search by showing that a random walk algorithm on semantic networks learned from language use can replicate human patterns in the semantic fluency task, with results indicating plausibility across various semantic information sources.
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.