Proceedings of the Annual Meeting of the Cognitive Science Society

An agent-based model for semantic search and retrieval in memory is proposed. The model seeks to generate verbal fluency lists with properties similar to those generated by humans in the semantic fluency task. This model is compared to a random walk in a semantic network in its ability to adjust to the results of 141 undergraduate students in the semantic fluency task in eight different outcomes. We found that the agent-based model fits participants' results better than the random walk model. The results were consistent with optimal foraging theories, and the distributions of the total number of words, similarities, and frequency values were similar to those generated by participants. The potential uses of this model as a virtual environment to experiment with the search and retrieval process in semantic memory are discussed.